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1. T 5 i i e a e as reee OOJ L PH O 7 L osp J 0 8 f i a T 8 0 6p 2 5 081 y E I all 5 0 4 E 0 4 H 2 o l H 0 0 2 ea i iy OA E j 4 8 10 12 14 8 9 10 11 12 13 14 Wavelength jem Wavelength um Figure 3 1 Atmospheric transmittance in the thermal region Similar to the solar region there are three radiation components thermal path radiance L1 i e photons emitted by the atmospheric layers emitted surface radiance L and reflected radiance La In the thermal spectral region from 8 14 um the radiance signal can be written as L Lpatn TELBB T 7 1 e F 7 3 1 32 CHAPTER 3 BASIC CONCEPTS IN THE THERMAL REGION 33 L c 4DN L Ly A A Figure 3 2 Radiation components in the thermal region Li Lp L 7 Lgp T L3 T 1 F r where Lpatn is the thermal path radiance i e emitted and scattered radiance of different layers of the air volume between ground and sensor 7 is the atmospheric ground to sensor transmittance e is the surface emissivity ranging between 0 and 1 Lgp T is Planck s blackbody radiance of a surface at temperature T and F is the thermal downwelling flux of the atmosphere see Fig 3 2 So the total signal consists of path radiance emitted surface radiance and reflected atmospheric radiation The adjacency radiation i e scattered radiation from the neighborhood of a pixel can be neglected because t
2. 137 CHAPTER 5 DESCRIPTION OF MODULES 138 5 9 Menu Help Finally the Help menu allows browsing of the ATCOR user manual provides a link to the web resources contains the software update center and displays license information X Airborne ATCOR File Sensor Topographic TCOR BRIF Filter Simulation Tools Help Licensed for Daniel Version 7 0 0 c DLR ReSe 2015 Browse Manual Web Resources About Check for Updates Install Components Your License Figure 5 83 The help menu 5 9 1 Help Options The options of the help menu are listed below Browse Manual Opens this manual in the default PDF display application of your machine Web Resources Opens the html document atcor3_webresources htm in the systems default ap plications for viewing HTML documents About Provides basic information about the copyright and also displays the build number of the software please provide the build number for debugging purposes in case the ATCOR support is contacted Check for Updates Connects to the ReSe Web server to check if a new build release of the software is available The software update is downloaded automatically if available and may be installed thereafter Install Components This is your software update center atmospheric database files may be downloaded and updated directly through this update tool upon availability Your License Provides information about the licensed features in your license key
3. bright sand oo oo pe b w p sand bare soil v E E o o E E Y a Y Hr E asphalt man made lt water 0 5 1 0 1 5 2 0 0 5 1 0 1 5 2 0 Wavalength sm Wavalength sm Figure 5 72 Examples of reflectance spectra and associated classes Select Sensor 3 w AHS_FEBOS w AHS_FINOS w AWIRISOL w AVIRISIB w DAISO2 w DAIS99 w HYEUROPEOZ gt HYMAPO4 y HYMAPOS_FINAL w SEBASS w emissivity classes reflectance classes INPUT Reflectance IMAGE Yexport data data 040607 OPAF ReF 040607_0P_1_rad_atn bsq OUTPUT Classification IMAGE Vexport data data71 040807 0PAF Ref 040607_0P_1 rad_atn_cla bsq If applicable Output emissivity file has extension _cla_emi bsq CONTINUE Figure 5 73 SPECL spectral classification of reflectance cube 5 8 3 Spectral Smile Detection This routine uses sharp atmospheric absorption features and Fraunhofer lines for inflight smile i e spectral across track non uniformity detection The calculation is done by correlation analysis of a number of spectral bands in the vicinity of selected absorption features The outputs may be used for smile aware atmospheric correction Initially the smile characterization for each spectrometer channel is derived from laboratory mea surements From such data the wavelength shift with respect to the center pixel of the detector array can be parametrized using a 4th order polynomial fit However in case of instr
4. 56 4 14 BRDF Correction coso vu aneu darias EAE oH SOR N 57 5 Description of Modules 59 Dill Menas Pile ssa soso he ee Pa cas RR eld ee Wa oe E Pad ee 60 Held Display ENYI File sc odo maa ma wp te Phe hh Shed a de heed 60 BLE Bhow Tepig i e a ra we tae a A ee ee a we A 63 5 1 3 Resize Input Image 2 2 45 ceo ee RR EER eR aA i 63 5 1 4 Select Input Image sano ao 5655 Gee REL eH Ee dtaa eS 64 A a i ae ae Og ee Be BS lee A Ree be Bee eke od 64 A e raa He ae a ae Gwe Blk Al a A ode ee ee bee 2 eo 65 ol Plot Sensor Response s ssc aucta a ke ee a 65 CONTENTS de 5 3 5 4 5 0 5 6 5 7 5 18 5 1 9 5 1 10 Menu uz ll la 20 5 2 4 3 2 0 Menu 53 1 5 3 2 Diao 5 3 4 5 3 5 5 3 6 DaT 5 3 8 Menu 5 41 5 4 2 54 3 5 4 4 94 5 5 4 6 SAT 5 4 8 5 4 9 5 4 10 SA 11 Menu 6 0 1 5 0 2 5 5 3 5 54 5 0 0 Menu 5 6 1 5 6 2 36 3 5 6 4 5 6 5 5 6 6 5 6 7 5 6 8 Menu Sfl lez 4 Plot Calibration File occiso 82 ee he Ba ee eee 66 Dhow System Fies a o or we kok Md cs ew ak ae ee ee ee eS 66 Edit Preigrenges 2 5 4 go ek a eel aa ee GE aoi e e a M ae RO 67 DEE s o ee ee hee a ee we Pe ee 69 Define Sensor Parameters a 69 Generate Spectral Filter Functions ee ee 71 Apply Spectral Shift to Sensor 2 2 2 2 2 ee ee eee 73 BBCALC Blackbody Function oaoa 0 00002 eee 73 RESLUT Resample Atm LUTS from Database 74 TOPOPTAp ME e 2505 Bo ee
5. Emissivity selection panel e o Options for haze processidg e Reflectance ratio panel for dark reference pixels 0 Incidence BRDF compensation panel e o Value added panel for a flat terrain ee Value added panel for a rugged terrain e ee LAT FPAR panel ias e ee PASE ARA RRA A A Job stat ION ia e a AAA a we ATCOR Tiled Processiig ua a e ek Pie Bee acs LIST OF FIGURES 9 9 92 5 93 5 54 5 55 5 96 5 57 5 58 5 59 5 60 5 61 5 62 5 63 5 64 5 65 5 66 5 67 5 68 5 69 5 70 5 71 5 72 5 73 5 74 5 75 5 76 dd 5 78 5 79 5 80 5 81 5 82 5 83 Tal 1 2 8 1 8 2 8 3 8 4 8 5 Al 9 2 9 3 9 4 10 1 10 2 BRIDE top Meni ista E kOe RES a ae ee ee oO 104 BREFCOR correction panel airborne version 2 08 105 Nadir HO MSlEAUO lt scos pi ratos ace a E OO eb ee ee A G 106 BRDF model analysis panel lt o 2 6 cee be ee ee eee ee 107 BRDF model fitting analysis panel co s srca cac euo u troadad rao 108 BRIDE model plots 4 4444 saagaa a eie a a ae a be Ae e e ea aS 109 Mosaicking Wool 2244444 eed g aa a e e aa Aa a a eS 111 Filter modules 2 6442462454508 aa 112 Resampling of a reflectance spectrum 000000 0008 eee 112 Low pass filtering of a reflectance spectrum e e e 113 Statistical spectral polishing 2 2 a 114 Radiometric spectral pol
6. ee The Pile Men s eccone a tacia 34 eS ad eh ARS ESE we a ee eS Band selection dialog for ENVI file display o o Display ot ENVI imagery pi dado ia a RARA A 42 LIST OF FIGURES 5 5 5 6 5 7 5 8 5 9 5 10 5 11 5 12 5 13 5 14 5 15 5 16 5 17 5 18 9 19 5 20 5 21 5 22 5 23 5 24 5 25 5 26 Dar 5 28 0 20 5 30 5 31 9 32 5 33 5 34 5 35 5 36 5 37 5 38 5 39 5 40 5 41 5 42 5 43 5 44 5 45 5 46 5 47 5 48 5 49 5 50 5 51 Simple text editor to edit plain text ASCII files ooa a Resize ATCOR input imagery ooo 200002 ee Import AVIRIS imagery from JPL standard format Plotting the explicit sensor response functions osoo o a Plotting a calibration Tle ws bk ee ee ea A WOE a o Displaying a calibration file same file as in Fig 5 9 0 Panel to edit the ATCOR preferences ee a The New Sensor Menu eccone ae ecegare nid ae aiea aA G Sensor definition files the three files on the left have to be provided created by the USE ia e ans eo hee da ee A E ae eS Definition of a new Sensor Spectral Filter Creation sace sa io cnda d krada g e ri eka Application of spectral shift tO Sensor eso rra Black body function calculation panel lt a caesos ee re a PSO eR e Panels of RESLUT for resampling the atmospheric LUTs Topographic Modules lt 4 2 64 Pi see ahaa ee ba E a aTa a Import DEM f
7. Lp L T Lgg T L3 1 e Fr L at sensor radiance L Lp thermal path radiance T ground to sensor atmospheric transmittance E surface emissivity T surface temperature LBB blackbody radiance at temperature T weighted with the channel s filter curve F thermal downwelling flux on the ground The second term on the right hand side of equation 10 34 is emitted surface radiance reaching the sensor the third term is the atmospheric radiance reflected at the surface and attenuated by the surface to sensor path The spectral band index elevation and angular dependence is omitted for brevity The Lgg T term is Planck s blackbody radiance B A T weighted with the spectral channel response function R A FEA TIRA Az Lgg T 10 35 A2 TROJA M1 For a discrete temperature interval T T T2 and increment e g T 200 K To 350 K increment 1 K equation 10 35 is solved numerically Temperature and radiance are then approx imated by an exponential fit function function with channel dependent coefficients a1 a2 ay a T D 10 36 LB ag T ln a Lgg 1 10 37 CHAPTER 10 THEORETICAL BACKGROUND 201 For convenience an offset ag is introduced with default a9 0 The offset term can be used to adjust a temperature bias in a scene Example if scene temperatures are too low by 3K they can be raised by setting ag 3 a2 T ao 0 in a Lpg 1 10 38 Remark
8. Model Options 4 Roujean Geometric Kernel J Maignan Hot Spot Geometry Plot Anisotropy Factor Help Export Graphics Figure 5 57 BRDF model plot The currently displayed graphics can be exported to an EPS file using the Export Graphics button CHAPTER 5 DESCRIPTION OF MODULES 110 5 5 5 Mosaicking Mosaic a number of georectified scenes into one in an efficient way Figure 5 58 shows the parameter settings which can be chosen for this procedure Inputs Input Files first file on top list of files to be mosaicked The files are stacked in the order of appearance i e the first file in the list is on top of the mosaicked output Buttons Add File s adds one or more new files Remove Entry removes the selected file s emphMove moves the file one position up or rotates if already at top Range range in x and y direction to edge of pixels of the mosaicked product should be entered The coordinates refer to the pixel borders edges on either side of the image Button From Files reads the maximum extend of all selected files as of the list Pixel Size Size of output pixels in meters Note the mosaicking uses a bilinear interpolation no aggregation is done Cut Options treatment of image borders in overlap area Edge Overlay The mosaicikng is such that the first file is in the list is strictly on top Center Cropped While mosaicking the routine tries to find the middle of the overlap area between the new image
9. On the IDL command line program names can be written in lower case letters so as an example toarad instead of TOARAD is used synonymously in this context If toarad or toarad2 is submitted as a batch job the following keywords can be specified e toarad input filename pixelsize pixelsize sz solar_zenith atmfile atmfile elev elevation vis visibility adjrange adjrange scalef scalef The input file name must include the path and the keywords in brackets indicate optional parameters A detailed description is given below e toarad2 input filename temfile temfile elev elevation vis visibility Similar to toarad temfile is the atmospheric LUT file name for the thermal region The solar zenith angle is not required in the thermal region In addition the pixel size and adja cency range are missing because the adjacency effect can be neglected The output thermal radiance is in the unit mW m sr ym For ambient surface temperatures the radiance range is from about 5 000 t0 15 000 in this unit and data is always stored as float i e the scale factor is scalef 1 Besides the input image_atm bsq the file mage_atm_emiss bsq is automatically loaded as well and all processing parameters will be taken from the m age inn generated by the ATCOR run If the keyword temfile is not specified the file h99000_wv10 tem is used The default visibility is 23 km CHAPTER 8 SENSOR S
10. PARAMETERS infile input data cube single band ENVI image dist size of smoothing filter outfile name of output file KEYWORDS median use median filter instead of default low pass filter e bil_2_bsq infile outfile e bip_2_bsq infile outfile Conversion of ENVI band interleaved by line BIL or by pixel BIP to ATCOR standard band sequential storage order BSQ PARAMETERS infile input data cube in BIL or BIP format ENVI image outfile name of output file to be created optional default _img bsq CHAPTER 6 BATCH PROCESSING REFERENCE 149 e multires sensor sensor psens psens pspec pspec Resampling of multiple spectra stored as separate ASCII files dat in the path pspec The spectral channel response functions rsp are stored in the path psens and the keyword sensor specifies the sensor name The ASCII files dat contain 2 columns wavelength um or nm spectral value Empty lines in between or at the end are not allowed The spectrum can have any unit radiance reflectance emissivity Example multires sensor xxx psens data7 sensor xxx pspec data7 spectra The resampled spectra are written to a new folder data7 spectra xxx e multires_slb sensor sensor psens psens slbfile slbfile Same as multires but all input spectra are stored in a single ENVI spectral library file slb and the keyword slbfile specifies this file with path
11. RUN Generate FODIS slb file MESSAGES QUIT Figure 4 16 FODIS GUI supporting CaliGeo and NERC formats illumination file range 0 1 containing the fractional shadow If a float coded illumination file exists in the folder of the input scene then it is used for de shadowing also for the flat terrain case without DEM files Therefore it has always the first priority If the matched filter de shadowing is intended then the float coded illumination file has to be renamed or temporarily removed from this folder Internally the range 0 1 of the float illumination is then re scaled to 0 1000 to be compatible with the matched filter de shadowing case only for the purpose of de shadowing not for the correction of terrain effects 4 14 BRDF Correction For the BRDF correction including the BREFCOR approach the following workflow is recom mended 1 Perform the atmospheric compensation using ATCOR 4 The indigence BRDF needs to be corrected in this step by selecting the BRDF option for terrain correction The observer BRDF correction is a step done subsequently to the atmospheric correction 2 Apply spectral polishing routines if required 3 Prepare the inputs for the BREFCOR model calibration i e check the scan angle file specifically the second layer should be contained therein as absolute azimuth 4 Select calibration data set a number of 2 4 image scenes from a whole campaign should be
12. after putting the new file smile_poly_ord4 dat into the sensor definition directory CHAPTER 4 WORKFLOW 50 4 Apply the spectral polishing routine see Section 5 6 3 and 5 6 4 and 5 Run the Spectral Smile Interpolation module see Section 5 6 7 on the atmospherically cor rected image 4 8 Haze cloud water map Although the surface reflectance cube and temperature emissivity for thermal channels is the main result of the atmospheric correction some additional products are often requested One of these products is a map of the haze cloud water and land pixels of a scene This map not only delivers a basic scene classification but it may also contain information about potential processing problems For example if turbid water pixels are not included in the water mask the haze mask may also be not appropriate and consequently results of the haze removal over land might be of poor quality Such a pre classification as part of the atmospheric correction has a long history 22 40 58 59 60 41 48 It is also employed as part of NASA s automatic processing chain for MODIS 1 using the classes land water snow ice cloud shadow thin cirrus sun glint etc Therefore the calculated haze cloud water map is a useful optional output of ATCOR It is enabled by setting the parameter thcw 1 in the preference_parameters dat file see chapter 9 4 If the file name of the imagery is 2mage bsq the corresponding map is named
13. 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 Riano D Chuvieco E Salas J and Aguado I Assessment of different topographic corrections in Landsat TM data for mapping vegetation types IEEE Trans Geoscience and Remote Sensing Vol 41 1056 1061 2003 Richter R Derivation of temperature and emittance from airborne multispectral thermal infrared scanner data Infrared Phys Technol Vol 35 817 826 1994 Richter R A spatially adaptive fast atmospheric correction algorithm Int J Remote Sensing Vol 17 1201 1214 1996 Richter R Atmospheric correction of satellite data with haze removal including a haze clear transition region Computers amp Geosciences Vol 22 675 681 1996 Richter R On the in flight absolute calibration of high spatial resolution spaceborne sensors using small ground targets Int J Remote Sensing Vol 18 2827 2833 1997 Richter R Correction of satellite imagery over mountainous terrain Applied Optics Vol 37 4004 4015 1998 Richter R Bandpass resampling effects on the retrieval of radiance and surface reflectance Applied Optics Vol 39 5001 5005 2000 Richter R and Coll C Bandpass resampling effects for the retrieval of surface emissivity Applied Optics Vol 41 3523 3529 2002 Richter R and Schlapfer D Geo atmospheric pr
14. IEEE Trans Geoscience Remote Sensing Vol 49 1772 1780 2011 References 249 74 79 76 77 78 79 80 81 82 84 85 87 88 Richter R Wang X Bachmann M and Schl pfer D Correction of cirrus effects in Sentinel 2 type of imagery Int J Remote Sensing Vol 32 2931 2941 2011 Rodger A and Lynch M J Determining atmospheric column water vapour in the 0 4 2 5 um spectral region Proceedings of the AVIRIS Workshop 2001 Pasadena CA 2001 Trish R R Barker J L Goward S N and Arvidson T Characterization of the Landsat 7 ETM automated cloud cover assessment ACCA algorithm Photogr Eng Remote Sens Vol 72 1179 1188 2006 Yi C Y Haze reduction from the visible bands of Landsat TM and ETM images over a shallow water reef environment Remote Sens Environm Vol 112 1773 1783 2008 Salisbury J W and D Aria D M Emissivity of terrestrial materials in the 8 14 wm atmo spheric window Remote Sensing of Environment Vol 42 83 106 1992 Sandmeier S T and Deering D W Structure analysis and classification of boreal forests using airborne hyperspectral BRDF data from ASAS Remote Sensing of Environment vol 69 no 3 pp 281295 1999 Santer R et al SPOT Calibration at the La Crau Test Site France Remote Sensing of Environment Vol 41 227 237 1992 Schlapfer D Borel C
15. Now eq 10 79 is again employed for the green band instead of the blue band to calculate the path radiance the best match to a MODTRAN aerosol type and possibly a fine tuning of the path radiance L Radiance t L total blue band reflected radiance blue band 1 1 1 1 1 1 Sa i a XA 1 e Ts 1 N li 1 1 Aa 1 ms 1 lt 1 1 gt blue green red Figure 10 13 Rescaling of the path radiance with the blue and red band After subtraction of the reflected radiance from the total radiance in the blue band the remaining signal is the updated path radiance in the blue band The path radiance of bands in the blue to red region is then rescaled with interpolation Aerosol retrieval for VNIR sensors If no SWIR bands exist but at least a red band around 660 nm and a NIR band around 850 nm a different approach has to be taken see reference 67 for details It starts with the assumption of average clear atmospheric conditions visibility VIS 23 km to calculate the surface reflectance in the red and NIR bands which is appropriate for situations of clear atmospheres VIS 15 40 km The second step derives a mask of dark vegetation pixels using the ratio vegetation index rvi of the red and near infrared surface reflectance rvi Pnir Prea and multiple reflectance thresholds e The mask pixels have to fulfill rvi gt 3 and Pnir gt 0 10 and Pnir lt 0 25 and Preg lt 0 04 Water pixels are au
16. correction 2 b 0 75 A lt 720 nm and b 1 A gt 720 nm strong correction In most of the tested cases the first mode was appropriate A simple criterion vegetation index P850nm P660nm gt 3 is used to distinguish soil sand and vegetation The right part of Figure 10 26 shows the effect of shifting the threshold illumination angle Br For larger values of r the decline of function G starts later with a larger gradient and the lower bound g is met at slightly higher values of P In most cases g 0 2 to 0 25 is adequate in extreme cases of overcorrection g 0 1 should be applied CHAPTER 10 THEORETICAL BACKGROUND 235 0 0 5 Br 45 degr 5 2 top to bottom curves 2 ira exponent b ira exponent b 1 b 1 3 top to bottom curves b 1 2 fr 45 degr b 3 4 Ar 55 degr b 1 0 fr 55 degr 40 50 60 70 80 90 40 50 60 70 80 90 local illumination angle degree local illumination angle degree Figure 10 26 Geometric functions for empirical BRDF correction Left Functions G eq 10 118 for different values of the exponent b Right Functions G of eq 10 118 for b 1 and different start values of Br The lower cut off value is g 0 2 Practical considerations The angle Sr can actually be calculated from the imagery as demonstrated by the following ex ample When processing the scene with ATCOR the map of local solar zenith angles is stored in a separate file ilu If the output file after atmospheric topograph
17. gt ae l wn de shadowing with b gt Figure 10 21 Flow chart of processing steps during de shadowing Here J is the scene average spectrum without the water cloud pixels Selecting p 0 for a shadow target yields a special simplified form of the matched filter where the sh index symbolizes shadow cp e CREE sh pi Cp 10 109 The shadow matched filter vector is then applied to the non water non cloud part of the scene and yields the still un normalized values that are a relative measure of the fractional direct illumination also called unscaled shadow function here O a y Vin 0 a y P 10 110 The matched filter calculates a minimum RMS shadow target abundance for the entire non water non cloud scene Therefore the values of are positive and negative numbers The arbi trary image depending range of has to be rescaled to the physical range from 0 to 1 where 0 indicates no direct illumination full shadow and 1 means full direct illumination The histogram of is used to rescale the image data Fig 10 22 shows a schematic sketch of such a histogram with CHAPTER 10 THEORETICAL BACKGROUND 227 a smaller peak at 2 representing the shadow pixels and the main peak at Pinar representing the majority of the fully illuminated areas The statistical assumption is used that full direct solar illumination is already obtained for pixels with 2 y Bmax Then the values are li
18. i 1m 10 42 min 8 e for thermal band imagery with at least 5 channels the ISAC In Scene Atmospheric Com pensation method is available A detailed description is given by Young et al 99 The method does not require ancillary meteorological data or atmospheric modeling It neglects the downwelling thermal flux and employs the equation L Lp TE Lgg T Lp tT Lesur face 10 43 This approximation is justified for pixels with a high emissivity close to 1 i e blackbody pixels First the highest brightness temperature 730750 for each pixel in each channel is computed based on the at sensor radiance L converted into brightness temperature In the CHAPTER 10 THEORETICAL BACKGROUND 203 current implementation only channels in the spectral region 8 13 um are employed for the maximum brightness temperature search because the spectral regions A lt 8 um and A gt 13 um are strongly affected by atmospheric water vapor absorption Next a reference channel is defined where most pixels with maximum brightness temperature occur Only those blackbody pixels are retained which have the maximum brightness temperature in this reference channel most hits method For these selected blackbody pixels the scatterplot of measured at sensor radiance L versus blackbody radiance corresponding to Lgr Tis0r is computed for each channel This means the surface radiance of eq 10 43 is approximated as Lsurface Lap Tee The final step is a
19. sensor Tbb grey at surface Thb 8 9 10 11 12 13 8 3 10 11 12 13 Wavelength pm Wavelength jm 1 00 0 99 Y 2 o a Emissivity o o J amp 8 10 11 12 13 Wavelength gm Figure 4 15 Comparison of radiance and temperature at sensor and at surface level CHAPTER 4 WORKFLOW 54 4 10 External water vapor map Sometimes it is convenient to use an external water vapor map even if this map could be de rived from the scene If the scene is named scene bsq then the external map should be named scene_wv bsq and it must have the same number of image lines and pixels per line as the scene If this file is available in the same directory as the scene it will be automatically used during the processing and the usage is also documented in the log file scene_atm log Note this feature is only supported for instruments that are able to retrieve the water vapor column with the intrinsic channels because the prerequisite is the availability of the corresponding LUTs 4 11 Filter for HySpex HySpex is a suite of hyperspectral cameras developed and manufactured by Norsk Elektro Optikk Norway www neo no There is a navigation file for each acquired airborne hyperspectral cube in the following format an ASCII file with 8 columns separated by blanks consisting of e line longitude deg latitude deg ALT meters roll deg pitch deg heading deg UTCx ALT is the flight altitude above sea l
20. Example multires_slb sensor xxx slbfile data7 spectra field1 slb The resampled spectra are written to file data7 spectra field1_xxx slb e spect_rz input input rc xc yc yc box box spnam spnam Extraction of a reflectance spectrum from a Level 1 image DN image file name input The spectrum is taken from the center coordinates xc yc and averaged over a square box of box box pixels The name of the spectrum is specified with the keyword spnam The corresponding inn file must exist and all parameters for the calculation of the surface re flectance are taken from this file If box is an even number it is replaced with the next higher odd number to uniquely define the box center If box is not specified then box 1 is taken NOTE The above spect_rz is a symbolic placeholder There are four implementations flat and rugged terrain satellite and airborne ATCOR spect_a2 satellite version flat terrain spect_a3 satellite version rugged terrain spect_4f airborne version flat terrain spect_4r airborne version rugged terrain So an example of invoking this feature is spect_4r input datal mission2 scene15 bsq xc 500 yc 600 box 3 spname target15_n1 Two ASCII output files will be created in the folder datal mission2 target15_n1 dat the surface reflectance spectrum 2 columns wavelength reflectance target15_nl rdn 4 columns wavelength at se
21. L p u pi p3 and repeating the cycle A minimum of two channels one reference one measurement channel is required The advanced APDA method can take into account multiple absorption channels in the 910 960 nm and 1110 1150 nm regions Two water vapor retrieval algorithms are available in ATCOR compare chapter 9 5 parameter iwv_model 1 2 1 The water vapor maps with the smallest standard deviation in the 940 nm and 1130 nm region are selected Finally if both regions are available the average of these two water vapor maps is taken parameter iwv_model 1 in the inn file The scan angle dependence of the path radiance is taken into account 2 A linear regression ratio LIRR is applied to multiple bands parameter iwv_model 2 This water vapor map might be more accurate because the regression reduces sensor noise and may partially compensate calibration problems in lucky cases Although the water vapor map might be less noisy the retrieved surface reflectance spectrum will always retain any channel calibration problems The scan angle dependence of the path radiance is not accounted for Remarks CHAPTER 10 THEORETICAL BACKGROUND 219 1 The APDA algorithm is relatively fast Its disadvantage is that it is not stable numerically for very low reflectance targets water shadow regions The transmittance slope ratio method 75 might work better in these cases so it is an interesting alternative water vapor algorithm Howev
22. SAVI Remote Sensing of Environment Vol 25 295 309 1988 Hueni A Schlapfer D Jehle M and Schaepman M Impacts of dichroic prism coatings on radiometry of the airborne imaging spectrometer APEX Applied Optics Vol 53 5344 5352 2014 Idso S B and Jackson R D Thermal radiation from the atmosphere J Geophysical Research Vol 74 5397 5403 1969 Isaacs R G Wang W C Worsham R D and Goldberg S Multiple scattering LOW TRAN and FASCODE models Applied Optics Vol 26 1272 1281 1987 Jimenez Munoz J C and Sobrino J A Atmospheric water vapour content retrieval from visible and thermal data in the framework of the DAISEX campaign Int J Remote Sensing Vol 26 3163 3180 2005 Kahle A B et al Middle infrared multispectral aircraft scanner data analysis for geological applications Applied Optics Vol 19 2279 2290 1980 Kaufman Y J and Sendra C Algorithm for automatic atmospheric corrections to visible and near IR satellite imagery Int J Remote Sensing Vol 9 1357 1381 1988 Kaufman Y J et al The MODIS 2 1 um channel correlation with visible reflectance for use in remote sensing of aerosol EEE Transactions on Geoscience and Remote Sensing Vol 35 1286 1298 1997 References 247 42 43 44 45 46 47 ou E 52 53 56 Kleespies T J and McMillin L M Re
23. Vierrain The next steps include the adjacency correction eq 10 9 10 10 and the spherical albedo effect eq 10 14 If O On s dn denote solar zenith angle terrain slope solar azimuth and topographic azimuth respectively the illumination angle 8 can be obtained from the DEM slope and aspect angles and the solar geometry cosB x y cosO cosO x yY sinOgsinO 1 y cos s bn x y 10 17 CHAPTER 10 THEORETICAL BACKGROUND 194 Geometry of solar illumination cos fi cos 8 cos 6 sin sin cos 4 yA sur Y A Direct irradiance Circumsolar diffuse imadiance lsotropic diffuse lt fux Direct and diffuse radiation components Figure 10 6 Solar illumination geometry and radiation components The illumination image cosp x y is calculated within ATCOR and stored as a separate map The diffuse solar flux on an inclined plane is calculated with Hay s model Hay and McKay 1985 also see Richter 1998 for the enhancement with the binary factor b Ejq y z Ealz bTs z cos x y cosOs 1 bTs z Vsky x y 10 18 The sky view factor can be computed from local information as Vs y y cos On x y 2 based on the local DEM slope angle O ATCOR uses the horizon algorithm that provides a more accurate value of the sky view factor by considering the terrain neighborhood of each pixel Dozier et al 1981 Vary and Vierrain are related by Vory _ y 1
24. and uses values of path radiance atmospheric transmittance and global flux for the current solar and viewing geometry stored in precalculated LUTs L Lp t Prey E 7 modelling a atm LUT sun geometry measurement gt 5 190 Visibility km Figure 10 11 Schematic sketch of visibility determination with reference pixel Automatic masking of reference areas 1 6 or 2 2 wm band required or at least red NIR bands If the sensor has a SWIR band at 1 6 or 2 2 wm then the scene can be searched for dark pixels in this band and a correlation of the SWIR reflectance with the reflectance in the red and blue band can be employed to estimate the visibility automatically Kaufman et al 1997 For this purpose we use a modified version of the original idea for the following algorithm If a SWIR band exists the SWIR reflectance is calculated assuming a visibility of 23 km instead of the original version of top of atmosphere reflectance Then water pixels are excluded by employing only those pixels with SWIR reflectance values above 1 and an NDVI gt 0 1 For the 2 2 um band the upper threshold of the reflectance of the dark pixels is selected as 5 If the number of reference pixels is less then 1 of the image pixels then the upper threshold is increased to 10 or finally 12 If a 1 6 um band exists but no 2 2 wm band the corresponding upper thresholds are selected as 10 and 15 or finally 18 respectively The ref
25. b4 b3 gt 3 0 and b2 b3 gt 0 8 or b3 lt 0 15 and b4 gt 0 45 dark vegetation b4 b3 gt 3 0 and b2 b3 gt 0 8 or b3 lt 0 15 and b3 lt 0 08 and b4 lt 0 28 yellow vegetation b4 b3 gt 2 0 and 62 gt b3 and b3 gt 0 08 and b4 b5 gt 1 5 mix veg soil 2 0 lt b4 b3 lt 3 0 and 0 05 lt b3 lt 0 15 and b4 gt 0 15 asphalt dark sand b4 b3 lt 1 6 and 0 05 lt b3 lt 0 20 and 0 05 lt b4 lt 0 20 and 0 05 lt b5 lt 0 25 and b5 b4 gt 0 7 sand bare soil cloud b4 b3 lt 2 0 and b4 gt 0 15 and b5 gt 0 15 bright sand bare soil cloud b4 b3 lt 2 0 and b4 gt 0 15 and b4 gt 0 25 or b5 gt 0 30 dry vegetation soil 1 7 lt b4 b3 lt 2 0 and b4 gt 0 25 or 1 4 lt b4 b3 lt 2 0 and b7 b5 lt 0 83 sparse vegetation soil 1 4 lt b4 b3 lt 1 7 and b4 gt 0 25 or 1 4 lt b4 b3 lt 2 0 and b7 b5 lt 0 83 and b5 b4 gt 1 2 turbid water b4 lt 0 11 and b5 lt 0 05 clear water b4 lt 0 02 and b5 lt 0 02 clear water over sand b3 gt 0 02 and 63 gt b4 0 005 and b5 lt 0 02 Figures 5 73 5 74 show the panel of the SPECL program and a sample output When the button emissivity classes and emissivity values is clicked the emissivity classes of table 10 1 are assigned the emissivity values as defined in file emissivity dat in the atcor4 directory So this file can be edited to modify the emissivity values for the 15 classes CHAPTER 5 DESCRIPTION OF MODULES 124 gt
26. nty 2 In this case the image is split into 3 2 6 tiles each tile is processed separately finally all tiles are merged into one output file and the sub scenes are deleted The maximum size of each tile depends on the available memory for a specific machine because ATCOR performs most calcula tions in memory loading one or two complete bands of the scene A typical tile size for modern machines is 3000 3000 pixels 9 Mpixels to 5000 5000 pixels 25 Mpixels The user has to try CHAPTER 6 BATCH PROCESSING REFERENCE 141 and find out the appropiate tile size As an example with a 9 Mpixel tile size and a 30 Mpixel scene the image has to be split into 4 sub scenes Assuming that the number of image columns and lines is approximately the same one would choose the keywords ntx 2 nty 2 in this example Of course processing of much smaller tiles is also possible e g ntx 20 nty 10 but this is not recommended because of potential image border effects i e larger visibility differences for the small tiles might lead to seams at the tile borders An optional keyword output can be used to define the output directory and name of the output reflectance file If the keyword specifies only the output path which is recommended then all output files are written to the specified output directory and the reflectance output file name is the name of the input file with _atm bsq appended The optional keyword vis can be used to overwrite
27. scene up to 9 targets can be extracted and used for the spectral calibration The geometry scene visibility and average water vapor content of the targets enter as parameters to the spectral calibration see Fig 5 76 The water vapor content has to be averaged from the values found in the location_target txt files The first target DN file has to be selected by the user the remaining target files are automatically found provided the nomenclature with a consecutive numbering is applied The result of the spectral calibration are files with the spectral shifts per spectrometer and the new center wavelengths of all channels The spectral bandwidth of channels is not modified The DN spectra will be employed in an optimization procedure that minimizes the spikes of the derived surface reflectance spectra in the atmospheric absorption regions The first target DN file has to be entered at the top left button of the GUI panel Figure 5 76 The other target files are automatically searched and employed if the nomenclature of chapter 2 2 is employed Further input data are the sensor definition the range of bands per spectrometer solar geometry and atmospheric parameters Output is a file with the spectral channel center wavelength shifts per spectrometer and a new wavelength file containing the updated wavelengths for each channel The results of the spectral shift are summarized in a file spectral_calibration_results tat where the wavel
28. 010 lt p cirrus lt 0 015 e medium thickness color coded as darker yellow with 0 015 lt p cirrus lt 0 025 e high thickness color coded as bright yellow with p cirrus gt 0 025 reflectance units In addition to the 1 38 wm cirrus channel another channel index w1 around 1 24 um or as a substitute a NIR channel from the 800 to 900 nm region is employed with a ratio criterion to define cirrus pixels p cirrus p w1 gt T cir 10 106 Reference 25 proposes a threshold of T cir 0 3 to distinguish tropospheric aerosols due to dust storms from cirrus clouds However in the absence of dust storms this threshold is too high and predicted no cirrus in a number of test scenes containing a lot of cirrus clouds Therefore we use much lower values of T cir ranging from 0 01 for water vapor columns W gt 1 cm to T cir 0 15 for W lt 0 5 cm So with these thresholds tropospheric aerosols might be misclassified as cirrus in situations with dust storms but this is a necessary trade off In any case those cloud areas are excluded from the map of pixels employed for the aerosol retrieval which is the main purpose The cirrus and boundary layer haze removal options are exclusive i e only one of them can be selected per run 10 5 6 De shadowing with matched filter Remotely sensed optical imagery of the Earth s surface is often contaminated with cloud and cloud shadow areas Surface information under cloud cove
29. 10 86 Rappa p u Reflectance 0 90 0 95 1 00 Wavelength um Figure 10 15 Reference and measurement channels for the water vapor method The at sensor radiance is converted into an at sensor reflectance The problem is the estimation of the surface reflectance pa in the absorption band eq 10 85 The technique tries to estimate the reflectance p2 with a linear interpolation of the surface reflectance values in the window channels ch 1 3 that are not or only slightly influenced by the water vapor content Therefore the reflectance pa is calculated as p2 wipi W3P3 10 87 Then equation 10 85 can be written as _ paraluJEglu Te u Egan wu Rappa G0 aa 0 au 0 E au 20 ORS CHAPTER 10 THEORETICAL BACKGROUND 218 where Ey2 u is the global flux on the ground for the measurement channel index 2 ATCOR employs 4 to 5 water vapor columns u 0 4 1 0 2 0 2 9 4 0 cm sea level to space geometry to calculate an exponential fit function Rappa u exp a vu 10 89 which can be solved for the water vapor column u see Fig 10 16 where the diamonds in the figure mark the calculated water vapor grid points u 0 4 1 0 2 0 2 9 cm a InRApDA 3 y 10 90 u APDA Ratio water vapor column em Figure 10 16 APDA ratio with an exponential fit function for the water vapor Equations 10 85 10 87 to 10 90 are iterated starting with u 1 0 cm calculating Rappa up dating u
30. 12 dry veg soil 0 975 13 sparse veg soil 0 975 14 snow 0 980 15 cloud 0 980 16 turbid water 0 984 Table 10 1 Example of emissivity values for a 11 um channel thermal band such as Daedalus 1268 10 reflective 1 thermal band The drawback is that it cannot be employed for night time data or sensors with only thermal bands In this case a fixed emissivity has to be specified for one channel e g e 0 97 In the ATCOR model the following radiance unit mW cm sr um is employed equivalent to yW cm7 sr nmt Split window covariance variance ratio SWCVR The method derives the water vapor map from thermal band imagery 42 46 38 The water vapor content W can be retrieved as a function of the ratio Rj of transmittances Ti Tj in two thermal bands i and j W a b Ry 10 46 with N a o EI T T Ri X ES 10 47 i Ti gt Tix N where N is the number of pixels in a moving window box centered at pixel k T is the average brightness temperature in this box and e is the land surface emissivity Equation 10 47 is the ratio of covariance to variance accounting for the abbreviation SWCVR The two selected channels should be in the 10 5 12 5 um region where the emissivity of most land surfaces changes only slightly yielding an emissivity ratio j e close to 1 yielding Rj 7 7 Then the parameters a and b in eq 10 46 can be calculated from a regression of channel transmittances versus
31. 2 5 gt p cirrus lt 4 0 10 60 The same definition is used for cirrus over water if the land water distinction is still possible based on the selected spectral criteria Still higher apparent reflectance values are defined as cirrus cloud if 4 0 gt p cirrus lt 5 0 10 61 and thick cirrus cloud if p cirrus gt 5 0 10 62 and no distinction concerning land water is made for the last two classes Cirrus detection is is switched off in the following cases e no water vapor map available if DEM height 2000 m e water vapor map W available if W 1 cm or W Twv where Twv is the water vapor threshold specified in the file preference_parameters dat Haze over land see chapter 10 5 3 The the mean of the tasseled cap transformation TC is calculated Clear pixels are those with TC lt mean TC and p blue lt Te cloud over land threshold and p NIR gt Twater NIR water reflectance threshold defined in preference_parameters dat Next the mean and standard deviation o of the HOT transformation are calculated Pixels are assigned to the compact haze mask if HOT gt mean HOT and to the large haze mask if HOT gt mean HOT 0 5 HOT Then the HOT histogram of all haze pixels is calculated Pixels with values less than 40 of the cumulative histogram are assigned to thin medium haze pixels with higher values to medium thick haze This distinction is arbitrary and has no effect on th
32. 5 8 9 Create Scan Angles e 134 5 8 10 MTF PSF and eitective GIFOV 6 a dem a wn 136 PSA PODS Process o eoe Sadoe dicea bs Gee ee e ee o Be 136 BO Menu Help lt lt cone ead ee Sa a eh ae we ee aE A ae wa ata 138 p91 Telp Options seais ao oo ae a a ed a Oe Be Seed eR eee 138 6 Batch Processing Reference 139 Gl Stiatme ATCOR trom console cosa eae a ee 139 6 2 Using the bate mode trom within IDL oa ensa d aons ee wik Be 140 6 3 Batch modules keyword driven modules o 02000002 eee 141 7 Value Added Products 151 Ti DAL FPAR Albedo 2 4 4 a gaca OAS Oe Bae ra a Be es 151 7 2 Surface energy balance a a 153 8 Sensor simulation of hyper multispectral imagery 159 9 Implementation Reference and Sensor Specifics 166 9 1 Monochromatic atmospheric database o o 166 9 1 1 Database update with solar irradiance a 168 9 2 Sensor specific atmospheric database ee 169 9 2 1 Resample sensor specific atmospheric LUTs with another solar irradiance 170 oo VO IG Tyee AA IN 171 Ga Mal Input s e sai eoad daria a ae eee SO 171 03 2 Bide INPUTS lt saci core beg kV AED eae ee ede a a 171 fio Win GUtOUG ou pies Seek BR ae Se ay bee oy 173 02A DISQUE asus a eas oe Gl OSE E sb eee ee BS is 173 9 4 Preference parameters for ATCOR ee 174 95 Job control parameters of the ina Me cocina ra a Se a 177 OO Probleme and Hints usos GA
33. 7 Input File Number Spectral Band Number BCI min BCI max F_geo Fm_geo F_vol Fm_vol F_iso Fm_iso Goodfit 1 0 400 NaN 0 100 NaN 0 000 NaN 0 000 1 962 2 0 400 0 250 0 115 0 117 0 025 0 005 0 480 0 130 0 078 El 0 250 0 650 0 095 0 095 0 015 0 015 0 520 0 520 0 043 4 0 550 1 200 0 069 0 069 0 005 0 005 0 480 0 480 0 058 5 1 200 1 500 0 027 0 027 0 005 0 005 0 080 0 080 0 049 KA volumetric Geometric 10 Kernel Factors lsotropic Raflactance 5 1 0 0 5 0 0 0 5 10 BCI Help Export Plot Export Table Plot Fit Done Figure 5 55 BRDF model analysis panel Actions Export Plot Exports the current plot as EPS image file Export Table Exports the current parameter table to an ASCII file Plot Fit Opens a new panel to show the current fitting results see Fig 5 56 The displayed data in the fitting plot may be adjusted in various ways the input file which had been used for model fitting may be changed the two displayed spectral bands may be changed and the range of BRDF cover index BCI levels may be selected 5 5 4 BRDF Model Plot This tool is to plot a BRDF model as it has been used for BREFCOR correction An example is shown in Fig 5 57 CHAPTER 5 DESCRIPTION OF MODULES eoo A Plotting BRDF Fit al Current Model cubes CASI chile brefcor brefcor_3bd_model saw ETT Input File Number cubes CASI chi le run401 CASI_2013_01_
34. C Keller J and Itten K I Atmospheric precorrected differential absorption technique to retrieve columnar water vapor Remote Sensing of Environment Vol 65 353 366 1998 Schlapfer D and Richter R Geo atmospheric processing of airborne imaging spectrome try data Part 1 parametric orthorectification Int J Remote Sensing Vol 23 2609 2630 2002 Schlapfer D PARGE User Guide Version 3 2 ReSe Applications Schlapfer Wil Switzer land 2012 available at http www rese ch Schlapfer D and R Richter Evaluation of Brefcor BRDF Effects Correction for Hyspex CASI and APEX Imaging Spectroscopy Data presented at IEEE Whispers Lausanne pp 4 2014 Schlapfer D Richter R and Feingersh T Operational BRDF Effects Correction for Wide Field of View Optical Scanners BREFCOR IEEE Trans Geoscience and Remote Sensing vol 53 no 4 pp 18551864 2014 Schowengerdt R A Remote Sensing Models and Methods for Image Processing 3rd Edition Elsevier Academic Press 2007 Shao Y Taff G N and Walsh S J Shadow detection and building height esti mation using IKONOS data International Journal of Remote Sensing 32 22 69296944 doi 10 1080 01431161 2010 517226 2011 Sirguey P Simple correction of multiple reflection effects in rugged terrain Int J Remote Sensing Vol 30 1075 1081 2009 89 Slater P N Remote Sensing Opt
35. The computer implementation of the channel resampled radiance equations is coded to minimize spectral resampling effects 63 64 Temperature emissivity separation For a sensor with n thermal channels there are n equations of 10 34 with n 1 unknowns namely the n surface emissivities plus a surface temperature So the system of equations 10 34 is always underdetermined Several possibilities exist to address this problem Gillespie et al 1986 1998 27 28 Five options are offered by the airborne version of ATCOR e a constant emissivity default e 0 98 independent of surface cover type 10 12 um region for sensors with a single thermal channel For sensors with multiple thermal bands the parameter itemp_band described in chapter 4 6 defines the channel employed for the surface temperature calculation e fixed emissivity values assigned for 3 classes for the selected surface temperature band param eter itemp_band e soil 0 96 e vegetation 0 97 else e 0 98 water and undefined class The assignment to the vegetation soil class is performed on the fly in memory employing the vegetation index red and NIR bands required and the 3 class emissivity map is also available file mage_atm_emi3 bsq compare chapter 4 5 e emissivity map based on a pre classification performed with coregistered reflective bands 58 to each class the user can assign an e value as shown in Table 10 1 The classification algorithm itsel
36. The solar and DEM geometry is shown in figure 10 6 as well as the three solar radiation components taken into account for rugged terrain direct and circumsolar irradiance and diffuse hemispherical sky flux It can be shown that these three components are equivalent to the direct and diffuse solar flux components in flat terrain In case of a shadow pixel the direct and circumsolar components CHAPTER 10 THEORETICAL BACKGROUND 193 y Sky and terrain view factors trigonometric approach horizon line approach Adjeceacy Netghbourkood 1 24 Figure 10 5 Radiation components in rugged terrain sky view factor Left schematic sketch of radiation components in rugged terrain 1 path radiance 2 pixel reflected radiance 3 adjacency radiance 4 reflected terrain radiance Right sky and terrain view factor are set to zero i e the binary factor b 0 The next step iterates eq 10 15 averaging the reflected terrain radiation over a square box of 1 0 x 1 0 km If equation 10 15 is used with E E then three iterations are usually sufficient to be independent of the start value of the terrain reflectance 62 However for highly reflective surfaces e g snow and high terrain view factors more than three iterations are necessary and a faster convergence of T can be achieved with a geometric series for the terrain reflected radiation E as proposed in 88 gt i 1 El E P Vterrain 10 16 i pe
37. The wavelength change or shift is calculated as Amop Amop h Xo 2 19 Asen Asen h Asen hias 2 20 Figure 2 4 top shows the calculated wavelength shifts for MODTRAN required for 3 flight alti tudes 1 4 2 100 km The 4 2 km corresponds to a pressure level of 600 hPa mbar Note If the sensor is contained in a pressurized chamber at p 600 hPa this pressure level has to be used for the calculation of the sensor wavelength shift independent of the actual flight altitude This means the MODTRAN wavelength shift also has to be adapted to this pressure level i e using the corresponding virtual flight altitude of 4 2 km Figure 2 4 middle left and right show the lab wavelength shifts for the 2 cases of piap 1013 hPa and Pray 940 hPa to study the influence of lab measurements at sea level 1013 hPa and at a higher elevation 598 m above sea level The new MODTRAN wavelengths have a negative shift indicating they are smaller than the orig inal Ap shifted left to shorter wavelengths whereas the lab wavelengths have a positive shift i e they are shifted to the right to longer wavelengths Therefore the combined shift MODTRAN and lab is not the sum but the difference of these shifts CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 24 The combined total shift is shown in the bottom two plots the left one represents the case with Plab 1013 hPa the right one with pjgy 940 hPa Both cases are very similar wit
38. Therefore the obtained visibility value usually can be considered as a lower bound The higher visibility value of AEROSOL TYPE and VISIB ESTIMATE is recommended as a start visibility for the SPECTRA module The Inflight Calibration routine is described in chapter 2 4 and chapter 5 4 8 The WATER VAPOR button can be used to test the appropriate band combinations for the retrieval of a water vapor map without a calculation of the surface reflectance cube Fig 4 7 shows the panel with the image processing options Some options may not be accessible they are blocked if the required spectral bands are missing In case of a rugged terrain the ATCOR4r button has to be selected Fig 4 1 ATCOR This panel is similar to Fig 4 6 but an additional panel for the specification of the DEM files will appear Fig 4 8 The user has to provide the DEM file matched to the size of the input image The slope and aspect files can be calculated from the corresponding module under Topographic Fig 4 1 These two files may need a special treatment as discussed in chapter 5 5 2 Therefore they are not automatically created from the elevation file The skyview file and cast shadow file are optional only required in extremely steep terrain The skyview calculation can also be found under the Topographic label of Fig 4 1 Depending on the selected image processing option some additional panels may pop up Most
39. Vol 65 367 375 1998 Berk A Anderson G P Acharya P K and Shettle E P MODTRAN5 2 0 0 User s Man ual Spectral Sciences Inc Burlington MA Air Force Research Laboratory Hanscom MA 2008 Brutsaert W On a derivable formula for long wave radiation from clear skies Water Re sources Research Vol 11 742 744 1975 Buettner K J K and Kern C D The determination of infrared emissivities of terrestrial surfaces Journal of Geophysical Research Vol 70 1329 1337 1965 Carlson T N Capehart W J and Gillies R R A new look at the simplified method for remote sensing of daily evapotranspiration Remote Sensing of Environment Vol 54 161 167 1995 Choudhury B J Synergism of multispectral satellite observation for estimating regional land surface evaporation Remote Sensing of Environment Vol 49 264 274 1994 244 References 245 13 14 a IN 16 17 18 19 20 21 22 24 25 Choudhury B J Ahmed N U Idso S B Reginato R J and Daughtry C S T Rela tions between evaporation coefficients and vegetation indices studied by model simulations Remote Sensing of Environment Vol 50 1 17 1994 Coll C Caselles V Rubio E Sospreda F and Valor E Temperature and emissivity separation from calibrated data of the Digital Airborne Imaging Spectrometer Remote Sens Environm Vo
40. air temperature gradient as well as water vapor have to be defined If the value added option is selected another panel pops up Figure 5 49 It contains parameters for the leaf area index LAI model and FPAR model as described in chapter 7 Finally a job status window indicates the processing progress Note The job status window of ATCOR shows the percentage of processed image data and the estimated remaining time The time estimate is based on the processing time for the current band The time per band increases for channels in atmospheric water vapor regions it decreases in regions where interpolation is applied e g around 1400 nm However the time also depends on other factors such as the overall CPU load in case of multi user machines or the traffic on net worked machines Accordingly the estimate for the remaining time is not always continuously decreasing but may increase sometimes CHAPTER 5 DESCRIPTION OF MODULES 101 Figure 5 47 Value added panel for a flat terrain Figure 5 48 Value added panel for a rugged terrain CHAPTER 5 DESCRIPTION OF MODULES 102 Figure 5 49 LAI FPAR panel Figure 5 50 Job status window CHAPTER 5 DESCRIPTION OF MODULES 103 5 4 11 Start ATCOR Process Tiled from inn This is a way to start a tiled process of ATCOR from within the ATCOR GUI instead of the standard batch based process atcor_tile The process requires that an x inn file has been created
41. analytical models using a large set of spaceborne measurements with emphasis on the Hot Spot Remote Sens Environ Vol 90 No 2 210220 2004 Makarau A Richter R Miller R and Reinartz P Haze detection and removal in re motely sensed multispectral imagery IEEE TGRS Vol 52 5895 5905 2014 Mouroulis P Green R O and Chrien T G Design of pushbroom imaging spectrometers for optimum recovery of spectroscopic and spatial information Applied Optics Vol 39 2210 2220 2000 Moran M S Clarke T R Inoue Y and Vidal A Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index Remote Sensing of Environment Vol 49 246 263 1994 Murray F W On the computation of saturation vapor pressure J Applied Meteorology Vol 6 203 204 1967 Stamnes K Tsay S C Wiscombe W J and Jayaweera K Numerically stable algorithm for discrete ordinate method radiative transfer in multiple scattering and emitting layered media Applied Optics Vol 27 2502 2509 1988 Nicodemus F E Reflectance nomenclature and directional reflectance and emissivity Ap plied Optics Vol 9 1474 1475 1970 Parlow E Net radiation of urban areas Proc 17th EARSeL Symposium on Future Trends in Remote Sensing Lyngby Denmark 17 19 June 1997 pp 221 226 Balkema Rotterdam 1998 References 248 57
42. and all the images mosaicked so far as a cut line number of bands to process Button Select Bands lets you select the bands to mosaic the number of currently selected bands is displayed beneath the button Output File Name name of output file to be written Actions Preview the first of the selected bands is mosaicked at a resolution reduced by a factor of 2 and the result is displayed Run Process The mosaicking is performed Restrictions This routines requires georeferenced data with same coordinate system no rotation in ENVI header background coded with 0 all files should have the same number of bands for mosaicing The input resolutions of the imagery may vary CHAPTER 5 DESCRIPTION OF MODULES 111 Figure 5 58 Mosaicking Tool CHAPTER 5 DESCRIPTION OF MODULES 112 5 6 Menu Filter The Filter menu provides spectral filtering of single spectra reflectance emissivity radiance provided as ASCII files spectral filtering of image cubes and spectral polishing Xx Airborne ATCOR File Sensor Topographic ATCOR PRIF Filter Simulation Tools Help Licensed for Daniel Resample a Spectrum Low pass filter a spectrum Spectral Polishing Statistical Filters Spectral Polishing Radiometric Variation Flat Field Polishing Pushbroom Polishing Destriping Spectral Smile Interpolation Image Cube Cast Shadow Border Removal Figure 5 59 Filter modules 5 6 1 Resample a Spec
43. angles close to the solar zenith angle The threshold angle can be specified by the user and the following empirical rules are recommended e Br b 20 if O lt 45 e If 45 lt 0 lt 55 then Br 0 15 e If 0 gt 55 then Br 0 10 These rules are automatically applied if Br 0 e g during batch processing The geometric function G needs a lower bound g to prevent a too strong reduction of reflectance values Values of G greater than 1 are set to 1 and values less than the boundary g are reset to g This means the processing works in the geometric regime from Sr to 90 and the updated reflectance is Pg PLG 10 119 where pz is the isotropic Lambert value Figure 10 26 shows a graphical presentation of equation 10 118 The left part displays the function G for different values of the exponent b For b 1 the decrease with P is strong with a constant gradient For smaller values of b the decrease with 8 is moderate initially but the gradient increases with larger i Currently different functions G for soil sand and vegetation can be selected in ATCOR compare the graphical user interface of Figure 5 46 The function G for soil sand is applied with a wavelength independent exponent b After testing a large number of vegetated mountainous scenes two vegetation modes were finally selected because of their good performance 1 b 0 75 for channels with A lt 720 nm and b 0 33 for A gt 720 nm weak
44. are excluded in this case e g the value added calculation of surface energy balance components the automatic spectral classification SPECL BRDF corrections or top of atmosphere radiance TOARAD Summary of output data types e byte default surface reflectance scale factor 4 0 e 16 bit signed integer scale factor gt 10 0 typically 10 or 100 e float scale factor 1 0 9 3 4 Side outputs A number of side outputs is written by default after successful execution whereas some outputs are optional marked with an Log file Name outputname log Standard log file containing all necessary information about the data processing Format ASCII Error log file Name atcor_error log An error log file is written whenever an uncaught error occurred Please send this file to the software supplier in case of malfunction Format ASCII Aerosol optical thickness Name outputname _atm_aot bsq Aerosol optical thickness map scale facor 1 000 Format 1 channel binary 16 bit signed integer CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 174 DDV classification Name outputname _atm_ddv bsq Classification of dark dense vegetation cast shadows and water pixels map used for aerosol retrieval Format 1 band ENVI byte file containing the three classes Visibility index Name outputnamej_atm_visindex bsq Index of Visibility used for atmospheric correction if variable aerosol distribution has been selected as
45. balance 7 2 Surface energy balance Surface energy balance is an essential part of climatology The energy balance equation applicable to most land surfaces can be written as Asrar 1989 Rn G H LE 7 7 where Rn is the net radiant energy absorbed by the surface The net energy is dissipated by conduction into the ground G convection to the atmosphere H and available as latent heat of evaporation LE The amount of energy employed in photosynthesis in case of vegetated surfaces is usually small compared to the other terms Therefore it is neglected here The terms on the right hand side of equation 7 7 are called heat fluxes The soil or ground heat flux G typically ranges from 10 to 50 of net radiation Convection to the atmosphere is called sensible heat flux H It may warm or cool the surface depending on whether the air is warmer or cooler than the surface The energy available to evaporate water from the surface LE is usually obtained as the residual to balance the net radiation with the dissipation terms Net radiation is expressed as the sum of three radiation components Rn Reolar Ratm A Reur face 7 8 where Rsolar is the absorbed shortwave solar radiation 0 3 3 um or 0 3 2 5 wm Ratm is the longwave radiation 3 14 wm emitted from the atmosphere toward the surface and Reur face is the longwave radiation emitted from the surface into the atmosphere Downwelling radiation is CHAPTER 7 VALUE A
46. be removed by this method as long as the image statistics allow for a good model calibration However shaded areas and forests are still affected by visible differences due to the variable spatial structure if seen from varying angles 10 7 Summary of atmospheric correction steps Although the case of a flat terrain could be treated as a special case of a rugged terrain with the same elevation everywhere this is not an efficient solution because the rugged terrain algorithm runs 3 to 4 times slower than the flat terrain code Therefore the coding is done in separate modules as discussed below 10 7 1 Algorithm for flat terrain The complete sequence of processing for sensors with water vapor bands and a short wave IR band 1 6 or 2 2 um region consists of the following steps e masking of haze cloud water and clear pixels e haze removal e de shadowing e masking of reference pixels e calculation of visibility or optical thickness for reference pixels The optical thickness for the remaining pixels can be defined as the average of the reference pixels or a spatial triangular interpolation is employed to fill the gaps Finally a moving low pass window with a box size of 1 5km x 1 5km or the minimum of ncols 2 and nlines 2 ncols image columns nlines lines is applied to smooth sensor noise and small scale variations of the spectral correlation coefficient for the DDV reference pixels The resulting visibility index and AOT maps are
47. before by going through the atcor GUI or by editing a respective ASCII file manually or by using the routine write_atcor_inn_file pro provided in the directory docu of the ATCOR installation The below parameters are to be entered for processing Input file name name of image data cube to be processed The file must be accompanied by a valid x inn file for processing Name of output cube to be created Number of tiles in X and Y dimensions the total number of tiles to process is then X x Y tiles ATCOR method selection between flat processing and rugged processing for the latter the DEM has to be prepared and ready After starting the process using the run button the messages are printed to the prompt or the console if available Error and status messages may also be found in the log file during and after processing 000 X ATCOR Tiled Processing Select Input File Name src_idl atcor atcor_23 demo_data tm_rugged tm_blforest bsd DeFine Name of Output Cube Varc_idl atcor atcor_23 deno_data tn_rusged tn_blforest_atn bsq Number of Tiles X Dimensiont A Y Dimension le ATCOR Method w ATCOR 2 flat ATCOR 3 rugged Help Run Done Figure 5 51 ATCOR Tiled Processing CHAPTER 5 DESCRIPTION OF MODULES 104 5 5 Menu BRDF The BRDF menu provides access to the simple nadir normalization method and the more ad vanced BREFCOR BRDF correction X Airborne ATCOR File Sensor T
48. by up to 30 specifically for vegetation and man made surfaces ATCOR offers three different methods of correcting BRDF effects The first method is mainly intended for flat terrain and normalizes the off nadir reflectance values to the corresponding nadir values The second method is exclusively dedicated to rugged terrain imagery and corrects for BRDF effects due to the variability of the solar incidence angle The reflectance values of areas with low local solar elevation angles i e large local solar zenith angles are often overcorrected by the assumption of isotropically reflecting surfaces The method reduces these high overcorrected values depending on the illumination and or viewing angles The third method corrects the observation BRDF effects by fitting a physical BRDF model to a number of images and surface cover classes in order to obtain a generic BRDF correction function This function is used to calculate a per pixel anisotropy factor which corrects for the deviation from an averaged spectral albedo In some cases of rugged terrain imagery it is useful to apply both incidence and observation angle correction methods of BRDF correction CHAPTER 10 THEORETICAL BACKGROUND 231 Figure 10 24 De shadowing of a HyMap scene Same image as Figure 10 23 Left original scene right after de shadowing 10 6 1 Nadir normalization method A simple algorithm was implemented as part of the ATCOR package to normalize the scan angl
49. calculated Note as the flux files have to use a float encoding 32bits pixel the file size is twice or four times the size of the input scene for a 16bit pixel and 8bit pixel input scene respectively Notice concerning visibility iterations ATCOR will automatically iterate the initial visibility parameter visib set in the inn file if the number of negative reflectance pixels is larger than 1 of the scene for the red band around 650 nm vegetation is checked here or the NIR band around 850 nm water is checked here The specified visibility is always kept if the visibility is set to a negative value i e visib 20 means the program performs the calculation with visib 20 km and does not iterate even if a large number of negative reflectance pixels occurs If the parameter npref is set to 1 the program computes the visibility map based on dark reference pixels in the scene and npref 1 overwrites the initial value of the visib parameter With npref 1 the program still iterates the average visibility of the visibility map by checking for water pixels in the NIR band unless the specified visib is negative A constant scene visibility is employed for npref 0 In case of scene tiling and npref 0 or npref 1 the iterated visibility obtained for sub scene 1 is also applied to all other sub scenes to avoid brightness steps for the merged sub scenes caused by potentially different visibilities Attention If scene tiling has to be performed and
50. case is something like exelis id184 bin Arguments input Input file to be processed or input reference file respectively R F E Flag for processing option R rugged terrain processing atcor4r_batch F flat terrain processing atcor4f_batch E elevation data preprocessing at_prepele output Name of output file to be created 139 CHAPTER 6 BATCH PROCESSING REFERENCE 140 logfile name of log file to be used or created elefile input elevation file for at_prepele factor factor for elevation data processing NOTE files names other than the input file may be set to dum in order to process default file names After execution the idl session quits and throws an error status of 1 if an error occurred during processing ATTENTION These routines are overwriting existing outputs 6 2 Using the batch mode from within IDL ATCOR can process scenes in the batch mode For large scenes the tiling option is also available which splits a big scene into a number of smaller sub scenes processes the sub scenes and finally merges them into one file A prerequite for the tiling is that enough IDL memory is available to keep one image channel and the sub scene channel in memory The batch mode can be accessed after the processing parameters have been specified in the inter active graphical user interface GUI panel i e after the SPECTRA module has been accessed or after one of the image processing options has been sele
51. covariance matrix and matched filter part of the algorithm The minimum requirement is a band in the near infrared region 0 8 1 0 wm The performance usually increases significantly if two additional bands at 1 6 um and at 2 2 wm are available i e a Landsat TM type of multispectral sensor Even for hyperspectral imagery these three bands around 0 85 1 6 2 2 um are sufficient for the matched filter calculation The usage of a hundred bands would not be helpful but only cause numerical problems during the inversion of the covariance matrix eq 10 109 Spectral channels from the visible region are merely employed for the masking of cloud regions not for the matched filter part because water vegetation dark soils and shadowed pixels all range within a few percent reflectance In addition the visible region is not very sensitive to partial shadow effects because of its larger fraction of diffuse radiation component as compared to wavelengths longer than 0 8 um The distinction of water bodies from cloud shadow areas may be difficult or impossible if it is based merely on spectral reflectance shape and amplitude information Water bodies should be excluded as far as possible to improve the performance of the de shadowing algorithm Currently water and cloud pixels are masked with the spectral criteria p 0 85um lt 5 and p 1 6um lt 1 water 10 114 p 0 48um gt 30 and p 1 6um gt 30 cloud 10 115 If no channel in t
52. dat in the corresponding sensor folder see chapter 4 7 Due to the smile shift the wavelength values of a spectral channel vary slightly in across track direction The smile interpolation function allows the specification of a common center wavelength for each channel Then for each channel all pixel reflectances are interpolated to this new reference wavelength Since the smile shift between adjacent bands does not vary significantly a linear interpolation can be applied If A i denotes the center wavelength of band i and column j and p 1 the surface reflectance of a column j pixel then the new interpolated CHAPTER 5 DESCRIPTION OF MODULES 117 reflectance is A O ee pe I py i Areg pj i TE GED a I j 5 5 where A ef i is the user defined reference center wavelength for band i There are three options for the reference wavelength grid 1 use wavelength corresponding to the center of the detector array 2 use average wavelength over all detector columns per band 3 use nominal wavelength specified in the ENVI header of the reflectance cube This tool is available in the interactive mode main menu then Filter then Spectral Smile Interpolation Image Cube and in the batch mode smile_interp3_batch see chapter 6 3 OS LX Spectral Smile Interpolation Satellite ATCOR m The Spectral Cube is Interpolated to a New Reference Wavelength Grid INPUT IMAGE _atn bog data hyper i
53. express permission of the owner the United States of America as represented by the United States Air Force Contents 1 Introduction 12 2 Basic Concepts in the Solar Region 16 21 Radiation components e s sos aacr ad Pe SEP Re eee a 18 22 Spectral Callado s easa a a a we ee a ee A 21 2 3 Wavelength and refractive index 5 5 4448 eG eR ER asas a 22 2 4 Inflight radiometric calibration 4524454 2 bP Ree ee Gs 24 23 DEPARA a cs ee atk eae es ee ds Se ee aeae aa 26 20 TDP gorroclhion og aos kn a a ee ae Rh G 26 3 Basic Concepts in the Thermal Region 32 3 1 Thermal spectral calibration gt s 2 4 4 8 604442 BYR SR SRR ee ee a 34 4 Workflow 36 dl Men s Overview saos euses e d aiue d eee eee ee o ROH 36 42 Bars stepe with ACOs ocu ae ie aa E a ls he a 39 4 3 Survey of processing steps gt e ca sa as ee 41 414 Directory structure of ATCOR 4 osa nieue ack or ka o aa a eee 43 4 5 Convention for file names o cacra wwe fe bee REESE ER ee aeaa 43 4 6 Definition of a new Sensor o oa 6s eee ee esas 45 4 7 Spectral smile sengorg o s a sa racea ee Ae RG 48 4 8 Haze cloud Water map 50 4 9 Processing of multiband thermal data eee eee sao 52 4 10 External water Vapor Map lt a e 6 ee lt 4 ses a a 54 4 11 Filter for ESPE e ss tpu a tas Bee ee a ep ee ew a 54 4 12 Airborne FODIS instrument ee ee 54 4 13 External float illumination file and de shadowing
54. flux G Wm7 Sensible heat flux H Wm Latent heat LE Wm7 Net radiation Rn Wm Chapter 8 Sensor simulation of hyper multispectral imagery After atmospheric correction the surface reflectance and temperature emissivity cubes can be used to simulate new products which might be of interest e at sensor radiance cubes in the solar region 0 4 2 5 um for different solar geometries and atmospheric conditions e at sensor radiance cubes in the thermal region 8 14 um for different atmospheric conditions e g for satellite sensor studies e resampling of the surface reflectance cube to an existing or new multispectral sensor e resampling of the surface emissivity cube to an existing or new multispectral sensor This is a convenient way to simulate realistic data for a spaceborne version of an airborne in strument to obtain radiance data at different flight levels or to compare hyperspectral hs data with broad band multispectral ms data As a restriction the TOA top of atmosphere or at sensor radiance calculated with the TOARAD program assumes a nadir view The HS2MS hyperspectral to multispectral program requires the hs and ms center wavelengths and the ms channel filter curves for resampling In addition noise of the ms sensor can be included as Gaussian noise with a specified amplitude either as noise equivalent radiance NER or as noise equivalent reflectance NEAp The hs contribution
55. for thermal bands only the elevation dependence of the atmospheric parameters is taken into account CHAPTER 3 BASIC CONCEPTS IN THE THERMAL REGION 34 3 1 Thermal spectral calibration The spectral calibration in the thermal region using atmospheric absorption features can be con ducted in a similar way as for the solar region A spectral mis calibration will cause spikes and dips in the surface emissivity spectrum An appropriate shift of the center wavelengths of the chan nels will remove these artifacts This is performed by an optimization procedure that minimizes the deviation between the surface emissivity spectrum and the corresponding smoothed spectrum However in the thermal region one also has to account for the unknown surface temperature Therefore the merit function also has to be evaluated for a range of surface temperatures Tk and the calculated emissivity depends on the assumed temperature L t Lp Y F i 7 et Th Loli Tk T t F i a 3 3 Here the index i indicates the channel L is the measured at sensor radiance L the path radiance Lp the blackbody radiance and F the downwelling thermal flux multiplied with the ground to sensor transmittance 7 i The merit function to be minimized as a function of the wavelength shift 6 is IM 2AN i Tk 8 Eli Th 6 Min 3 4 The moving average of the emissivity is performed over 5 channels In the present version only channels i
56. format again if the scene file name is scene bsq then the roll pitch yaw files must be named scene_COroll txt scene_COpitch txt scene_COhead txt and the FODIS mea surement file has to be scene_FODIS txt These files have to be in the same folder as the scene The angles in the roll pitch yaw files are in degrees times a scale factor 1 000 The COroll COpitch COhead txt ASCII files contain one value per scan line representing the corre sponding angle times 1 000 stored as integer The ASCII file scene_FODIS txt contains the measured flux spectra with n columns n number of scan lines of scene and m lines m number of bands All flux values are in the unit mWem nm stored as integer in the FORTRAN I8 format Note The capability of water vapor retrieval is switched off in case of FODIS processing as it relies on the sun ground sensor radiation components and FODIS allows only the calculation of the sensor ground sensor radiation components So the water vapor of the selected atm LUT is taken However there is the possibility of providing an external water vapor map see chapter 4 10 which can be calculated for the same scene in the non FODIS mode e g by moving the file scene_fodis slb to another folder or renaming it temporarily in the same folder 4 13 External float illumination file and de shadowing If the scene is processed with a DEM the additional files of slope aspec
57. hPa and T is the air temperature K Figure 7 1 shows py as a function of air temperature for relative humidities of 20 100 The partial pressure is computed as Pww RH es 100 7 14 where RH is the relative humidity in per cent and e is the water vapor partial pressure in saturated air Murray 1967 a Ta 273 16 es Ta eso cap o 0 The constants are a 17 26939 b 35 86 and eso es 273 16K 6 1078 hPa An alternative to equation 7 13 is the following approximation Idso and Jackson 1969 which does not explicitly include the water vapor and holds for average humidity conditions compare Figure 7 2 7 15 a 1 0 261 exp 7 77 x 1074 273 T 7 16 un o o 30 Water Yapor Partial Pressure hPa D 5 19 15 20 25 30 35 Air Ternperature C Figure 7 1 Water vapor partial pressure as a function of air temperature and humidity Relative humidi ties are 20 to 100 with a 10 increment bottom to top curves respectively eq 7 14 The calculation of the heat fluxes G H and LE on the right hand side of equation 7 7 requires different models for vegetated and man made surfaces For vegetated or partially vegetated surfaces we employ a simple parametrization with the SAVI and scaled NDVI indices Choudury 1994 Carlson et al 1995 G 0 4 Rn SAV Im SAVI SAVIm 7 17 where SAV Im 0 814 represents full vegetation cover The sensible heat flux is computed as
58. hae ee oS oH ERS BAe EE eos OS 196 10 9 Effect of cast shadow correction middle and shadow border removal right for bulding shadows ok aooi ale RG Ae e Be Se eee ee Se Sa we ee 197 10 10Radiation components in the thermal region 2 0202000 200 10 11Schematic sketch of visibility determination with reference pixel 213 10 12Correlation of reflectance in different spectral regions o 214 10 13Rescaling of the path radiance with the blue and red band 215 10 140Optical thickness as a function of visibility and visibility index 216 10 15Reference and measurement channels for the water vapor method 217 10 16APDA ratio with an exponential fit function for the water vapor 218 10 17 Haze removal method o wee eG 221 10 18Subset of Ikonos image of Dresden 18 August 2002 222 10 19Scatterplot of apparent reflectance of cirrus 1 38 um band versus red band 224 10 20Sketch of a cloud shadow geometry 2 0 ee ee 225 10 21Flow chart of processing steps during de shadowing 0004 226 10 22Normalized histogram of unscaled shadow function 0004 227 10 23Cloud shadow maps of a HyMap scene 00 eee ee ee es 228 10 24De shadowing of a HyMap scene 2 0 231 10 25Nadir normalization of an image with hot spot geometry 233 10 26Geometric functions for empirical BRDF co
59. has been defined between these two quantities for clear sky conditions as shown in Fig 2 1 left for a path from sea level to space The optical thickness can be defined separately for the different atmospheric constituents molecules aerosols so there is an optical thickness due 16 CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 17 to molecular Rayleigh and aerosol scattering and due to molecular absorption e g water water ozone etc The total optical thickness is the sum of the thicknesses of all individual contributors molecular scattering 6 aerosol 6 molecular absorption 2 3 The MODTRAN visibility parameter scales the aerosol content in the boundary layer 0 2 km altitude For visibilities greater than 100 km the total optical thickness asymptotically approaches a value of about 0 17 which at 550 nm is the sum of the molecular thickness 6 0 0973 plus ozone thickness 6 0 03 plus a very small amount due to trace gases plus the contribution of residual aerosols in the higher atmosphere 2 100 km with 6 0 04 The minimum optical thickness or maximum visibility is reached if the air does not contain aerosol particles so called Rayleigh limit which corresponds to a visibility of 336 km at sea level and no aerosols in the boundary layer and higher atmosphere In this case the total optical thickness molecular and ozone is about 6 0 13 Since the optical thickness due to molecular scatterin
60. image from the input ENVI file Use the function Display ENVI file see section 5 1 1 to export JPG PNG or TIFF imagery in RGB or grayscale for illustration purposes 5 1 7 Plot Sensor Response In the panel Plot Sensor Response one may select the respective sensor response from within the available response functions in the ATCOR installation or elsewhere When selecting a response file the related bands are loaded automatically and the total number of bands is displayed The band range for display can be adjusted manually afterwards Normalization of the curves can be such that the area below the curves is constant same weight of the functions or the maximum is at 1 for all curves The displayed graph may be adjusted in appearance and size and finally being exported to a standard graphics file for further use CHAPTER 5 DESCRIPTION OF MODULES 66 000 XI Sensor Response Viewer Choose any rsp file to diplay the related series of response curves select Sensors Response Ysro idl atcor atcor_23 sensor aster14_hs asterOL rsp Channels Bands from gt 0 a tons E Normalization of Response 2 to Area Default wv to Maximum Xx ATCOR Sensor Response Plot File Font_Size Display Output Response from asterd1 rsp yee 0 5 1 0 15 2 0 Wavelength Figure 5 8 Plotting the explicit sensor response functions 5 1 8 Plot Calibration File When selecting this function the dialog defaults to the atcor
61. image_out_hcw bsq It is a 1 channel false color coded ENVI file In principle if a certain mask of image_out_hcw bsq say haze pixels contains artifacts it may be edited and if the edited file is named image_hcw bsq it will automatically be used for the ATCOR processing This means ATCOR can repeat the processing with an improved edited haze mask The file mage_hcw bsq can also be provided by an external ATCOR independent source In any case if this files exists ATCOR will skip its internal calculations of these masks and use the pre calculated map ATCOR Figure 4 13 Optional haze cloud water output file Edit File The haze cloud water file contains the following classes see Table 4 4 e land e water e boundary layer haze two classes thin to medium haze and medium to thick haze CHAPTER 4 WORKFLOW 51 label definition 0 geocoded background 1 shadow 2 thin cirrus water 3 medium cirrus water 4 thick cirrus water 5 land 6 saturated 7 snow 8 thin cirrus land 9 medium cirrus land 10 thick cirrus land 11 thin medium haze land 12 medium thick haze land 13 thin medium haze water 14 medium thick haze water 15 cloud land 16 cloud water 17 water 18 cirrus cloud 19 cirrus cloud thick Table 4 4 Class label definition of hcw file cirrus three classes for thin medium thick and cirrus cloud thick cirrus cloud
62. in the image folder ATCOR flat terrain will automatically use this file during atmospheric correction and this will be documented in the corresponding scene_atm log file Mandatory name conventions apply for the FODIS processing CHAPTER 4 WORKFLOW 56 e Specim CaliGeo format if the scene file name is scene bsq then the navigation data must be named scene_nav txt and the FODIS data coming in the binary ENVI BIP format scene_fodis0 bip These files have to be in the same folder as the scene The angles in the nav file use the degree unit The ASCII file nav txt contains 9 columns separated with blanks scan line number time x y coordinates flight altitude heading roll pitch and aircraft speed Only data in columns 6 to 8 heading roll pitch is used Heading is in the 180 180 degrees interval and will be converted into the 0 360 interval The FODIS flux measurements in _fodis0 bip are float data and the ENVI header specifies the parameters samples 1 lines n where n is the number of image lines bands m where m is the number of bands and data type 4 i e float All other ENVI header information is not used The standard spectral flux unit is mMmWem 2nm requiring a conversion factor of 0 001 for the unit mMmWem um which is used by ATCOR However the FODIS GUI see Fig 4 16 provides a flexible widget for re scaling if necessary e NERC
63. in the sensor specific folder Columns 1 3 are band number center wavelength bandwidth micron or nm Wavelength File Select Type of Filter Function Red Gauss w 1 Butterworth order 1 slow drop off w 2 Butterworth order 2 close to Gauss a w 3 Butterworth order 3 between Gauss rectangular wv 4 Butterworth order 4 close to rectangular v5 Gauss w E Rectangular vw 7 Triangular w 8 Shape changes from near rectangular first bands to triangular last bands due to binning Generate Filter Files rsp 1 i 0 50 0 52 quit Wavelength 2m Figure 4 12 Supported analytical channel filter types Now you may start processing imagery of the just defined sensor If the sensor has thermal spec tral bands the program RESLUT will automatically calculate the coefficients of the temperature radiance relationship They will be stored in a sensor bbfit file in the appropriate sensor subdi rectory RESLUT will also create the resampled spectrum of the extraterrestrial solar irradiance e g e0_solar_hymap04 spc Two examples of the file sensor_erample dat are given below The first table presents a sensor without thermal bands and without gain settings The second table defines a sensor with 79 bands DAIS 7915 having 6 thermal channels The mid IR bands have to be specified separately no atmospheric correction is performed for these bands The input channel
64. included here they are stored separately file image_fshd bsq 4 9 Processing of multiband thermal data Several options have been implemented to process multiband thermal data see chapter 10 1 5 for details Apart from the final products surface temperature and emissivity intermediate products are available such as surface radiance at sensor blackbody temperature and surface blackbody temperature The intermediate products might be useful to trace back spectral or radiometric problems If image bsq denotes the file name of the input image then the following products are available e image_atm_emi3 bsq 3 or 4 emissivity classes obtained from an on the fly in memory pre classification vegetation soil sand water The pre classification requires daytime data acquisition and spectral bands in the solar region This file has one channel with the emissivity values for the specified thermal band or in case of ANEM the pixel dependent values assign the maximum emissivity of all available thermal bands e image_atm_emiss bsq contains the spectral emissivity map for all thermal channels e image_atm_emiss_lp3 bsq is the same emissivity map but filtered with a 3 channel low pass filter to smooth spectral noise features requires at least 10 thermal bands e image_atm_emiss_lp5 bsq is the same emissivity map but filtered with a 5 channel low pas filter to smooth spectral noise features requires at least 30 thermal bands e image_atm
65. incube dbfile respfile resol outfile featureflags vis zen ele alti chlist results spline zeroborder 0 1 2 range splitband overwrite Smile detection routine PARAMETERS incube input data cube dbfile raw database file to be used for convolution no height interpolation respfile response file e g band001 rsp resol internal resolution for the calculation outfile name of output file for smile coefficients KEYWORDS featureflags bytarr n feat 15 feature regions featureflags i 1 if feature is set else 0 vis visibility km zen solar zenith angle deg zenith at 0 deg ele average ground elevation km alti flight altitude elevation km for pressure compensation chlist list of bands which are used for smile detection and for interpolation of the results numbering starting at 0 results write idl save dump of all results in a file named sav together with the regular output spline 1 spline channel interpolation 0 linear channel interpolation of smile coefficients zeroborder 2 set smile coefficients to 0 at spectral borders first last channel 1 repeat smile coefficients outside of interpolated values range search range default 20 nm splitband splitchannel index between two detectors starting at 0 first band of second detector overwrite silently overwrites the older output e at_smoothdem infile dist outfile median DEM smoothing routine
66. installation for the selection of a x cal file to be displayed Both gain and offset are then plotted in the same graph to get an overview of their relative values 5 1 9 Show System File This is the same function as Show Textfile but defaults always to the ATCOR installation in order to allow to select an ATCOR system file from within the installation such as the cal files the solar reference files the sensor definition files The function then allows to adjust and save the respective text contents of the selected file CHAPTER 5 DESCRIPTION OF MODULES 67 o00 X Calibration File Plot File Font_Size Display Output Help sreJdl ateor atcor23 cal ali ali 22dec2004 cal Calibration Canstonts c0 Offost mW cm ar am c1 Ga in mW cm ar pm DN o 2 4 6 amp 10 Band Number Wovalength Figure 5 9 Plotting a calibration file O X src_idl atcor atcor_4 sensor hymap04 hymap04_final cal File Help 0 00000E 00 1 10356E 03 0 00000E 00 1 08869E 03 0 00000E 00 1 08498E 03 0 00000E 00 1 08277E 03 1 07497E 03 ie 1 07411E 03 1 08829E 03 1 06655E 03 1 07261E 03 1 07881E 03 1 08309E 03 1 07141E 03 1 08243E 03 1 06822E 03 1 05392E 03 1 04424E 03 1 01844E 03 Figure 5 10 Displaying a calibration file same file as in Fig 5 9 5 1 10 Edit Preferences The default settings of ATCOR may be edited through this panel The updated preferences are then written to the ASCI
67. is available the file is to be created from scratch on the basis of the flightpath direction The given parameters are used to to interpolate a guess of the scan angle file under assumption of stable and straight flight conditions between and beyond a given starting point and ending point Accepted Inputs Create SCA file select either option a or b from above Name of Reference File File which defines the spatial dimensions of the output scan angle file to be created this is the GLT MAP file if the respective option is selected but may also be a DEM of output resolution if flight path parameters are used OPTION a Flight Heading direction of the flight path with respect to north east is 90 degrees Flight Altitude average flight altitude in meters OPTION b Starting Point one initial point in pixel coordinates and meters altitude of nadir line the pixel coordinates are the row and column numbers as of the reference file selected in this window Ending Point one final point in pixel coordinates and meters altitude of nadir line NOTE the two points may be anywhere on the flight path they are just used to find the nadir line within the imagery The y coordinates are entered in bottom up system ie the lower left pixel of the image is pixel number 0 0 Use the function File Display ENVI file 5 1 1 to find the correct coordinates END OPTION b Sensor total Field of View total across track FOV of the
68. is clicked Profile Vertical Opens a window for a vertical profile through the image of the first band only Profile Spectrum Opens a window for a spectrum of the image for images with 4 and more bands only Export Allows to export the currently displayed image to one of the given image data formats The displayed image may be exported as a scaled 8bit 24bit image to the available standard image formats Note when clicking in the zoom window the current image value and location is displayed or a small plot of the spectrum at this pixel location is created same as the function Profile Spectrum of above The menu in the such loaded window allows to save the spectrum to an ASCIT table to adapt the graph s properties and font size configure the display and to output the graph to an appropriate graphics format CHAPTER 5 DESCRIPTION OF MODULES Figure 5 4 Display of ENVI imagery 62 CHAPTER 5 DESCRIPTION OF MODULES 63 5 1 2 Show Textfile Use this function if you need to edit a plain text file which comes together with the data to be processed The file is opened in a simple editor and may be changed and saved The function comes handy if an ENVI header needs to be checked or updated e g Selecting the Save or the Save As function in the submenu will allow to overwrite the file or to create a new one O OX src_idl atcor atcor_23 demo_data tm_rugged tm_blforest hdr File Save H
69. is shown for a better visual comparison of the results based on the original spectral calibration thin line and the new calibration thick line The spectral shift values calculated for the 4 individual spectrometers of AVIRIS are 0 1 1 11 0 88 and 0 21 nm respectively 20 70 60 m 15 3 8 40 5 10 S 8 g 30 amp 20 10 0 O ha 0 4 0 6 0 8 1 0 1 2 1 4 0 4 0 6 0 8 1 0 1 2 1 4 Wavelength jem Wavelength jem Figure 2 3 Wavelength shifts for an AVIRIS scene 2 3 Wavelength and refractive index As the wavelength of electromagnetic radiation depends on the refractive index of the medium this effect has to be calculated for airborne sensors if a high accuracy is needed especially for hyper spectral instruments The spectral channel filter functions are usually measured in the laboratory So the measured wavelength depends on the refractive index Ngap or pressure Pjap at the elevation hiap during lab measurement If Ay denotes the wavelength in vacuum i e Nyac 1 the sensor wavelength during a lab measurement is Ao Nab Asen Plab Mas 2 12 We assume a typical scale height H 8 km for the height dependence of pressure and air density i e p h po exp h H 2 13 For a standard atmosphere mid latitude summer we have p0 1013 mbar hPa For a spaceborne sensor the lab measurement is performed in a vacuum chamber therefore nap is close to 1 and Asen Ay The MODTRAN radiative t
70. is similar to the standard cloud assignment in the _out_hcw bsq file where p blue gt Te Te 0 25 or 25 and p red gt 0 15 The 25 reflectance threshold in the blue or green band is the default value in the preference parameter file CHAPTER 10 THEORETICAL BACKGROUND 210 e high cloud probability coded 90 same as for medium probability but with p blue gt 0 35 and p red gt 0 25 10 67 If a thermal band exists the relationships 10 53 must also be fulfilled Water probability The criteria for the water class are described in the previous section The following water probability rules are employed e low water probability coded 30 water pixels fulfilling the above criteria eq s 10 48 10 49 This is the water assignment in the _out_hcw bsq file e medium water probability coded 60 same as for low probability but with a more stringent NIR reflectance threshold If no SWIR1 band around 1 6 um exists the criterion is p NIR lt 0 04 10 68 Note the default threshold Twater v1r is 0 05 or 5 in the reflectance percent unit defined in the preference parameter file yielding more low probability water pixels than medium probability pixels eq 10 68 If a SWIR1 band exists the apparent NIR reflectance thresh old is relaxed first line of eq 10 69 because of the additional SWIR1 surface reflectance threshold p NIR lt 0 05 and p SWIR1 lt 0 03 or P NIR lt 0 03 or
71. is transmitted to the sensor The sum of direct and diffuse flux on the ground is called global flux 3 reflected radiation from the neighborhood L3 scattered by the air volume into the current instantaneous direction the adjacency radiance As detailed in 68 the adjacency radiation L3 consists of two components atmospheric backscattering and volume scattering which are combined into one component in Fig 2 2 to obtain a compact description Only radiation component 2 contains information from the currently viewed pixel The task of atmospheric correction is the calculation and removal of components 1 and 3 and the retrieval of the ground reflectance from component 2 So the total radiance signal L can be written as L Lpath F Lreflected Ladi L L2 L3 2 6 The path radiance decreases with wavelength It is usually very small for wavelengths greater than 800 nm The adjacency radiation depends on the reflectance or brightness difference between the currently considered pixel and the large scale 0 5 1 km neighborhood The influence of the adjacency effect also decreases with wavelength and is very small for spectral bands beyond 1 5 um 68 For each spectral band of a sensor a linear equation describes the relationship between the recorded brightness or digital number DN and the at sensor radiance Fig 2 2 L c c DN 2 7 CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 19 L cy 0DN Figure 2 2
72. land CHAPTER 10 THEORETICAL BACKGROUND 207 Pixels must satisfy the conditions p blue gt T and p red gt 0 15 and p NIR p red lt 2 and p NIR gt 0 8 p red and p NIR p SWIR1 gt 1 and NDSI lt 0 7 or DN blue gt Tsaturation 10 51 where p blue is the apparent reflectance in a blue band Te is the cloud threshold as defined in the preference parameter file and DN blue is the corresponding digital number Tf no blue band is available a green band around 550 nm is taken as a substitute If no green band exists a red band around 650 nm is taken NDSI is the normalized difference snow index nosy Plgreen PUSWIRI p green p SWIR1 Note that saturated pixels in visible bands are automatically counted as cloud although they might be something else e g snow or a specular reflection from a surface If a thermal band exists the following cloud criterion must also be fulfilled 1 p SWIR1 Th lt 225 Kelvin and exclude Thy gt 300 Kelvin 10 53 10 52 where Ta is the at sensor blackbody temperature in the selected thermal band Cloud over water The following criteria have to be fulfilled 0 20 lt p blue lt 0 40 p green lt p blue NIR lt p green p SWIR1 lt 0 15 NDSI lt 0 2 10 54 For optically thick clouds it is not possible to distinguish clouds over water from clouds over land if only spectral criteria are used Cloud shadow P
73. least squares regression of the scatterplot data L versus Lsur face yielding the intercept path radiance Lp and slope transmittance T of eq 10 43 Care has to be taken to apply the regression only to the points near the top edge of all cluster points but allow some margin so the fitting line is allowed to sink by an amount of the sensor noise equivalent spectral radiance NESR The quality of the regression is significantly increased by allowing only those pixels in the scatterplot that had their maximum temperatures in the reference channel Two comments first because of the involved assumptions the obtained intercept is not the physical path radiance and the slope not the physical atmospheric transmittance Both quantities may be negative in some channels therefore they are referred to as unscaled path radiance i and unscaled transmittance 7 They might be rescaled to proper atmospheric path radiance and transmittance spectra e g using a radiative transfer code Second the ISAC method requires an adequate spread in surface temperatures in the scene and surface temperatures higher than the atmospheric radiation temperature So results for night time imagery will likely be degraded The compensated unscaled surface radiance spectrum is calculated as L A Lh A u L A 10 44 Sarre 7 u A and the unscaled ISAC surface emissivity can be obtained with D a e 10 45 where Trep is the brightness temperature
74. least the resolution of the sensor s footprint which is seldom available 62 Even in the latter case errors in the matching of imagery and DEM can lead to large relative reflectance errors exceeding 100 for critical geometries principal plane e g a mountain ridge with half a pixel offset between imagery and DEM 62 Thus the quality of the required DEM will limit the final accuracy of the geo atmospheric image product in many cases For a flat terrain and larger off nadir view angles BRDF effects may have to be accounted for and the appropriate surface cover dependent BRDF model will influence the accuracy Thermal region In the thermal wavelength region beyond 8 um the surface temperature retrieval additionally depends on the correct choice of the surface emissivity In the ATCOR model the emissivity in one thermal band is based on a classification of the reflective bands if the sensor collects co registered reflective and thermal band data Depending on the surface cover classification vegetation soil sand asphalt water etc a typical emissivity value is assigned to each class 78 If the deviation of the true surface emissivity to the assumed emissivity is less than 0 02 a typical error margin then the temperatures will be accurate to about 1 1 5 K A rule of thumb is a surface temperature error of about 0 5 0 8 K per 0 01 emissivity error if the surface temperature is much higher than the boundary layer air temperatur
75. line azimuth was 179 almost exactly pointing into the solar azimuth The left image shows HyMap band 30 at 868 nm after atmospheric correction The right image is the result after nadir normalization with a 1 sampling interval In this example the column means were calculated globally i e surface cover independent The algorithm also contains an option to compute the column means separately for 4 surface covers It can currently only be selected if the input imagery is reflectance data and not geocoded The processing time is much larger than for the global cover independent method The four surface classes are e bright vegetation ratio vegetation index NIR RED gt 10 e medium dark vegetation 6 lt ratio vegetation index lt 10 e dry vegetation or mixed vegetation soil 3 lt vegetation index lt 6 e soil vegetation index lt 3 The reflectance of off nadir water pixels criterion near infrared reflectance lt 5 is not modified 10 6 2 Empirical incidence BRDF correction in rugged terrain For many surface covers the reflectance increases with increasing solar zenith and or viewing angle 45 Scenes in mountainous regions often exihibit a large variation of terrain slopes and thus bidirectional brightness variations for a certain surface cover e g meadow or forest This behavior cannot adequately be eliminated with the Lambertian assumption of equation 10 15 This equation leads to overcorrected reflectance va
76. multiple terrain reflection effects Related quantities to the global spectral solar flux on the ground are the wavelength integrated global flux and the absorbed solar flux Wm These play a role in the surface energy balance and they are available as part of the value added channels see chapter 7 2 equations 7 9 7 10 Surface reflected radiance The ground reflected or ground leaving radiance per band can be obtained in addition to the spectral solar fluxes by setting the parameter irrad0 2 It is calculated corresponding to the surface reflectance cube p z y named scene_surfrad bsq For a flat terrain it is L surf x y E global p x y T 10 32 In case of a mountainous terrrain the direct and diffuse reflected radiation maps from the equations 10 30 and 10 31 are used L sur f dir ey Eair Eai plx y 7 10 33 Again the same output file name is used scene_surfrad bsq 10 1 5 Thermal spectral region Similar to the solar region there are three radiation components thermal path radiance L1 i e photons emitted by the atmospheric layers emitted surface radiance L and reflected radiance L3 The short form of the radiance equation in the thermal region can be written as 39 L Lp T Lea 7 1 e Ffr 10 34 where CHAPTER 10 THEORETICAL BACKGROUND 200 L c 0 DN L Ly A A Figure 10 10 Radiation components in the thermal region L
77. next step calculates the visibility or aerosol optical thickness map using the dense dark vegetation DDV method This is followed by an update of the aerosol model path radiance behavior in the blue to red bands if a blue band exists and the update option ratio_blu_red 0 is enabled Otherwise the selected aerosol model is not changed After calculation of the water vapor map the iterative surface reflectance retrieval is conducted accounting for adjacency and spherical albedo effects After atmospheric correction a spectral polishing for hyperspectral instruments and BRDF correction might be performed The visibility AOT 550 nm retrieval flow chart describes the case with a SWIR band at 2 2 um It starts with a low reflectance threshold T1 0 05 and performs the masking in this SWIR band to obtain the darkest pixels excluding water If the number of reference pixels is less than 1 of 186 CHAPTER 10 THEORETICAL BACKGROUND 187 Read LUTs Masking haze clear cloud water shadow Update Lp visible bands if blue band exists and ratio_blu_red gt 0 Water Vapor Map wv if required bands exist Update LUT LUT wv cirrus removal Iterative reflectance retrieval incl adjacency and spherical albedo Spectral polishing BRDF correction haze removal DDV algorithm VIS map bands Red SWIR or Red NIR shadow removal visibility index vi amp AOT Figure 10 1 M
78. number followed by the five channel dependent coefficients beginning with ag and ending with a4 one line per channel The fixed file name is smile_poly_ord4 dat and it has to be located in the corresponding sensor sub directory In the ideal case these coefficients should be derived from laboratory measurements CHAPTER 4 WORKFLOW 49 Since an accurate description is only required for channels in atmospheric absorption regions the 5 coefficients can be set to zero for the remaining regions but they must be provided for each channel If all 5 coefficients are set to zero for a certain channel this channel is processed in the non smile mode which will expedite the processing e Optionally the spectral bandwidth FWHM might also depend on the image column Again a 4th order polynomial is used to describe the bandwidth change depending on column posi tion x and channel j A x j nm bolj b1 3 x bolj bali bali x 4 3 FWHM z j FWHM j A z j 4 4 The fixed file name is smile_poly_ord4_fwhm dat and it has to be located in the corresponding sensor sub directory It is an ASCII file with 6 columns per channel the first column is the channel number or wavelength the remaining columns contain the polynomial coefficients starting with bo 30 0 512 across track FOV degree pixels per line 1 128 first last reflective band 0 35 2 55 um 0 0 first last mid IR band 2 6 7 0 um 0 0 first last
79. of this section are evaluated for the current earth sun distance For a flat terrain ATCOR provides spectra of the direct diffuse and global flux for the selected visibility water vapor In case of variable visibility water vapor the spectra are calculated for the average scene visibility water vapor The direct flux is just the beam irradiance on the ground times the cosine of the local solar zenith angle The diffuse flux spectrum Ey f is evaluated for a surface reflectance of p 0 and the global flux for p 0 15 i e Ey Edir Edif 0 1 s 0 15 where s is the spherical albedo The spectral band index is omitted for brevity For a flat terrain these fluxes are provided in the directory of the input file e g scene bsq e the direct spectral flux on the ground scene_edir dat e the diffuse spectral flux on the ground scene_edif dat for surface reflectance p 0 e the global spectral flux on the ground scene_eglo dat for a typical average surface reflectance p 0 15 These spectra will already give a realistic description for a flat terrain but they lack the dependence on the spectral reflectance variations in the scene Therefore an image of the global flux is also provided that accounts for the spatial reflectance and visibility water vapor patterns VIS named scene_eglobal bsq Edir VIS z y aay Fais P 0 VIS x y s x y p x y Es x y 10 29 CHAPTER 10 THEORETIC
80. of instrument during operations Absolute pressurized instrument with constant pressure as of this value Relative pressure difference to in flight ambient pressure in hPa value of 0 hPa is ambient pressure Thermal Sensor Temperature Band Number Spectral band used for temperature retrieval algorithm Optional according to sensor type Smile Sensor Smile Sensor Response Type required for convolution when sensor defini tion is not given explicitly may be Butterworth Order 1 slow drop off Butterworth Order 2 close to Gauss Butterworth Order 3 between Gauss Rect Butterworth Order 4 close to Rectangular Gaussian Rectangular Triangular Decreasing Binning from Rectangular to Triangular or Arbitrary Actions New Sensor A new sensor is created within the ATCOR installation which results in a new directory in the sensor directory of the installation Delete Allows to delete any sensor directory and all of its contents Update Sensor The sensor parameters of the selected sensor definition are updated according to the settings Rename The current sensor is renamed both directory and sensorx dat file Outputs A new sensor_x dat file and possibly sensor directory is created ATTENTION this routine requires write access to the sensor directory of the ATCOR installation 5 2 2 Generate Spectral Filter Functions ATCOR requires the spectral response
81. output file name is scene_dh_bilin bsq and scene_dh_trian bsq for ipm 1 2 respectively The dh indicates the dehazing and the interpolation method is also included Additionally a file _haze_map bsq is created containing the classes haze land haze water clear water and geocoded background IF the keyword water 1 is specified then the corresponding names are dhw dh1w etc Typing the name of the module without parameters will yield the list and description of the parameters This program can also be invoked from the ATCOR main panel e reslut_batch sensor zxx aero aero his h1s h2s h2s ith ith Here xxx is the sensor name corresponding to the atcor4 sensor xxx folder The key word aero can have the values rura urba mari or dese If not specified aero rura is the default The keywords hls and h2s specify the lower and uppder flight levels in km e g hls 3 and h2s 5 For the processing of thermal band LUTs the keyword ith 1 has to be set e at_derpolish infile outfile nbin respfile rsp smooth lowpass adj Derivative polishing routine PARAMETERS infile file of reflectances to be filtered outfile name of output file to be created nbin number of adjacent bands to use for filtering nbin 1 KEYWORDS respfile response file used for wavelength reference default ENVI header values lowpass perform lowpass filtering only CHAP
82. p NIR pixels X es gt 1 ofscene pixels lt gt and VIS lt 80 km D gt Increase VIS up to 80 km W Calculate visibility index vi amp AOT maps Non ref pixels are assigned the average VIS ref or spatially interpolated values Figure 10 2 Visibility AOT retrieval using dark reference pixels 10 1 Basics on radiative transfer This chapter presents the basic concepts and the terminology The full set of equations is docu mented here as implemented in ATCOR We start with the radiative transfer equation in the solar spectral region 0 4 2 5 ym for a flat terrain under clear sky conditions First the equation for an infinite plane of uniform reflectance is presented Then the case of a small uniform surface embedded in a large homogeneous background of different reflectance is discussed We continue with the rugged terrain and finally discuss the equations for the thermal spectral region 8 14 um 10 1 1 Solar spectral region For a cloud free sky and a uniform ground of reflectance p the radiance signal received at the sensor consists of scattered solar radiation and ground reflected radiation The scattered radiation component is also called path radiance It depends on the solar and viewing geometry as sketched in Fig 10 3 In case of a flat terrain the at sensor radiance L can be written as Asrar 1989 chapter 9 p E 0 L Lp 0 Os F Ty Ov 1 prs 10 1 CHAPTER 10 THEORETICAL B
83. p SWIRI lt O0 03 10 69 The relationships on the second line of eq 10 69 assign the water probability based on the smaller reflectance value in the NIR or SWIR1 e high water probability coded 90 same as for medium probability but with lower NIR SWIR1 thresholds p NIR lt 0 03 no SWIR1 band NIR lt 0 03 or p SWIR1 lt 0 02 10 70 Note the default threshold Tyater swirri is 0 03 or 3 in the reflectance percent unit defined in the preference parameter file Snow ice probability The criteria for the snow ice class are described in the previous section As mentioned before if pixels are saturated in the blue green spectral bands they are counted as cloud unless the NDSI gt 0 7 The following probability rules are employed for snow CHAPTER 10 THEORETICAL BACKGROUND 211 e low snow ice probability coded 30 p blue gt 0 22 and NDSI gt 0 4 and DN blue lt Tsaturation 10 71 If no blue band exists a green band is used as a substitute If a green band and a SWIR2 band exist the rules are DN blue lt Tsaturation and p blue gt 0 22 NDSI gt 0 4 or p green gt 0 22 NDSI gt 0 25 p SWIR2 p green lt 0 5 10 72 e medium snow ice probability coded 60 same as for low probability but with a more strin gent NDSI threshold of 0 6 This is the snow assignment in the hcw bsq file p blue gt 0 22 and NDSI gt 0 6 and DN blue lt Tsaturation 10 73 If no blue band exist
84. pixels from dark areas to avoid artifacts in full cast shadows all_layers write all layers instead of the illumination file only e at_shadowfilter infile ilufile outfile smfact interp meanadjust minz Filter dark bright borders of shadows after cast shadow correction PARAMETERS reflfile file to be filtered reflectance file ilufile illumination file containing a shadow mask which had been applied to the image outfile output file of the processing KEYWORDS smfact width of shadows to be corrected interp shadow borders are replaced by interpolations instead of brigthness adjustment meanadjust The mean brightness across all spectral bands is adjusted not only band wise min minimum threshold for border pixel detection default 0 02 e at_scalefwhm sensorin sensout_dir fwhmdat meanshift e at_shiftresp sensorin sensout_dir smiledat meanshift Apply FWHM or smile detection results to a sensor PARAMETERS sensorin input sensor definition file sensor dat sensout_dir output sensor name to be created path to directory in sensor directory of atcor installation fwhmdat smiledat files smile poly_ord4 dat to be applied to sensor e at_fwhmdetect incube dbfile respfile resol outfile featureflags vis zen ele alti chlist results spline zeroborder 0 1 2 range splitband overwrite CHAPTER 6 BATCH PROCESSING REFERENCE 148 e at_smiledetect
85. plants Interpolation in the strong atmospheric water vapor absorption regions CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 177 around 1400 nm and 1900 nm is recommended because of the low signal and large influence of sensor noise However interpolation can be disabled if required i e for test purposes If enabled non linear interpolation is performed in the 1400 1900 nm regions by fitting the surface reflectance curves with a hull of a template vegetation or soil spectrum All interpolated channels are marked with an in the ENVI header of the output reflectance cube Haze or sunglint removal over water the default apparent reflectance thresholds in the NIR chan nel for clear water and haze are T clear 0 04 or 4 and To haze 0 12 or 12 respec tively Pixels with values less than T clear are defined as clear water pixels with values between the thresholds Ti clear and To haze are assigned as haze or sun glint A lower value i e T clear lt 0 04 can be can be specified but might cause a wrong classification of bright coastal water sandy bottoms and bleached coral waters If the threshold 7 clear is too high haze pixels might erroneously be put into the clear water category Cast shadow areas mountainous terrain these may contain over and or undercorrected pixels during the standard empirical BRDF correction A reduction of these pixels is tried with the following steps e brigh
86. r from the x y position and A r is the area of a circular zone from r to r dr Now we approximate the circular regions by square regions to obtain the discrete version of eq 10 11 with exponentially decreasing weighting coefficients w nR pP x y p x y af a y Y Ziwi 10 12 i 1 CHAPTER 10 THEORETICAL BACKGROUND 192 mgp Wi and W A r exp r dr J rPexp r ar 10 13 2 Wi ai Ti 1 1 Wi ATCOR supports up to na 5 regions Since the sequence of moving digital low pass filters works with square filters of size 2r 2r the area A r is approximated as the corresponding square region A r 2r Step 3 it includes the spherical albedo effect on the global flux that was initially calculated with the reference background reflectance p 0 15 and is finally adapted to the scene dependent value p by correcting with the difference J pr p x y pP a y 11 Aa y pr s 10 14 Radiation components in rugged terrain Figure 10 5 shows a sketch of the radiation components in a rugged terrain 62 Compared to the flat terrain one additional component is taken into account in the ATCOR model It is an approximation of the terrain reflected radiation It is obtained by weighting the reflected radiation in a 0 5 km surrounding of a pixel with the terrain view factor The terrain view factor is Vierrain y 1 Vsky x y and the sky view factor Vsky y is calculated from the DEM as expl
87. reflected from the neighborhood and scattered into the viewing di rection adjacency effect qDN Figure 10 4 Schematic sketch of solar radiation components in flat terrain Only component 2 contains information on the surface properties of the pixel the other components have to be removed during the atmospheric correction As detailed in 68 the adjacency radia tion L3 consists of two components atmospheric backscattering and volume scattering which are combined into one component in Fig 10 4 CHAPTER 10 THEORETICAL BACKGROUND 191 The radiometric calibration assigns to each digital number DN the corresponding at sensor radi ance L L k co k c1 k DN k 10 6 where k indicates the channel number and cy c are the calibration coefficients offset and slope For sensors with adjustable gain settings the equation is L k co k e1 k DN k g k 10 7 where g k is the gain setting in channel k The atmospheric correction has to be performed itera tively since the surface reflectance and large scale 0 5 1 km neighborhood background reflectance are not known So three steps are employed in the ground reflectance calculation Step 1 The influence of the neighborhood adjacency effect is neglected and the surface reflectance is obtained from a r d co c1 DN Ly Tu Eg Pr 0 15 p 10 8 where the spectral band index is omitted for clarity The facto
88. results gt ee Select display AO Select calibration file Save last spectrum Red 28 Green 16 Blue 3 Cal file hymap04_final cal Atm file 02580_wv20_rura atmi Display image Target box pixels 5 Adj range km 9 20 reference spectrum Message 2 Last box 280 547 water vapor 1 55 cm 5 Visibility km 40 0 Direct plot to w Sereen 1 Screen 2 local neighborhood for adjacency effect more accurate slow if many bands w global neighborhood total image less accurate fast if many bands Extract Spectrum from x 280 y 547 Calculate 3 60 Mixing of Atmospheres intermediate flight altitude water vapor pa w h um 0 0 5 YHIN 0 0 YHAX 60 0 Clear screen 1 0 YMIN 0 0 YMAX h um Clear screen 2 Contrast stretching Gaussian w Histo Eq 0 40 0 Create Zoom Window Return Figure 5 36 SPECTRA module To obtain a target spectrum of the scene click at any position in the image In figure 5 36 the solid white line spectrum at the top shows a meadow signature the green line represents a reference spectrum taken from the spec_lib directory already resampled for the HyMap sensor A reference spectrum can be loaded when clicking the corresponding button at the top right of the panel An exact match of scene spectra and library spectra cannot be expected when they are measured at different times and loc
89. ri a Rs 184 10 Theoretical Background 186 10 1 Basics on radiative transfer 188 10 1 1 Solar spectral region o ak oa ese Be a d Rd Fc ec By ae 188 10 1 2 Illumination based shadow detection and correction 195 10 1 3 Integrated Radiometric Correction IRC o ccce es ce eee ee he es 197 10 1 4 Spectral solar flux reflected surface radiance 2004 198 10 15 Thermal spectral regioni 2 06 4 460 6084 eee ee ee a 199 CONTENTS 6 10 2 Masks for haze cloud Water snow e 205 103 Oualty Tavera aci A Rs A a ds e pr 209 10 4 Standard atmospheric conditions e is se msaa sanse s ie raa KiE ra aa 211 10 4 1 Constant visibility aerosol and atmospheric water vapor 212 10 4 2 Aerosol retrieval and visibility map a soaa a 000200 212 10 4 3 Water vapor retrieval 2 e eiie a aaa ee 217 10 5 Non standard conditions o lt scicca se ea ei ae doa ee 219 10 5 1 Haze removal lt 4 si earra diras aaa a 219 10 5 2 Haze removal method L ss coa a a a socs agaaa ee wee aS 219 10 5 3 Haze removal method 2 a seos ausmaa isa ee ra i eas 220 10 5 4 Haze or sun glint removal over water 2 2 ee ee 221 10 5 5 Cirrus removal s c c awe be ma a a a a ds ia 222 10 5 6 De shadowing with matched filter o o 224 106 Correction of BRDE electa soi s dow d x ee lt Pe Ee a 230 10 6 1 Nadir normalization method o o eae doaa sk owa ae eee es 231 10 6 2 Empirical incidenc
90. surface normal of a DEM pixel and the solar zenith angle of the scene In mountainous terrain there is no simple method to eliminate BRDF effects The usual assumption of an isotropic Lambertian reflectance behavior often causes an overcorrection of faintly illuminated areas where local solar zenith angles range from 60 90 These areas appear very bright see Figure 2 9 left part To avoid a misclassification of these bright areas the reflectance values have to be reduced Fig 2 9 center part In ATCOR empirical geometry dependent functions are used for this purpose In the simplest cases the empirical BRDF correction employs only the local solar zenith angle 8 and a threshold Br to reduce the overcorrected surface reflectance pz with a factor depending on the incidence angle For details the interested reader is referred to section 10 6 2 A more sophisticated method available in ATCOR is the BRDF effects correction BREFCOR method It uses both the surface cover type characterization and the per pixel observation angle to find an appropriate anisotropy factor for correction The method follows a novel scheme based on a fuzzy surface characterization and uses semi empirical BRDF models for the correction The process follows the below steps 1 perform a fuzzy BRDF Cover Index BCI image characterization 2 calibrate the BRDF model using a number of scenes of the same area and time of the year 3 calculate the anisotropy index for eac
91. terrain case If the image has the original scan geometry i e the number of pixels per line corresponds to the number specified in the sensor dat file then the scan angle assignment for each pixel is calculated internally employing the total FOV and pixels per line Otherwise a scan angle file created with PARGE has to be provided In mountainous terrain the DEM DEM slope and aspect files are required Optional input are the skyview file and the shadow map the latter can also be calculated on the fly The slope and aspect files can be calculated from ATCOR s interactive menu or run as a batch job slopasp_batch see chapter 5 The skyview file has to be computed with the skyview program see chapter 5 3 4 CHAPTER 4 WORKFLOW 43 SCA scan angle Input Image BSQ format Flat Terrain Rugged Terrain haze cloud water illumination 1 AOT water vapor y cloud shadow y p T cube T y value added Figure 4 10 Input output image files during ATCOR processing 4 4 Directory structure of ATCOR 4 Figure 4 11 shows the directory structure of the airborne version of ATCOR There are a number of sub directories with the following content The bin directory holds the ATCOR4 program with all modules as listed in chapter 5 The sensor directory holds all supported airborne sensors in sensor specific sub directories As a possible
92. the ratio of the direct to diffuse solar flux on the ground f 1 1 cosO5 cosB w if A lt 1l lum 5 1 f cosOg cosB if A gt 11pm 5 2 Os is the solar zenith angle of the scene For A gt 1 lum the diffuse flux is neglected i e w 1 The factor f is 1 for a flat terrain and some bounds were employed to prevent overcorrection So for each pixel the new digital number is calculated as DN new DN x f 5 3 The method was compared with the standard Minnaert correction eq 5 4 and was superior in most cases Figure 5 29 shows the GUI panel cosO og cos COS f 1 Sy g cosO s cosB 5 4 cos CHAPTER 5 DESCRIPTION OF MODULES Figure 5 29 Topographic correction only no atmospheric correction 85 CHAPTER 5 DESCRIPTION OF MODULES 86 5 4 Menu ATCOR The menu ATCOR contains the main processing modules of ATCOR i e the panels for ATCOR4 for flat and rugged terrain x Airborne ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Haze removal original DN data Licensed for Daniel DLR ReSe 2015 _ _ _ _ _ lt lt ___ ____ ATCOR4F flat terrain ATCOR4r rugged terrain Start ATCOR Process Tiled from inn Figure 5 30 The Atm Correction Menu 5 4 1 Haze Removal The atmospheric haze in imagery may be removed in a statistical procedure before going into atmospheric correction routines Figure 5 31 shows the parameter settings which can be cho
93. the aerosol map is requested for each sub image then specify npref 1 but this could cause different average visibilities in the sub scenes and potentially brightness steps at the sub scene borders CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 184 9 6 Problems and Hints e Distinction of haze and cloud when can the haze removal algorithm be applied Ground surface information under haze areas can still be recognized in the 600 900 nm region but the brightness contrast is low The haze removal is only applied for channels in the 400 800 nm region However for cloud areas no ground information can be observed If in doubt whether a certain area should be assessed as haze or cloud covered take a look at the scene in the NIR around 850 nm channel if surface features can be seen in this area the haze algorithm might be applied If not the area is cloud covered and a haze removal run will not be successful e The cloud mask is not appropiate The cloud mask might contain too few pixels then decrease the cloud reflectance threshold in the atcor preferences preference_parameters dat file The default threshold is 25 re flectance in the blue green spectrum The opposite case can also happen if the cloud mask comprises too many pixels the threshold has to be raised e The haze mask is not appropiate This problem may be connected with the setting of the cloud threshold If the cloud threshold is too high cloud pixels are
94. the scaled function The default of Pmaz is the location of the maximum of the histogram of but it could be set at a greater value if the corrected image is too dark in the expanded shadow regions which indicates the histogram maximum does not represent fully illuminated areas The advantage of the presented method is its fast processing performance because it relies exclu sively on spectral calculations and avoids time consuming geometric cloud shadow pattern consid erations The drawback is that useful geometric information is neglected In some cases it is useful to have the de shadowed digital number DN image in addition to the surface reflectance product This facilitates a comparison with the originally recorded DN imagery The conversion from reflectance to the corresponding at sensor radiance is performed with eq 10 1 Then eq 10 6 is employed to compute the de shadowed DN image for channel k L k co k O 10 116 Figure 10 24 shows an example of de shadowing More images with the results of the de shadowing method can be found on ATCOR s web page http www rese ch or http www op dlr de atcor 10 6 Correction of BRDF effects The bottom of atmosphere reflectance as retrieved after standard ATCOR atmospheric compen sation is highly variable due to the influence of the bidirectional reflectance distribution function BRDF 55 The observed reflectance value may deviate from the average spectral albedo
95. the temperature range Kelvin and box pixels parameters are optional Ten emissivity spectra in nadir direction are evaluated evenly spaced between the image lines Spectral shifts smaller than FWHM 30 usually can be neglected and do not required an update of the calculation of the sensor specific atmospheric LUTs This module can also be started from the main menu of ATCOR Tools Thermal Spectral Calibration Atm Features e thermalcal input filename xpos xpos ypos ypos box bozx Radiometric calibration for thermal band imagery if the scene contains water bodies xpos ypos are the user specified pixel coordinates box is the window size for averaging over box box pixels centered around xpos ypos If box is not specified then box 1 is taken This module calculates the radiometric gain cl asssuming that the offset c0 is zero For this purpose the theoretical spectral emissivity of water is used file eps_water_7 14um dat in the ATCOR4 folder e estimate_wv input filename This module estimates the water vapor column from purely hyperspectral imagery It au tomatically selects 10 pixel spectra from the scene covering a temperature interval Tmin Tmax but avoiding extreme temperatures Then the difference between measured and sim ulated at sensor radiance spectra for these 10 pixels is calculated and the water vapor LUT which minimizes the difference is recommended for processing e specl_batch input filename se
96. the visibility value in the inn file For a constant visibility per scene npref 0 in the inn file the input vis value is the start value that will be iterated as described in chapter 10 4 1 In case of a variable scene visibility npref 1 the vis parameter is ignored if the scene contains enough dark reference pixels If not the program switches to the constant visibility mode and vis is used as a start value IK i si An IDL routine called write_atcor4_inn_file is available to users who want to generate the inn file without the ATCOR GUI Note On the IDL command line the command atcor4 has to be typed first to load the atcor4 sav file Then the atcor4f_tile or atcor4r_tile commands will execute the tile processing A simple trick can be used to start the atcor_tile programs directly on the IDL command line without having to type atcor4 first just copy the atcor4 sav file to atcor4f_tile sav and atcor4r_tile sav The same can be done for atcor4f_batch sav and atcor4r_batch sav For the Linux Unix operation systems a symbolic link is sufficient e g In s atcor4 sav atcor4f_batch sav For Linux Unix users with a full IDL license a batch job can be started directly from the shell e g idl e atcor4f batch input export data data7 atcor4 hymap07 flight71 bsq Most of the modules are available in both modes intera
97. thermal band 7 0 14 um 0 no tilt in flight direction 0 required dummy 1 5 1 smile sensor 5 Gaussian spectral channel filter Table 4 3 Sensor definition file smile sensor without thermal bands Imagery from smile sensors must be processed in the raw geometry IGM Image Geometry Map to preserve the original image columns During the surface reflectance retrieval the atmospheric topographic correction is performed on a per column basis i e to each image column its appropriate center wavelength bandwidth is associated The per column processing typically implies a factor of 8 increase in processing time The following steps are to be performed 1 Define a sensor wvl cal xrsp files RESLUT using the original set of wavelengths pre launch values as provided with the data from the data provider 2 Run the smile detection tool compare Section 5 8 3 using the sensor defined in 1 and ap propriate absorption features to derive the polynomial coefficients smile_poly_ord4 dat for smile correction in step 3 alternatively enter the smile polynomial factors from laboratory calibration Note if two detectors are in the sensor system this should be done separately for VNIR and SWIR option repeat values resolution 0 02 nm Combine the two files for VNIR and SWIR manually into one file afterwards 3 Using the same sensor as above run the atmospheric correction with the smile correction option switched ON
98. to NIR dp A dn So this apparent reflectance gradient criterion is used for high flight altitude imagery while the NIR surface reflectance is employed for flight altitudes below 10 km or typically below 4 km The reason is that the path radiance contribution is rather small for low flight altitudes of typically 1 4 km therefore the apparent reflectance relationship of eq 9 3 does not work It also fails for high flight altitude imagery if the average ground elevation of a scene is high we use gt 1 2 km above sea level Therefore the apparent reflectance criterion is again replaced with the NIR surface reflectance threshold in these cases The water threshold for a 1600 nm band is always included as the second criterion if such a band exists Sometimes the gradient criterion with eq 9 3 is not also adequate for high flight altitudes and the NIR SWIR1 reflectance thresholds yield a better water mask This may happen in urban areas containing shadow pixels cast by buildings Then the NIR SWIR1 thresholds have to be defined as negative reflectance values to overwrite the gradient criterion lt 0 for 04 lt A lt 0 85 um 9 3 The band interpolation options are only intended for hyperspectral imagery Linear interpolation is employed in the 760 725 and 825 nm regions Non linear interpolation as a function of the vegetation index is applied in the 940 and 1130 nm parts of the spectrum to account for the leaf water content in
99. used for the calibration These scenes should have good statistical distribution of the objects of interest in across track direction and shouldn t contain too many clouds and cast shadows 5 Start the model calibration with standard 5 levels on a 3 band image RGB or NRG first and select write ANIF outputs Check the BCI side output image and compare to the selected bci level limits CHAPTER 4 WORKFLOW 58 6 If level limits are not appropriate for the object types the self defined level limits are to be set in the model calibration redo the calibration again Possibly you ll iterate this process and also you may need to increase the fitting accuracy threshold in order to get a BRDF model appropriate to your image 7 Now you re read to go for the full image using the model you just created Check the outputs possibly do a mosaicking for the analysis 8 If everything is correct the whole campaign may be proceed using this calibrated model Chapter 5 Description of Modules For most ATCOR modules a convenient graphical user interface is available but batch jobs can also be submitted If the atcor4 binary atcor4 sav is opened by the IDL virtual machine or when atcor4 is typed on the IDL command line a menu with pull down buttons pops up see Figure 5 1 with a thematic grouping of modules A detailed discussion of the interactive panel driven modules is given hereafter whereas a description of the batch commands can be
100. water vapor content using the pre calculated LUTs The moving averaging window box is selected at 100 m x 100 m The method requires moderate to high temperature contrasts in the moving window otherwise results are not reliable Therefore it is preferable to retrieve the water vapor map from channels in the solar reflective region if possible CHAPTER 10 THEORETICAL BACKGROUND 205 10 2 Masks for haze cloud water snow A useful first step before executing an atmospheric correction is the calculation of a pixel map for haze cloud water snow etc Such a pre classification has a long history in atmospheric correction methods 22 40 58 59 60 41 48 It is also employed as part of NASA s automatic processing chain for MODIS 1 using the classes land water snow ice cloud shadow thin cirrus sun glint etc A similar approach is taken here The calculation is done on the fly and if the scene is named scene bsq then the corresponding map is named scene_out_hcw bsq There is also the possibility to provide this information from an external source if a file scene_hcw bsq exists in the same folder as the scene bsq then this information is taken and the internal ATCOR calculations for this map are skipped In this case the coding of the surface types has to agree with the ATCOR class label definition of course see Table 10 2 This file is written if the corresponding flag is set to 1 see chapter 9 4 an
101. where a linear interpolation is performed The ratio of the filtered to the original soil spectrum is the spectral polishing function applied to all image pixels If zxx_atm bsq is the atmospherically corrected input image then xxr_atm_polish bsq is the pol ished output reflectance cube and the spectral polishing function is stored in xxx_atm_polish dat an ASCII file with two columns containing the center wavelength of each channel and the polishing factor Figure 5 63 shows the GUI panel 5 6 5 Flat Field Polishing This routine is to remove spectral artifacts from atmospherically corrected imaging spectroscopy data Average residual gains and offsets are calculated by comparison of assumed flat field data values to the measured values for each spectral band to find a gain correction factor The flat field is searched automatically within an image as the spectrally least variable part of the image 8000 ATCOR Flat Field Polishing Selec Input File Name cubes nuperiori ED1H1220642004125110PY_L1T_157band_subset bsq Type of Correction Function w Gain and Offset Gain only Help Run _Done Figure 5 64 Flat field radiometric polishing Inputs Input file name usually output of atmospheric correction _atm bsq can be any kind of image Type of Correction Function e Gain and Offset calculate average residual gain and offset for each pixel and apply them as correction function e Gain only constra
102. 0 Visibility km Figure 10 14 Optical thickness as a function of visibility and visibility index CHAPTER 10 THEORETICAL BACKGROUND 217 10 4 3 Water vapor retrieval A water vapor retrieval can be included after the aerosol retrieval because the aerosol retrieval does not use water vapor sensitive spectral bands but the water vapor algorithm employing bands around 940 or 1130 nm depends on aerosol properties The water vapor retrieval over land is performed with the APDA atmospheric precorrected differential absorption algorithm 81 In its simplest form the technique uses three channels one in the atmospheric water vapor absorption region around 940 or 1130 nm the measurement channel the others in the neighboring window regions reference channels The depth of the absorption feature is a measure of the water vapor column content see figure 10 15 In case of three bands the standard method calculates the water vapor dependent APDA ratio as Lolps u Loplu 10 85 wi L1 p1 Lip w3 L3 p3 Lap where the index 1 and 3 indicates window channels e g in the 850 890 nm region and 1010 1050 nm region respectively Index 2 indicates a channel in the absorption region e g 910 950 nm L and Lp are the total at sensor radiance and path radiance respectively The symbol u indicates the water vapor column The weight factors are determined from wi A3 Az2 A3 A1 and w A2 1 3 Az
103. 000_wv10 tem The wv10 might not be the correct water vapor column and for instance the wv04 or wv29 could be more realistic However this mainly influences the depth of the atmospheric absorption spectrum it has a small influence on the wavelength shift calculated during the spectral calibration The spectral sampling distance SSD of the high resolution thermal database bt7 files is SSD 0 4 cm in the 7 10 um region and SSD 0 3 cm in the 10 14 9 um region The full width at half max FWHM is always twice the sampling distance This means we have a variable SSD CHAPTER 3 BASIC CONCEPTS IN THE THERMAL REGION 35 in wavelength about 2 4 nm below 10 um and 3 5 nm in the 10 13 wm part of the spectrum This is adequate for the processing of thermal band imagery with bandwidths greater than 25 nm Chapter 4 Workflow This chapter familiarizes the user with ATCOR 4 s workflow and with the program s basic func tionality using the graphical User interface A detailed description of all modules and user interface panels is given in the subsequent chapter 5 ATCOR may also be used in batch mode for most of its functions A description of the batch mode can be found in chapter 6 4 1 Menus Overview To start ATCOR 4 double click the file atcor4 sav It will be opened through IDL or the IDL virtual machine Alternatively type atcor4 on the IDL command line after having added the atcor4 direct
104. 09 228 50 322 240 306 54 bright concrete 0 35 0 40 0 07 222 48 330 240 306 54 bright concrete 0 35 0 40 0 07 164 210 37 Table 7 1 Heat fluxes for the vegetation and urban model All fluxes in WWm All radiation and heat fluxes are calculated in units of Wm They represent instantaneous flux values For applications where daily 24 h LE values are required the following equation can be used for unit conversion cm 1 E LE Wm 7 27 Fe 535 LE Wm 7 21 The latent heat flux LE is frequently called evapotranspiration ET Although LE and ET are used interchangeably the unit em day or mm day is mostly employed for ET For water surfaces the distribution of net radiation into G LE and H is difficult to determine because it depends on several other parameters Therefore G and H are set to zero here and so LE equals R Spatial maps files of air temperature and air emissivity can also be included in the processing Usually isolated point like measurements of air temperature are available from meteorological stations These have to be interpolated to generate a spatial map coregistered to the image prior to applying the ATCOR model Data in the file containing the air temperature must have the Celsius unit data of the air emissivity file must range between 0 and 1 Future improvements to the ATCOR model will include an air temperature map derived from the image tr
105. 0_solar_ads80 spe F Include Terrain Illumination Calculate Skyview Estimate Options Apply Shade Pixel Filter l Write all Side Layers Range of index from full cast shadow to none Max 2 0 b 200000 tot b 500000 Select Slope File Name Ycubes ads _temp TueDec101408512013_876 swissalti_25_95_el11_DTM_1022_0_0_slp bsq Solar Zenith deg 5 9000 Solar Azimuth deg 155 800 Aircraft Altitude a g km 5 18300 DeFine Name of Output Illumination File Ycubes ads _temp TueDec101408512013_876 201208191022NRGBNOOA1308L2_0_0_ilu bsq Help Run Done Figure 5 27 Panel of Image Based Shadows 5 3 7 DEM Smoothing Smooth a DEM or any other single band image in order to remove artifacts in the atmospherically corrected imagery All related DEM layers are automatically smoothed as well e g slope aspect skyview Alternatively this task could be done with any image processing software Inputs Input DEM File Name Usually a DEM _x ele bsq is selected here but any other single band ENVI image or the _ilu bsq file is also accepted The routine searches automatically for related files i e _sky _slp and or _asp and smoothes them with the same parameters Diameter of DEM Filter Size of filter box in pixels diameter Output Name Name of Elevation file output auxiliary layer names will be derived from that Outputs ENVI file s smoothed or filtered by the given factor and method Actions Smoo
106. 100 causes the output file to be coded as signed 16 bit integer i e with two bytes per pixel The specification s 1 0 produces a float output image i e with 4 bytes per pixel Attention The float output could be used for testing on small images For large files and an input data type of 2 bytes per pixel the output float image would require twice the disk space of the input image The default output data type is signed 16 bit integer for all integer and float input data employing the scale factor s 100 The scale factor is always included in the output ENVI header file Note Any positive value of the scale factor s is accompanied with a truncation of surface reflectance values at 0 in the output cube So a negative reflectance e g caused by a wrong choice of visibility or inaccurate radiometric calibration will be reset to zero in the output image In the SPECTRA module no truncation is applied If a user wants the output reflectance cube without zero truncation the scale factor s should be specified with a negative value e g s 1 will provide a float output surface reflectance retaining negative reflectance values s 100 will provide a 16 bit integer output file The byte scale 10 lt s lt 1 and output data range 0 255 cannot be used to represent negative values The negative scale factor should only be used for test purposes since the results do not make a physical sense and some further processing options or modules
107. 17_173705_L401_atm bsq Bel 1 50 0 20 437nm B l 0 90 0 20 437nm BCI 0 20 0 40 437nm 5 O 2 SE aa se 0 10 T T 010 4 T acep os 3 3 5 5 5 0 06 3 3 3 3 3 3 E 02 E 2 E oF 4 0 04 o1 a oo O00 PR PPPOE or rra cra ab 2442 0 00 20 10 06 10 20 20 10 10 20 20 10 0 10 20 Observer zenith angle Observer zenith angle Observar zanith angle fgeo 0 000 fok 0 000 f gaor 0 018 fvol 0 950 tge 0 020 fol 0 390 Bek 1 50 0 90 572nm Bok G 90 0 20 57 nm BCI 0 20 0 40 579m AAA 0 25 a L ozo gt 0 20 N os T A ag T sh g est gos 5 osh tJ 5 ae 3 3 3 3 g i iof 010 BE os E men I o2 i N ocs 0 06 A S E Ola tirao ar raro ar a 40d Xen atraca rato arar ara a 1 41 0 00 20 10 0 10 20 20 10 0 10 20 20 10 0 10 20 Observar zenith angle Observar zanith angle Observar zanith angle fgeo 9 000 folk 0 000 f gaor 0 043 Lrol 2 140 fgeo 0 033 fyol 0 780 al 3 pl El J FER EE Spectral Band Number top Spectral Band Number bottom BCI Levels first Help Export Graphics BCI G 40 0 90 437nm Reflectance 2 2 3 Y A 2 N 0 001 20 10 0 10 20 Observer zenith angle fgeo 0 000 fol 0 140 BCI G 40 0 90 579nm 2 2 N R gt 1 A Reflectance ee 2 2 E amp 8 5 ATT T N y ES 2 R 7 2 a 20 10 o 10 20 Observer zenith angle fgeo 0 020 fyo 0 020 4 aS
108. 1900 nm channels i1400 1 nonlinear interpolation 11400 2 linear e toarad input filename pirelsize pixelsize sz solar_zenith atmfile atmfile elev elevation vis visibility adjrange adjrange scalef scalef The keywords in brackets are optional the meaning of all keywords is described in chapter 8 Information on all missing keywords is taken from the corresponding ini file If the keyword elev is missing and the corresponding inn file contains the DEM files elevation slope aspect then the simulation is performed for a rugged terrain otherwise for a flat terrain compare chapter 8 e toarad2 input filename pixelsize pixelsize atmfile atmfile elev elevation vis visibility Similar to program toarad but for thermal channels compare chapter 8 CHAPTER 6 BATCH PROCESSING REFERENCE 144 e cal_regress ntargets 4 outfile regression4 This program uses the rdn files to calculate a regression for the c0 cl radiometric cali bration see chapters 2 4 5 4 8 The above example is for the case of n 4 targets and the output file will be regression4 cal in the directory of the rdn files which are prompted with a dialog pickfile panel A graphical user interface for this program is available in the Tools pull down menu of ATCOR labeled Calibration Coefficients with Regression e sp_calth input filename trange 280 320 box 3 Spectral calibration in the thermal region
109. 6 CHAPTER 4 WORKFLOW 37 x Airborne ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Display ENVI File Version 7 0 0 c DLRReSe 2015 7 Show Text File Select Input Image Resize Input Image Import Geo TIFF Export RGBN Geo TIFF Plot Sensor Response NRGB Geo TIFF Plot Calibration File IPEG2000 Geo Show System File ENVI BIP Image Edit Preferences ENVI BIL Image QUIT ERDAS Imagine AYIRIS Figure 4 2 Top level graphical interface of ATCOR File Airborne ATCOR File Sensor Topographic ATCOR BRIF Filter Licena Define Sensor Parameters Simulation Tools sion 7 0 0 Le DLR ReSe 2015 mk Generate Spectral Filter Functions Apply Spectral Shift to Sensor BBCALC Blackbody Function T fiL RESLUT Resample Atm LUTs from Monochr Database Figure 4 3 Top level graphical interface of ATCOR Sensor The menu ATCOR gives access to the ATCOR 4 core processes for atmospheric correction in flat and rugged terrain It also allows the tiled processing It is further described in chapter 4 2 below and in chapter 5 4 The BRDF menu provides access to the BREFCOR BRDF effects correction method and to the nadir normalization for wide field of view imagery see chapters 5 5 and 5 5 2 The Filter menu provides spectral filtering of single spectra reflectance emissivity radiance provided as ASCII files spectral filtering o
110. 6 543 nm Top left original right de shadowed image bottom shadow map Figure 2 8 Nadir normalization of an image with hot spot geometry Left reflectance image without BRDF correction Right after empirical BRDF correction CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 31 Figure 2 9 BRDF correction in rugged terrain imagery Left image without BRDF correction Center after BRDF correction with threshold angle Br 65 Right illumination map cos Figure 2 10 Effect of BRDF correction in an image mosaic ADS image swisstopo Chapter 3 Basic Concepts in the Thermal Region Fig 3 1 left presents an overview of the atmospheric transmittance in the 2 5 14 um region The main absorbers are water vapor and CO which totally absorb in some parts of the spectrum In the thermal region 8 14 um the atmospheric transmittance is mainly influenced by the water vapor column ozone around 9 6 um and CO at 14 wm Fig 3 1 right shows the transmittance for three levels of water vapor columns w 0 4 1 0 2 9 cm representing dry medium and humid conditions T he aerosol influence still exists but is strongly reduced compared to the solar spectral region because of the much longer wavelength So an accurate estimate of the water vapor column is required in this part of the spectrum to be able to retrieve the surface properties i e spectral emissivity and surface temperature
111. ACKGROUND 189 Figure 10 3 Radiation components illumination and viewing geometry L at sensor radiance for surface reflectance p Ly path radiance Ta total ground to sensor atmospheric transmittance sum of direct Tair and diffuse Tq f transmittance Eg global flux on a horizontal surface sum of direct Eqir and diffuse Equip flux Ey 0 is calculated for a ground surface with p 0 Pr large scale reference background reflectance determining the effective global flux p 0 15 is used for ATCOR S spherical albedo of the atmosphere accounts for atmospheric backscattering to the ground The geometry is described by the angles view zenith and Os solar zenith and relative azimuth angles compare figure 10 3 Since p and p are not known for image data and can vary within a scene equation 10 1 has to be solved for p iteratively compare equations 10 9 10 15 In a strict sense the reflectance p used here should be called hemispherical directional reflectance factor HDRF because most surfaces show an anisotropic reflectance behavior characterized by the bidirectional reflectance distribution function BRDF Nicodemus 1970 Slater 1985 The ground is illuminated hemispherically by the direct and diffuse solar flux and the reflected radia tion is recorded from a certain direction i e hemispherical input radiation directional reflected radiation Since the reflected radiation is always measured in a small co
112. AL BACKGROUND 199 Here indicates a spatial averaging with a filter size corresponding to the specified adjacency range The unit of the global flux is mWem um and it is stored as float data 32 bits pixel Therefore its file size will be twice or four times the size of the input scene if the scene is encoded as 16bit pixel and 8bits pixel respectively For a rugged terrain images of the direct and diffuse fluxes will be calculated using the available DEM information on height z slope and aspect i e local solar illumination angle 8 and atmospheric conditions visibility water vapor VIS The direct flux on the ground is Edir x y b x y Eo Tsun VI S x y 2 cosB z y 10 30 where Eo Tsun are extraterrestrial solar irradiance and sun to ground transmittance respectively and b is the topographic shadow mask 0 shadow 1 sunlit pixel The diffuse flux in mountainous terrain accounts for the adjacency effect and multiple reflection effects from the surrounding topography Using the terrain view factor V from the last section and the effective terrain reflectance py Vi x y p x y and p Vi a y p x y the diffuse flux is approximated as Eaz a y Edif flat b Tsunlx y z cosB cos0s 1 b zx y Teun x y 2 Very 2 y Edir flat y 2 Espia 2 Y 2 pe pio y 10 31 The first line describes the anisotropic and isotropic components of the diffuse flux the second line accounts for
113. Atmospheric Topographic Correction for Airborne Imagery ATCOR 4 User Guide Version 7 0 0 June 2015 R Richter and D Schlapfer 1 DLR German Aerospace Center D 82234 Wessling Germany ReSe Applications Langeggweg 3 CH 9500 Wil SG Switzerland DLR IB 565 02 15 The cover image shows Sequence of ATCOR BREFCOR process for a mosaic of five image lines of CASI imagery Upper left original image middle elevation data ranging from 500 to 1200 m right ATCOR standard correction using the given DEM lower left BCI image ranging from 0 5 to 0 8 middle ANIF factor ranging from 0 9 to 1 1 approx lower right BREFCOR corrected image An improved BRDF correction algorithm BREFCOR has been introduced in the ATCOR 2015 release ATCOR 4 User Guide Version 7 0 0 June 2015 Authors R Richter and D Schlapfer DLR German Aerospace Center D 82234 Wessling Germany 2 ReSe Applications Langeggweg 3 CH 9500 Wil SG Switzerland All rights are with the authors of this manual The ATCOR trademark refers to the satellite and airborne versions of the software Distribution ReSe Applications Schlapfer Langeggweg 3 CH 9500 Wil Switzerland Updates see ReSe download page www rese ch software download The ATCORO trademark is held by DLR and refers to the satellite and airborne versions of the software The PARGEO trademark is held by ReSe Applications The MODTRAN trademark is being used with the
114. BCI Levels last Done Figure 5 56 BRDF model fitting analysis panel Sliders and Inputs 108 Select This lets you select a model file IDL save file which has been created by the BREFCOR model calibration procedure Input File Slider If calibration has been done on multiple files at once the files may be selected here Setting this slider to 0 displays the averaged results from all files Spectral Band BCI Levels Slider Lets you select the respective band number and BCI level number BRF Range Defines the scaling of the graphics in absolute BRF values or in ANIF values if selected in the model Options below The button AutoRange will reset to the min max of the available data Max Zenith Maximum sensor zenith angle to be displayed default 45 degrees off nadir rho_iso f_geo f_vol Factors of 3 parameter BRF model to be plotted these factors may be edited for test purposes on return the plot is updated Solar Zenith Solar zenith angle for the plot in degrees Options Allows to select variations of the BRDF model and to plot Anisotropy instead of BRF values CHAPTER 5 DESCRIPTION OF MODULES 109 Select BRIF Model File Yeutes CASI chi1e brefcor brefcor_3ed_nodel sav Input File cubes CASI chile run401 CASI_2013_01_17_173705_L401_atm bsq 1 2 2 a Input File Number Spectral Band Number BCI Levels rho_iso 156659 f_geo p o150000 f_vol i 400000 Solar Zenith deg 2 000
115. DDED PRODUCTS 154 counted with a positive sign the upwelling thermal surface radiation has a negative sign The absorbed solar radiation can be calculated as 2 5um Rsolar 1 p A EJ A dA 7 9 0 3m where p A is the ground reflectance 1 p A is the absorbed fraction of radiation and Ey A is the global radiation direct and diffuse solar flux on the ground The numerical calculation of equation 7 9 is based on the same assumptions regarding the extrapolation of bands and interpolation of gap regions as discussed in chapter 7 1 dealing with the surface albedo If the airborne imagery contains no thermal band s from which a map of ground temperature can be derived then Rsolar is the only surface energy component that can be evaluated In case of flat terrain with spatially varying visibility conditions or rugged terrain imagery a map of the global radiation is included as an additional value added channel 2 5um E f E A d 7 10 0 3um For flat terrain imagery with constant atmospheric conditions the global radiation is a scalar quan tity and its value can be found in the log file accompanying each output reflectance file For rugged terrain imagery the global radiation accounts for the slope aspect orientation of a DEM surface element With thermal bands a ground temperature or at least a ground brightness temperature image can be derived Then the emitted surface radiation is calculated as Reur face
116. DEM elevation file name empty line for a flat terrain calculation line 10 fslp DEM slope file name empty line for a flat terrain calculation line 11 fasp DEM aspect file name empty line for a flat terrain calculation CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 179 line 12 fsky DEM skyview file name empty line for a flat terrain calculation line 13 fshd DEM cast shadow file name empty line for a flat terrain calculation rugged terrain empty if calculated on the fly line 14 atmfile atmospheric LUT file name reflective region e If the automatic aerosol type retrieval is intended for batch jobs the usual aerosol identifier in the file name e g rura has to be replaced with auto Example file name without path is 2403000_wv10_rura atm replace it with h03000_wv10_auto atm The program then uses all aerosol types for the 3000 m altitude in the aerosol type estimate and selects the one with the closest match compare chapter 10 4 2 Of course the user has to provide the desired aerosol types using the RESLUT program If only one aerosol type is available for the specified altitude the program will use this atmosphere and quit the automatic aerosol mode The automatic aerosol type retrieval requires the parameter npref 1 variable visibility see line 20 below If npref 0 it is reset to npref 1 In the interactive mode the user can just press the Aerosol Type butt
117. H B T Ta 7 18 B 286 0 0109 0 051 NDVI 7 19 CHAPTER 7 VALUE ADDED PRODUCTS 156 0 95 color Idso amp Jackson Brutsaert 0 90 RH 90 E RH 70 5 5 0 85 RH 50 o 2 E 0 80 Z 0 75 0 79 0 65 O 5 10 15 20 25 30 35 Air Temperature C Figure 7 2 Air emissivity after Brutsaert eq 7 13 and Idso Jackson eq 7 16 n 1 067 0 372 NDVI 7 20 P850 P650 NDVI 7 21 0 75 P850 P650 en Equation 7 18 corresponds to equation la of Carlson et al 1995 because G is neglected there and so R G represents the energy left for evapotranspiration The factor 286 in equation 7 19 converts the unit em day into Wm NDVI is the scaled NDVI ranging between 0 and 1 and truncated at 1 if necessary Equation 7 21 corresponds to equation 3 of Carlson et al 1995 with NDV Io 0 bare soil and NDVI 0 75 full vegetation cover The latent heat flux LE is computed as the residual LE R G BH 7 22 A different heat flux model is employed for urban areas with man made surfaces asphalt concrete roofs etc These are defined here with the reflectance criteria Peso 0 10 and psso 0 10 and P650 gt p850 0 7 7 23 representing low vegetation indices with NDVI lt 0 176 This simple spectral definition is not unambiguous it might also apply to soils For urban areas the latent heat is usually very small and the fluxes G and H dominate Therefore th
118. Hs we OR RR E Re Ge ee Be 76 DEM aOR i s ke tes oe ee ee hk BL be ae A oe we ts 77 DEM Preparation sl ae oti ese eh Ee BE a Re gs 78 NS PS ai A ee ew Be ee E Se do A 79 pkyview PACtOr oia fe ek ee oe ee ee ao ee Pas Boal 80 Cast Shadow Mask s ar fa acd ot Ba wee ee a oa ee ea eS 80 Image Based Shadows ee a 81 DEM Smoothing incas sd Ae RE PE eR Se eee ee SS 83 Quick Topographic no atm Correction o o o e 84 ATL Di ss Be Ge Se ee a EA a e 86 Haze Removal oa fo ros ad eR de E 86 The ATOOU mami panel ox es Gee ok be a OR Ee es 88 ATCORAR flat ETA or oe A ER ee eG 89 ATCORAr rugged terrain o se ee ee ek ke ee oe Hp ka ee 90 SPECTRA Mod l s sais ee aud eR ERS e a ES 91 Aerosol Type e s as Khe a RA we ae Rl oe Be BR we eR ee ad 92 Visibility Estimate s lt 4 2 25 24 Ab EAH REE aS Ee eRe ee a a 92 Inflight radiometric calibration module 2004 92 Shadow removal panels o s s a sos edatia 0 adka duos KATE ee 95 Panels for Image Processing 262 ce kt Re p a ak daoi 98 Start ATCOR Process Tiled trom 4 00 ces css WA 103 BREF poe cop A 104 BREFCOR Oormertiot o a toa aote aa Si as aA E Ae aa a 104 Nadir normalization Wide FOV Imagery o ooe 106 BRIDE Model Analysis s e acs ece a 2445 bo a a A aa e A 106 BRDF Model Plot occasion 107 Mosaiddng pom a ee a Re Pe Rae eR a eee 110 UE he os ois ob ae ddr een ds Bs Gs are ee ee eee ee E AN 112 Resample a Speci esca o Bk ae ea a ee a e
119. I file as displayed at the top of the panel The preferences persist for the user who started the ATCOR application the next time the system is started and also for batch processing NOTE Preferences are set when one of the ATCOR modules has been opened So one should select one of the modules from within the menu ATCOR before editing the preferences CHAPTER 5 DESCRIPTION OF MODULES E ree E po pos E paso po Figure 5 11 Panel to edit the ATCOR preferences 68 CHAPTER 5 DESCRIPTION OF MODULES 69 5 2 Menu Sensor The menu Sensor is used to create a new sensor from calibration information if the sensor is not supported as standard sensor by ATCOR X Airborne ATCOR File Sensor Topographic ATCOR ERDF Filter Simulation Tools Help Licens Define Sensor Parameters sion 7 0 0 c DLR ReSe 2015 mk Generate Spectral Filter Functions Apply Spectral Shift to Sensor BBCALC Blackbody Function T F L RESLUT Resample Atm LUTs from Monochr Database Figure 5 12 The New Sensor Menu Fig 5 13 shows the three required steps to include a new hyperspectral sensor to ATCOR The example uses a sensor with 96 spectral bands denoted as sensor_x96 Using the function Define Sensor Parameters the following steps are done in the background which also could be done manually a sub directory of atcor sensor is created named x96 and the three files as displayed in Fig 5 13
120. IMULATION OF HYPER MULTISPECTRAL IMAGERY 163 NEAT noise ms sensor hs sensor Radiance Resampling At Sensor Thermal Radiance image bsq ll atmospheric parameters hs sensor ms sensor surface temperature Emissivity surface _ Resampling emissivity Figure 8 4 Sensor simulation in the thermal region 2 If a keyword is set it will overwrite the corresponding parameter from the inn file compare chapters 6 3 and 9 5 To perform a TOA at sensor radiance simulation for a given airborne scene the user has to resample files from the monochromatic atmospheric database e for the chosen new airborne altitude to be simulated if not already available e for the altitude 99 000 m that serves as flight altitude for space sensors see chapter 9 1 After running ATCOR4 for a certain scene and sensor a surface reflectance cube is obtained which is input to the TOA at sensor simulation that can be performed for a flat or a mountainous terrain A detailed description of the toarad keywords follows e input datal image_atm bsq the _atm bsq indicates a surface reflectance file which is the output of an ATCOR run The input file to ATCOR was datal image bsq and toarad extracts some information from the corresponding file datal image inn for example the sensor name The output file name is datal image_toarad bsq e atmfile
121. MPLEMENTATION REFERENCE AND SENSOR SPECIFICS 181 If only VNIR bands exist 400 1000 nm the ratio holds for the red to NIR band ratio blu red ratio of surface reflectance of blue band to red band for the reference pixels line 23 0 65 0 0 25 ibrdf beta_thr thr_g parameters for BRDF correction in rugged terrain For a flat terrain these parameters are not used e ibrdf 0 no empirical BRDF correction or flat terrain e ibrdf 1 correction with cosine of local solar zenith angle eq 10 118 with b 1 e ibrdf 2 correction with sqrt cos of local solar zenith angle eq 10 118 with b 1 2 e ibrdf 11 correction with cosine of local solar zenith angle eq 10 118 with b 1 for soil sand Vegetation eq 10 118 with exponent b 3 4 and b 1 3 for lt 720 nm and A gt 720 nm respectively i e option a in the BRDF panel see Figure 5 46 weak correction e ibrdf 12 correction with cosine of local solar zenith angle eq 10 118 with b 1 for soil sand Vegetation eq 10 118 with exponent b 3 4 and b 1 for A lt 720 nm and A gt 720 nm respectively i e option b in the BRDF panel see Figure 5 46 strong correction e ibrdf 21 correction with sqrt cos of local solar zenith angle eq 10 118 with b 1 2 for soil sand Vegetation eq 10 118 with exponent b 3 4 and b 1 3 for A lt 720 nm and A gt 720 nm respectively i e option a in the BRDF panel see Figure 5 46 weak correctio
122. OLAR REGION 18 Apparent reflectance The apparent reflectance describes the combined earth atmosphere behavior with respect to the reflected solar radiation TL 2 5 E cos0s 2 5 p apparent where d is the earth sun distance in astronomical units L cg cl DN is the at sensor radiance Co c DN are the radiometric calibration offset gain and digital number respectively E and 0 are the extraterrestrial solar irradiance and solar zenith angle respectively For airborne imagery the use of the downwelling solar flux Eg at the aircraft altitude would be a more accurate description but Eq is not available in the code Therefore the extraterrestial E is employed which is a useful approximation For high flight altitudes above 4 km the difference between E and Eg is small For imagery of satellite sensors the apparent reflectance is also named top of atmosphere TOA reflectance 2 1 Radiation components We start with a discussion of the radiation components in the solar region i e the wavelength spectrum from 0 35 2 5 um Figure 2 2 shows a schematic sketch of the total radiation signal at the sensor It consists of three components 1 path radiance L1 i e photons scattered into the sensor s instantaneous field of view with out having ground contact 2 reflected radiation L2 from a certain pixel the direct and diffuse solar radiation incident on the pixel is reflected from the surface A certain fraction
123. OR SPECIFICS 182 p wv mb or hPa default water vapor partial pressure at z0_ref zh_pwv km scale height of water vapor exponential decrease falls to 1 e value Parameters for the net flux calculation rugged terrain ksolflux gt 0 These are dummy values not used if ksolflux 0 or for a flat terrain line 28 2 2 ihot_mask ihot_dynr parameters for haze correction ihot_mask 1 small area haze mask ihot_mask 2 large area haze mask ihot_dynr 1 thin to medium haze levels are corrected ihot_dynr 2 thin to thick haze levels are corrected line 29 2 0 500 0 12 0 08 1 iclshad_mask thr_shad phi_unscl_max phi_scl_min istretch_type Parameters for correction of cloud building shadow effects if icl shadow gt 0 Default values are put in this line even if icl_shadow 0 iclshad_mask 1 2 3 small medium large cloud shadow mask thr_shad 0 500 threshold for core shadow areas 999 means threshold is calculated from image histogram phi_unscl_max max of unscaled shadow function Pa see chapters 2 5 10 5 6 phi_scl_min min of scaled shadow function see chapters 2 5 10 5 6 istretch type 1 linear stretching 2 exponential stretching of into line 30 ch940 1 6 vector with 6 channel numbers for the 940 nm water vapor retrieval ch940 1 left window channel 850 890 nm ch940 2 right window channel 850 890 nm ch940 3 left absorption channel 920 970 nm c
124. OSOL TYPE VISIB ESTIMATE INFLIGHT CALIBRATION Help WATER VAPOR IMAGE PROCESSING Output file already exists change name or press OVERURITE MESSAGES QUIT Figure 5 32 ATCOR panel Options that are not allowed for a specific sensor will appear insensitive If the haze removal option is selected in combination with Variable Visibility the visibility index proportional to total optical thickness map is coded with the values 0 182 The value visindex 0 corresponds to visibility 190 km each integer step of 1 corresponds to an AOT increase of 0 006 The array serves as a fast method of addressing the radiative transfer quantities transmittance path radiance etc in case of a spatially varying visibility i e in combination with the DDV algorithm IF the Haze or Sunglint Removal button is selected the next panel will ask for haze removal over land option 1 haze or sunglint removal over water option 2 or haze removal over land and water option 3 In case of the rugged terrain version of ATCOR the panel for the DEM files has to be specified in addition Figure 5 33 It pops up after the input file has been specified A quick quality check is performed on the DEM files The solar illumination file is calculated and if its standard deviation is large the panel of Figure 5 34 pops up with a warning In this case the DEM elevation file and the derived files of DEM slope aspect etc probably
125. R 5 DESCRIPTION OF MODULES 131 5 8 6 Convert High Res Database New Solar Irradiance The standard solar irradiance curve used with ATCOR is the Fontenla database 2011 However there s some uncertainty about the solar irradiance and people may want to use ATCOR with a different solar reference function This module CONVERT_DB3 converts the complete high resolution atmospheric database from the current definition to a new irradiance function Normally the standard database is converted this function does not apply to the thermal IR but also the specific CHRIS database may be selected In the panel see Fig 5 78 the two databases may be selected on the basis of the directory f1 and the new reference function e0_solarx dat N CONVERT_DB3 Convert bp7 Files in atm_database for Another Solar Irradiance File 2 Standard high resolution atmospheric database 340 2547 nm wv CHRIS Proba database smaller spectral coverage 380 1080 nm High resolution database 1 Yarc_idl atcor atcor_23 atn_database Solar irradiance file fl Ysrczidl atcor atcor_23 atn_database e0_solar_Fonten2011_04nn dat Solar irradiance file f2 Ysrc_idl atcor atcor_23 sun_irradiancese0_solar_kurucz1997_04nm dat High resolution database 2 Ysrc_idl atcor atcor_23 atn_database_kurucz1997 Convert Database 1 irradiance f1 into Database 2 irradiance f2 Lal a QUIT 1 Figure 5 78 Convert monochromanic datab
126. Schematic sketch of solar radiation components in flat terrain L path radiance La reflected radiance L3 adjacency radiation The cy and c are called radiometric calibration coefficients The radiance unit in ATCOR is mWem sr um For instruments with an adjustable gain setting g the corresponding equation is tq DN 2 8 g During the following discussion we will always use eq 2 7 Disregarding the adjacency component we can simplify eq 2 6 L Lath Dreflected z Lath TpE T co c DN 2 9 where 7 p and E are the ground to sensor atmospheric transmittance surface reflectance and global flux on the ground respectively Solving for the surface reflectance we obtain Td co amp DN Lpath 2 10 P TE The factor d takes into account the sun to earth distance d is in astronomical units because the LUT s for path radiance and global flux are calculated for d 1 in ATCOR Equation 2 9 is a key formula to atmospheric correction A number of important conclusions can now be drawn e An accurate radiometric calibration is required i e a knowledge of cg cy in each spectral band e An accurate estimate of the main atmospheric parameters aerosol type visibility or optical thickness and water vapor is necessary because these influence the values of path radiance transmittance and global flux e If the visibility is assumed too low optical thickness too high the path radian
127. TER 6 BATCH PROCESSING REFERENCE 146 smooth smooth the outputs by a lowpass filter of size smooth after derivative filtering adj use only adjacent bands excluding current fro derivatives e at_pushpoli infile outfile gainfile spatial nospectral zero_offset Spectral polishing and post calibration destriping on the basis of pixel wise deviations from local average PARAMETERS infile file of reflectances to be filtered outfile name of output file to be created gainfile optional output file containing gains and offsets will be stored KEYWORDS spatial use the spatial dimensions with a filter size of the keyword for filtering nospectral don t apply spectral interpolation by default spectral interpolation with 3 bands is used to get systematic offsets zero_offset apply linear function through zero no offset e at_prepele infile demfile ofile kernelsize fillzero log slopasp skyview Program to resize and prepare a DEM and all its layers for ATCOR processing PARAMETERS infile ENVI formatted BSQ file to be processed with ATCOR demfile ENVI DEM covering the area of ifile same coordinate system can be different resolution extent ofile name of primary elevation file KEYWORDS kernelsize size of kernel to calculate slope aspect default 5 fillzero fill in zero values at edges of image shrink image slopasp set to calculate slope aspect files skyview set to c
128. Torr 13 3 mbar conditions e The APEX spectrometer has a pressure regulation unit keeping the optical subunit at 200 mbar above ambient flight altitude pressure 35 e Most airborne spectrometers operate under ambient flight altitude pressure The next table shows the default contents of file pressure dat The file is created for each sensor in the sensor specific folder during the first run of the RESLUT resampling module if no pressure dat exists The user should edit the first line of the file if necessary 1013 0 R0 0 lab pressure instrument pressure mbar hPa instrument pressure is relative or absolute R r relative pressure above ambient flight altitude A a absolute pressure Table 2 1 Default file pressure dat to be edited if necessary 2 4 Inflight radiometric calibration Inflight radiometric calibration experiments are performed to check the validity of the laboratory calibration For spaceborne instruments processes like aging of optical components or outgassing during the initial few weeks or months after launch often necessitate an updated calibration This approach is also employed for airborne sensors because the aircraft environment is different from the CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 25 laboratory and this may have an impact on the sensor performance The following presentation only discusses the radiometric calibration and assumes that the spectral calibration does not cha
129. ULES 65 NRGB Geo TIFF Geotiff in 4 band configuration storage order N R G B JPEG2000 Geo Import of JPEG2000 file extension jp2 with embedded georeferencing infor mation in geotiff or gml format ENVI BIP Image Imports and transforms an ENVI image in BIP band interleaved by pixel format to the ATCOR default BSQ band sequential format ENVI BIL Image Imports and transforms an ENVI image in BIL band interleaved by line format to the ATCOR default BSQ band sequential format Erdas Imagine Imports an uncompressed image in ERDAS Imagine format to ENVI format specifically suited for DEM data import AVIRIS Imports the orthorectified AVIRIS imagery and creates an appropriate DEM and scan angle file for atmospheric correction AAA A ATCOR AVIRIS data Import Select Orthorectified Input File Names Ydata aviris AVIRIS_illinois_2011 F110809t01p00r12rdn_c_sc l_ort_img Define Output Elevation File base Ydata aviris AVIRIS_illinois_2011 output av_ele bsq Name of Output Cube Ydata aviris AWIRIS_illinois_2011 outputimg bsq Help Reload Import Done Figure 5 7 Import AVIRIS imagery from JPL standard format 5 1 6 Export Transformation and export of standard ENVI format outputs to ENVI BIP Image Transforms and Exports an ENVI BSQ band sequential image to BIP for mat ENVI BIL Image Transforms and Exports an ENVI BSQ band sequential image to BIL for mat Multiband TIFF creates an universal multiband TIFF
130. VeierainlDs y 10 19 CHAPTER 10 THEORETICAL BACKGROUND 195 10 1 2 Illumination based shadow detection and correction For high resolution imagery the correction of cast shadows and illumination on the basis of a surface model does not lead to useful results as the surface representation with respect to the radiometry is never accurate enough Severe over and under correction artifacts are observed due to these inaccuracies in the resulting images The detection and correction of cast shadows has been widely studied specifically for space borne high resolution instruments 3 87 A new method for cast shadow detection has been implemented for the ATCOR case It produces a continuous shadow field and relies on the fact that all areas in cast shadows are illuminated by diffuse irradiance only The diffuse illumination is caused by scattering and thus exhibits very specific spectral characteristics if compared to the direct irradiance Specifically the signal in the blue spectral band is significantly higher in cast shadow areas than in directly illuminated areas For the shadow quantification the brightness in the NIR spectral band is first calculated using the solar illumination Secondly two blue indices have been defined as the band ratios green blue and red blue respectively These three measures are combined such that a value equivalent to the illumination between 0 and 1 is created 0 being a cast shadow area The shadow fraction paramet
131. _isac_emiss bsq emissivity cube for the ISAC algorithm e image_at_sensor_channel_tmaz bsq map of channel numbers with maximum at sensor tem perature e image_at_surface_channel_tmaz bsq map of channel numbers with maximum surface tem perature e image_at_sensor_tbb bsq at sensor brightness temperature cube e image_at_surface_tbb bsq at surface brightness temperature cube The last channel of image_atm bsq contains the surface temperature map evaluated with the ap propriate emissivity the preceding thermal channels in this file contain the surface radiance In case of the ISAC algorithm an additional file image_isac_lpath_trans dat contains the spectral path radiance and transmittance estimates for the scene Fig 4 14 shows an example of these spectra derived from a SEBASS scene Fig 4 15 presents the at sensor at surface radiance and brightness temperatures The at sensor products clearly show the atmospheric absorption features which are removed in the at surface quantities apart from small residual effects The bottom graphic presents the corresponding surface emissivity spectrum CHAPTER 4 WORKFLOW 53 unscaled ISAC path radiance unscaled ISAC Transmittance transmittance 7 8 9 10 11 12 13 14 7 8 9 10 11 12 13 14 Wavelength ym Wavelength gm Figure 4 14 Path radiance and transmittace of a SEBASS scene derived from the ISAC method Tob c black at sensor L grey at surface L black at
132. a atm represents a file with flight altitude 3000 m above sea level water vapor column 0 4 cm and the rural aerosol Each atm file contains the look up tables for the visibility range 5 120 km solar zenith angles 0 70 and ground elevations 0 2500 m increment 500 m If the flight altitude is lower than 2500 m the maximum ground elevation is set to 100 m below flight altitude In the solar spectral region the database was compiled for a nadir view However the atmospheric transmittance will be calculated in ATCOR depending on the actual scan angle for each image pixel employing the nadir value For the path radiance the nadir value is not sufficient to predict the off nadir values Therefore the scan angle dependence of the path radiance was approximated by 2nd order polynomials obtained by a least squares fit The polynomial coefficients depend on the aerosol type view and illumination geometry and wavelength In general they are different for the left and right part of a scanline The extension of the files for the thermal spectral region is tem Since the aerosol type is of negligible influence in the thermal region these files do not include an aerosol type identifier in CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 168 their name They were compiled for the rural aerosol So the file name corresponding to the 3000 m altitude and the 0 4 cm water vapor column is called h03000_wv04 tem The th
133. abase The scan angle dependence of the atmospheric correction functions within a scene is neglected here The airborne version is called ATCOR 4 to indicate the four geometric DOF s x y z and scan angle 65 It includes the scan angle dependence of the atmospheric correction functions a necessary feature because most airborne sensors have a large FOV up to 60 90 While satellite sensors always operate outside the atmosphere airborne instruments can operate in altitudes of a few hundred meters up to 20 km So the atmospheric database has to cover a range of altitudes Since there is no standard set of airborne instruments and the spectral radiometric performance might change from year to year due to sensor hardware modifications a monochromatic atmospheric database was compiled based on the MODTRAN 5 radiative transfer code This database has to be resampled for each user defined sensor Organization of the manual Chapters 2 and 3 contain a short description of the basic concepts of atmospheric correction which will be useful for newcomers Chapter 2 discusses the solar spectral region while chapter 3 treats the thermal region Chapter 4 presents the workflow in ATCOR and chapter 5 contains a detailed description of all graphical user interface panels It is followed by chapters on batch processing value added products available with ATCOR sensor simulation internal reference and finally a comprehensive chapter on the the
134. adiometric re calibration 5 8 5 Calibration Coefficients with Regression This routine employs the rdn files obtained during the single target calibration the c1 option of ATCOR s calibration module to create a calibration file by rlinear regression Figure 5 77 CAL_REGRESS radiometric calibration with more than one target So for n gt 2 the single target calibration is to be performed n times and the resulting rdn files radiance versus digital number are offered as input to the cal_regress program CHAPTER 5 DESCRIPTION OF MODULES 130 Inputs Number of calibration targets A maximum of 9 calibration targets may be selected The files rdn should having been calculated beforehand and the need to be calculated consecutively e g calibl rdn calib2 rdn First rdn file Name of the first rdn file of the series to be used for regression Output Name Name of the calibration output to be created Output The output of this program is an ASCII file name cal with three columns center wavelength co c1 where name is specified by the user Note If several calibration targets are employed care should be taken to select targets without spectral intersections since calibration values at intersection bands are not reliable If intersections of spectra cannot be avoided a larger number of spectra should be used if possible to increase the reliability of the calibration CHAPTE
135. ain processing steps during atmospheric correction the scene pixels then the threshold T1 is increased until threshold T2 0 12 is reached If not enough reference pixels are found then a constant VIS 23 km is used otherwise the visibility for each reference pixel is determined as the intersection of modeled and measured radiance in the red channel Then the NIR channel is checked concerning negative reflectance pixels mainly water shadow If the percentage of negative reflectance pixels is higher tahn 1 of the scene pixels then the visibility is iteratively increased up to 80 km Finally the visibility index and AOT 550nm are calculated and the nn reference pixels are assigned the average visibilty or optionally a spatial triangular interpolation can be performed If the aerosol type rural urban maritime desert is not fixed by the user the flow chart is executed for these four types and the type closest to the scene estimated type is used see chapter 10 4 2 for more details CHAPTER 10 THEORETICAL BACKGROUND 188 Start threshold T1 0 05 Mask DDV reference pixels 0 01 lt p ref SWIR lt T1 Number of ref pixels no KK A lt 1 of scene pixels Y yes Increasethreshold T1 Va oe gt eee lt Dist no Vv gt Constant scene VIS 23 km Apply DDV p red 0 5 p 2 2 um Calculate VIS from intersection of modeled L red with measured L red Number of negative
136. ained below The sky view factor is normalized to 1 for a flat terrain The reflectance is calculated eons The first step neglects the adjacency effect and starts with 5 0 1 65 a fixed terrain reflectance of Dierrain T d co c1DN 2 y Lolz Ov 9 To z Ov b a y Ests z cosB a y Eje y 2 EP z pr PprdinVierrain 2 Y plz y 10 15 The terms are defined as X y horizontal coordinates corresponding to the georeferenced pixel positions Z vertical coordinate containing the elevation information from the DEM DN x y digital number of georeferenced pixel Lolz Ov Q path radiance dependent on elevation and viewing geometry Tolz Oy ground to sensor view angle transmittance direct plus diffuse components me Sun to ground beam direct transmittance B x y angle between the solar ray and the surface normal illumination angle b x y binary factor b 1 if pixel receives direct solar beam otherwise b 0 Es extraterrestrial solar irradiance earth sun distance d 1 astronomical unit Ej x y 2 diffuse solar flux on an inclined plane see equation 10 18 E z global flux direct plus diffuse solar flux on a horizontal surf at elevation z E 2 radiation incident upon adjacent slopes ph a 0 1 initial value of average terrain reflectance Domain Cs y locally varying average terrain reflectance calculated iteratively i 1 2 3 Vieron 1D Y terrain view factor range 0 1
137. al irradiance from extreme ultraviolett to far infrared J Geophys Res Vol 116 D20108 31pp 2011 Fraser R S Bahethi O P and Al Abbas A H The effect of the atmosphere on classifi cation of satellite observations to identify surface features Remote Sens Environm Vol 6 229 249 1977 Gao B C Kaufman Y J Han W and Wiscombe W J Correction of thin cirrus path radiances in the 0 4 1 9 um spectral region using the sensitive 1 375 wm cirrus detecting channel J Geophys Res Vol 103 D24 32 169 32 176 1998 Gao B C Yang P Han W Li R R and Wiscombe W J An algorithm using visible and 1 38 um channels to retrieve cirrus cloud reflectances from aircraft and satellite data IEEE Trans Geosci Remote Sens Vol 40 1659 1668 2002 Gao B C Kaufman Y J Tanre D and Li R R Distinguishing tropospheric aerosols from thin cirrus clouds for improved aerosol retrievals using the ratio of 1 38 um and 1 24 um channels Geophys Res Letters Vol 29 No 18 1890 36 1 to 36 4 2002 Gao B C Meyer K and Yang P A new concept on remote sensing of cirrus optical depth and effective ice particle size using strong water vapor absorption channels near 1 38 and 1 88 um IEEE Trans Geosci Remote Sens Vol 42 1891 1899 2004 References 246 27 28 29 30 o 35 36 37 38 39 40 41 Gil
138. al location If in doubt the rural continental aerosol is generally a good choice The aerosol type also determines the wavelength behavior of the path radiance Of course nature can produce any transitions or mixtures of these basic four types However ATCOR is able to adapt the wavelength course of the path radiance to the current situation provided spectral bands exist in the blue to red region and the scene contains reference areas of known reflectance behavior The interested reader may read chapter 10 4 2 for details Visibility estimation Two options are available in ATCOR e An interactive estimation in the SPECTRA module compare chapter 5 The spectra of different targets in the scene can be displayed as a function of visibility A comparison with reference spectra from libraries determines the visibility In addition dark targets like vegetation in the blue to red spectrum or water in the red to NIR can be used to estimate the visibility CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 21 e An automatic calculation of the visibility can be performed if the scene contains dark reference pixels The interested reader is referred to chapter 10 4 2 for details Water vapor column The water vapor content can be automatically computed if the sensor has spectral bands in water vapor regions e g 920 960 nm The approach is based on the differential absorption method and employs bands in absorption regions and window regions
139. alculate skyview factor file e at_rhoapp infile calfile eOsolar outfile scale zen date Apparent reflectance calculation PARAMETERS calfile ATCOR calibration file to be used for conversion of the cube eOsolar File containing the solar irradiance for the sensor atcor file outfile name of output KEYWORDS scale scale for processing same convention as for ATCOR scale 1 0 is floating point output zen solar zenith angle default 0 degrees in degrees date date as two elemt array day month CHAPTER 6 BATCH PROCESSING REFERENCE 147 e at_shadowdetect infile calfile eOsolar outfile solangles slopefile skyview min all_layers pixfilter range Image based shadow detection classification PARAMETERS infile file to be analysed calfile calibration file for current input file eOsolar solar irradiance file for current input outfile output file of the processing KEYWORDS castshadow if keyword is set an existing cast shadow file is provided in order to find the shadows through this keyword slopefile use slope and aspect and include them in the illumination solangles solar angles zenith azimuth only required for slopefile option skyview calculate additional skyview layer based on illumination requires keyword solangles and slopefile being set range range of index default 0 5 1 0 min minimum value of output default 0 pixfilter set to filter single
140. alculation with vis 80 km do not differ much from the results with VIS 120 km So the iteration capability is most important for low visibility start values visibility km vis increment km 5 3 8 3 11 3 14 3 17 3 20 3 23 3 26 4 30 5 35 5 40 5 50 10 60 10 7O 10 80 20 100 20 120 max VIS 120 km Table 10 3 Visibility iterations on negative reflectance pixels red NIR bands 10 4 2 Aerosol retrieval and visibility map If a sensor has the appropriate spectral bands the aerosol type and visibility or optical thickness of the atmosphere can be derived provided the scene contains reference areas of known reflectance CHAPTER 10 THEORETICAL BACKGROUND 213 behavior Kaufman and Sendra 1988 Kaufman et al 1997 The minimum requirements are spectral bands in the red and near IR If the scene contains dense dark vegetation coniferous type the reflectance values in the red band can be obtained from a correlation with the SWIR band reflectance as detailed below The visibility of each reference pixel can then be calculated in the red band as the intersection of the modeled at sensor radiance curve with the measured radiance see figure 10 11 The measured radiance for a reference pixel of digital number DN is L cy amp DN which is a constant value indicated by the dashed line in figure 10 11 The curve indicates the modeled radiance It employs the reflectance of the reference surface e 8 Pref 0 02
141. ald 1_ge05_atm bsq OUTPUT IMAGE nadir normalized eubes hynap vord_1 Nordenuald 1_geo5_atn nadir boq HELP w Input Image WITHOUT sca scan angle file Input Image WITH sca file Sensor total field of view FOV degree 60 0 2 Global normalization surface cover independent art regir rormedization classes bright veqet madly Y Cover Band selection w hot spot geometry across track angular sampling interval 1 degree no hot spot geometry across track angular sampling interval 3 degree RUN we Check data range degree 100 background 9100 Ts nadir angular region 0 3 degr included in image QUIT l Figure 5 54 Nadir normalization 5 5 3 BRDF Model Analysis This tool is to analyze the model parameters which have been derived by best fit approach from the imagery An example is shown in Fig 5 55 The top section shows the meta parameters of the currently selected model file i e the output from the model calibration procedure CHAPTER 5 DESCRIPTION OF MODULES 107 leoo X BRDF Model Analysis Select BRIF Model File votumes trunk data AISA arch subset brefcar_model_2253r sav Input File Wolumes Trunk data AISA arch subset 0526_0824_geo_atm bsq Scan Angle File Volumes Trunk data AISA arch subset 0526_0824_geo_sca bsq Flight Heading 76 5000 Solar Azimuth 120 600 Solar Zenith 39 3000 Options Ross Maignan Nosmooth Raws Water 2 2
142. alibration with two targets In case of two targets a bright and a dark one should be selected to get a reliable calibration Using the indices 1 and 2 for the two targets we have to solve the equations L co c DN L co c DNX 2 23 This can be performed with the co amp c option of ATCOR s calibration module see chapter 5 The result is Li L2 DNF DNS 2 24 Co Li C DN 2 25 Equation 2 24 shows that DNY must be different from DN3 to get a valid solution i e the two targets must have different surface reflectances in each band If the denominator of eq 2 24 is zero ATCOR will put in a 1 and continue In that case the calibration is not valid for this band The requirement of a dark and a bright target in all channels cannot always be met Calibration with n gt 2 targets In cases where n gt 2 targets are available the calibration coefficients can be calculated with a least squares fit applied to a linear regression equation see figure 2 5 This is done by the cal_regress program of ATCOR It employs the rdn files obtained during the single target calibration the CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 26 cl option of ATCOR s calibration module See section 5 8 5 for details about how to use this routine Note If several calibration targets are employed care should be taken to select targets without spectral intersections since calibration
143. and also dis plays which license key is currently active Chapter 6 Batch Processing Reference For most ATCOR modules a convenient graphical user interface is available but batch jobs can also be submitted A detailed discussion of the interactive panel driven modules is given in chapter 5 Running ATCOR in batch mode can be done in two ways either from the operating system console directly or from within IDL The latter provides a higher flexibility whereas the first is well suited for integration of ATCOR in a processing environment 6 1 Starting ATCOR from console ATCOR can be started directly from a console or from a different processing environment using the standard call to IDL run time environment This call will show a splash screen when starting IDL unless an IDL RT or a full IDL license is installed on the processing computer RT licenses can be acquired from exelisvis com at a significantly reduced price than full IDL licenses This makes this method useful for operational processing with ATCOR The call in a windows environment is idlpath idlrt exe rt atcorpath atcor_4 bin atcor4 sav args input R FIE output logfile elefile factor where idlpath is the path to the idl installation typically something like id184 bin bin x86_64 On a unix macOSX system the call syntax is as follows idlpath idl rt bin atcor4 sav args input R F E output logfile elefile factor The idlpath in this
144. and b 1 all pixels with DN 255 will be marked as saturated in the _out_hcw bsq file color coding red Setting b 0 9 implies pixels with DN gt 230 will be considered as saturated or in the non linear radiometric range close to saturation This factor is only used for 8 and 16 bit signed or unsigned data not for float or 32 bit data Note on the cloud mask The criterion al or a2 is also coupled with the conditions p NIR p red lt 2 and p NIR p SWIR1 gt 1 and NDSI lt 0 7 where NDSI is the normalized difference snow index A quality or probability mask for clouds is generated with the above three conditions and different apparent reflectance thresholds Te in the blue green spectral region For Te 15 we define a low probability cloud Te 25 a medium probability and T 35 a high probability cloud The result is put into a cloud map file named image_quality bsq if the input scene is named image bsq The cloud mask in the image_out_hcw bsq file is based on the user defined threshold Te in the preference parameter file and the above three conditions Note on the water mask The NIR band surface reflectance threshold is only used for sensors operating in altitudes lt 10 km or typically below 4 km For high altitude imagery e g 20 km AVIRIS data better water masks are obtained with the criterion that the apparent reflectance p spectrum must have a negative gradient for bands in the visible
145. and used as the new reference wavelength nominal position ENVI header the nominal position as provided in the ENVI header file is taken as the reference Output A cube containing the spectrally interpolated image data is generated and the ENVI header is updated for options 1 and 2 5 6 8 Cast Shadow Border Removal This routine is used after cast shadow correction in case the borders of the shadows show dark or bright artifacts It corrects the imagery by adjusting the brightness to the direct neighbor pixels Using the illumination file all pixels at the cast shadow borders are first identified and a buffer along the borders is calculated using the border width Secondly a brightness correction factor is calculated for each pixel in the border lines using the relative brightness in comparison to its neighbours The pixels are then corrected using this relative difference in a multiplicative way The inputs are as follows see Fig 5 66 Inputs Input File A hyperspectral image cube usually the output of atmospheric correction _atm bsq Tllumination Cast Shadow File illumination file containing a shadow mask which had been applied to the image during atmospheric correction Options Two options for alternate processing are available try those in case the artifacts are not well removed adjust spectral mean An average additional correction factor is applied to the border pixels in order to correct for spectral variation of the bright
146. ans the direct solar flux Eg term in eq 10 107 has to be multiplied with z y m d co c i DNi a y Lpa i Lara x y Espa In equations 10 107 10 113 the aerosol optical thickness or visibility required for the atmospheric terms path radiance transmittance direct and diffuse flux can be derived from the image provided the necessary bands in the visible and shortwave infrared region exist and the scene contains dark reference areas 41 Otherwise the user has to specify an estimated visibility The second important atmospheric parameter is the water vapour column For instruments with bands in the atmospheric water vapour regions this information can be derived from the image data 81 otherwise an estimate has to be provided by the user In summary three channels around 0 85 1 6 and 2 2 wm are used to define a matched filter vector with three elements per pixel For each image pixel the surface reflectance in these three channels and the scene average reflectance of these channels are calculated to obtain the unscaled shadow function and finally the scaled shadow function The same shadow function is employed to de shadow the imagery not only in the initial three channels but for all channels of the sensor eq 10 113 pi x y 10 113 CHAPTER 10 THEORETICAL BACKGROUND 229 Details of the method One of the most important parameters is the available number of spectral channels during the
147. are placed in this sub directory After execution of these stpes the new sensor will be automatically detected when ATCOR is started Details about the sensor definition files are explained in chapter 4 6 Template files of several sensors are included in the distribution After sensor definition the module RESLUT see section 5 2 5 is to be run New Sensor Gaussian Filter Files G band01 rsp band02 rsp band96 rsp New Sensor RESLUT Atm LUTs atm ATCOR G Figure 5 13 Sensor definition files the three files on the left have to be provided created by the user a 5 2 1 Define Sensor Parameters This panel is the first step if a new sensor is to be defined The panel as displayed in Fig 5 14 allows the below options CHAPTER 5 DESCRIPTION OF MODULES 70 000 x ATCOR Sensor Definition Selected Sensor l atcor atcor_4 sensor aviris98_demo sensor_aviris98_demo dat Sensor Type 2 Standard w Smile Sensor w Thermal Sensor Sensor Total FOY deg 1 2000 Number of Across Track Pixels 14 First last Reflective Band fa to f4 First last Mid IR Band p to lb First last Thermal IR Band p to b Applied scaling factor from ml cm2 sr micron 500 000 Calibration Pressure hPa 013 000 Instrument Pressure w Absolute Relative 0 000000 Sensor data loaded from src_idl atcor atcor_4 sensor aviris98_demo sensor_aviris98_demo da
148. as set to zero co 0 and c according to equation 2 22 Remark a bright target should be used here because for a dark target any error in the ground reflectance data will have a large impact on the accuracy of c1 ee of Calculation ci ywocd amp cl Number of calibration targets 1 Target 1 tox 5 Ground reflectance f rahletune Target 2 box 5 tread rat Results of calibration File data atcor2 2 dexo_data tn_flat sandi cal Bone Definition of target center coordinates at 2 Click targets in zoom window w Specify x y coordinates Target 1 mouse button 1 left Target 2 mouse button 2 center Target 1 t rolun E hnos fis Yarget ot pajan fi fiw f Colas el lbr tion oaf ficiorta Figure 5 37 Radiometric calibration target specification panel At the top line of the menu of figure 5 37 the mode of calibration is specified one or two targets can be employed by selecting the button cl or c0 amp cl respectively If a calibration is intended for n gt 2 targets each target has to be specified separately in the c1 mode which creates a file target i rdn i 1 2 n with the name target_i specified by the user These files contain the radiance and corresponding digital number spectrum as a 2 column ASCII table and they are employed in the batch module cal_regress see chapter 5 to calculate a least squares regression for
149. ase to new solar reference function 5 8 7 Convert atm for another Irradiance Spectrum The conversion as described in module 5 8 6 can be applied to a sensor specific atmospheric library of a self defined sensor using this function In the panel as of Fig 5 79 the sensor has first to be entered and the new solar function e0_solarx dat is to be selected before the conversion may be applied CHAPTER 5 DESCRIPTION OF MODULES 132 Figure 5 79 Convert atmlib to new solar reference function CHAPTER 5 DESCRIPTION OF MODULES 133 5 8 8 Thermal Spectral Calibration Atm Features This program is intended for hyperspectral thermal sensors and employs atmospheric absorption features to detect and remove possible wavelength calibration errors see chapter 3 It is the counterpart to the spectral calibration in the solar reflective region At least 20 channels in the 8 5 13 5 um region are required Input to the thermal spectral calibration module is the original non geocoded scene and its inn file from which the necessary parameters sensor name atmospheric LUTs etc are taken Then 10 spectra are evaluated from 10 image lines located at the image center nadir and the corresponding wavelength shifts are calculated for the specified surface temperature range In addition an averaging pixel box size can be specified to reduce noise Figure 5 80 presents the interactive GUI Output is a file with the new shifted channel center wavele
150. ations but the trends spectral shapes should be similar The bottom CHAPTER 5 DESCRIPTION OF MODULES 92 graphics shows an asphalt spectrum taken from the aircraft runway in the lower part of the image The parameter Visibility can be set in the corresponding widget near the center of the panel and its influence on the retrieved spectrum will be shown when placing the target box in the scene For hyperspectral sensors the water vapor column corresponding to the last spectrum is given in the message box upper left of panel In addition there is the choice of selecting the global neigborhood average over whole image or the local neigborhood adjacency range as specified on the main panel see figure 5 32 for the correction of the adjacency effect The global option is faster because it has to be calculated only once per channel The local option is more accurate and slower because it is always updated but depending on the scene content the difference between both options may be small The button Save last spectrum upper right corner of figure 5 36 can be used to save the selected surface reflectance spectrum A sequence of target DN spectra can also be generated here that is required as input to the spectral calibration module see chapters 2 2 5 8 4 Up to 9 targets can be defined to be used in the spectral calibration They have to be labeled consecutively e g target1 target2 etc These output fil
151. atmospheric and topographic correction The algorithm is briefly outlined here more details can be found in the original papers Although it was originally proposed for satellite imagery it can also be applied to airborne scenes The first step is the orthorectification of the scene using a digital elevation model DEM Then the slope and aspect maps are calculated The next step is the calculation of the sky view factor see chapter 10 1 1 The original paper uses the simple equation based solely on the slope angle but with ATCOR a more accurate calculation based on a ray tracing can also be used in case of a steep terrain Then the following quantities are computed keeping the original notation of Kobayashi in most cases m 26 ho 10 24 2a Here 0 is the solar zenith angle in radian IF s denotes the slope map in radian then the simple version of the skyview is obtained with h 1 s m 10 25 The cosine of the local solar zenith illumination angle is given in eq 10 17 Then the surface radiance for each channel Ls is calculated by subtracting the path radiance Lp from the at sensor radiance L L x y L x y Lplz y 2 10 26 CHAPTER 10 THEORETICAL BACKGROUND 198 In the ATCOR version of the IRC algorithm the path radiance varies spatially particularly due to the DEM height variation while a constant value per channel is used in the original IRC paper Then a regression analysis per channel of Ls versu
152. ay also vary in space and time Since ozone usually has only a small influence ATCOR employs a fixed value of 331 DU Dobson units corresponding to the former unit 0 331 atm cm for a ground at sea level representing average conditions The three most important atmospheric parameters that vary in space and time are the aerosol type the visibility or optical thickness and the water vapor We will mainly work with the term visibility or meteorological range because the radiative transfer calculations were performed with the MODTRAN code Berk et al 1998 2008 and visibility is an intuitive input parameter in MODTRAN although the aerosol optical thickness can be used as well ATCOR employs a database of LUTs calculated with MODTRAN 5 Aerosol type The aerosol type includes the absorption and scattering properties of the particles and the wave length dependence of the optical properties ATCOR supports four basic aerosol types rural urban maritime and desert The aerosol type can be calculated from the image data provided that the scene contains vegetated areas Alternatively the user can make a decision usually based on the geographic location As an example in areas close to the sea the maritime aerosol would be a logical choice if the wind was coming from the sea If the wind direction was toward the sea and the air mass is of continental origin the rural urban or desert aerosol would make sense depending on the geographic
153. be obtained as _ n d co i c i DNi a y Lpi a 10 107 pix y TlEdira Edif Here d is the Earth Sun distance at the image acquisition time in astronomical units co and c are the radiometric calibration coefficients offset and slope to convert the digital number into the corresponding at sensor radiance L i e L co c amp DN and i is the channel index CHAPTER 10 THEORETICAL BACKGROUND 226 The proposed de shadowing algorithm consists of a sequence of eight processing steps as sketched in Fig 10 21 It starts with the atmospheric correction The next step is the masking of water bodies and cloud areas with simple spectral criteria as detailed below Water pixels have to be excluded as far as possible to avoid their assignment as shadow pixels Step 3 calculates the covariance matrix C p where p is the surface reflectance vector comprising only the non water and non cloud pixels For each pixel this vector holds the reflectance values in the 3 selected channels around 0 85 1 6 2 2 um The matched filter is a vector tuned to a certain target reflectance spectrum p to be detected 2 Cp P pe P O p P Vint 10 108 surface reflectance Ad exclude cloud amp water matched filter vector unscaled shadow function gt scaled shadow function y threshold P core shadow areas gt expand shadow mask
154. ce becomes high and this may cause a physically unreasonable negative surface reflectance Therefore dark surfaces of low reflectance and correspondingly low radiance cy cj DN are especially sensitive in this respect They can be used to estimate the visibility or at least a lower CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 20 bound If the reflectance of dark areas is known the visibility can actually be calculated The interested reader may move to chapter 10 4 2 but this is not necessary to understand the remaining part of the chapter e If the main atmospheric parameters aerosol type or scattering behavior visibility or optical thickness and water vapor column and the reflectance of two reference surfaces are measured the quantities Lpatn T p and Ey are known So an inflight calibration can be performed to determine or update the knowledge of the two unknown calibration coefficients co k c1 k for each spectral band k see section 2 4 Selection of atmospheric parameters The optical properties of some air constituents are accurately known e g the molecular or Rayleigh scattering caused by nitrogen and oxygen molecules Since the mixing ratio of nitrogen and oxygen is constant the contribution can be calculated as soon as the pressure level or ground elevation is specified Other constituents vary slowly in time e g the CO2 concentration ATCOR calculations were performed for a concentration of 400 ppmv Ozone m
155. ce thresholds apply as defined in the preference parameter file chapter 9 4 These surface reflectance thresholds can be defined as positive values as the gradient criterion in not valid anyway or as nega tive An increase decrease of the thresholds will increase decrease the number of water pixels However if an external water map exists either from file image_hcw bsq or image_water_map bsq if image bsq is the file name of the scene and this file indicates more than 15 of water pixels then this information is regarded as reliable and this water map is excluded from de shadowing CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 185 e Rugged terrain the slope and illumination maps show strong horizontal and vertical stripes Strong artifacts in the DEM files will immediately be visible in the atmospherically topo graphically corrected surface reflectance image This problem frequently occurs for resampled DEMs e g the original DEM resolution is 30 m which is resampled to a 5 m pixel size Ar tifacts will be enhanced especially if the stepsize of the original DEM height resolution is coded as integer Float data would have smoother transitions A simple way to get better results is the use of a larger kernel size for the slope aspect calculation e g kernel 5 or kernel 7 instead of the default kernel 3 pixels but this approach causes a reduction of the high frequency spatial information Attention in addition t
156. commended so a surface reflectance value of say 20 56 is coded as 2056 and is stored as a 2 byte integer which means the file size is only half of the float file size with no significant loss of information If the input file name is image bsq then the default output file name for the atmospherically corrected image is image_atm bsq The user may modify the output name but it is recommended to keep the _atm bsq qualifier to facilitate the use of subsequent programs Then the flight and solar geometry have to be specified as well as the sensor and the calibration file The atmospheric file contains the look up table LUT results of the radiative transfer calculations separately for the solar and thermal region For a new user specified sensor these LUTs have to be calculated once prior to the first call of ATCOR This is done with the module RESLUT available under the Sensor button of Fig 4 1 It is recommended to check the quality of the atmospheric correction before processing the image data For that purpose the SPECTRA module should be used where the surface reflectance of small user defined boxes can be evaluated and compared with library spectra compare chapter 5 5 2 In case of calibration problems the spectral calibration module available from the Tools button of Fig 4 1 and the radiometric inflight calibration may be employed before finally processing the image data The AEROSOL TYPE button pr
157. convention the last 2 digits indicate the year of calibration e g hymap04 means the spectral and radiometric calibration of HyMap conducted in 2004 The atm_database contains the files of the monochromatic ATCOR database see chapter 9 1 The atm_lib contains the results of the atmospheric database after resampling with the sensor specific spectral response curves The spec_lib is an optional subdirectory where the user can put field measurements of surface reflectance spectra resampled for the appropriate sensor This is useful for inflight calibration or comparison of scene spectra with ground spectra Finally the demo_data contains some demo imagery to be able to run ATCOR 4 immediately 4 5 Convention for file names Although file names are arbitrary it is useful to agree on some conventions to facilitate the search of files especially concerning the extensions of file names Input images to ATCOR must have the band sequential format BSQ therefore it is recommended to employ the bsq as an extension e g imagel bsq Then in this example the default output file name of ATCOR will be magel_atm bsq and a log report of the processing is available in imagel1_atm log Once an image is processed with ATCOR all input parameters are saved in a file inn that is automatically loaded should the image be processed again The recommended extension for the radiometric calibration files is cal Other extens
158. conversion for all height levels requires about 3 minutes The user can also provide additional solar irradiance files to the sun_irradiance folder provided the spectral range increment and irradiance unit agree with the template spectra Attention ATCOR will always work with files in the active folder atm_database therefore the old atm_database has to be renamed or deleted and the folder with the new database has to be renamed as atm_database before applying the sensor specific resampling program RESLUT Since each atm_database folder contains its corresponding solar irradiance spectrum a unique identification is always possible Previously generated channel resampled atm files do not have a reference to their solar irradiance file but they are based on the e0_solar_kurucz1997_06nm dat irradiance Beginning with the 2011 release the directory of the atm files contains an ASCII file named irradiance_source txt identyfying the underlying solar irradiance file High resolution database 1 Vexport data data atcor43 atm_database Solar irradiance file ls Yexport data data7 atcor43 atm database e0_solar_kurucz2005_04nn dat Solar irradiance file f2 Yexport data data atcor43 sun_irradiance e0_solar_thuZ003_RSL_kuZ005_04nn dat High resolution database 2 Yexport datadata7 atcor43 atm_databasethu2003_RS Convert Database 1 irradiance f1 into Da
159. cor4 sensor casi04 sub directory twice separated by a comma and space line 5 1 0 gain setting Any positive value is accepted this gain setting g is used to replace the c in the corresponding cal file with c g where g is the same for all channels line 6 calfile calibration file name line 7 fscapix scan angle file _sca empty line if image is not geocoded line 8 0 9500 0 iemiss dem_unit surface emissivity DEM height unit iemiss surface emissivity option or value disregarded if no thermal band exists iemiss 0 invokes e 0 98 to be consistent with the definition of earlier ATCOR versions Since iemiss 1 is reserved for the cover dependent emissivity setting below e 1 0 has to be ap proximated as iemiss 0 999 or iemiss 0 9999 In case of multiple thermal bands this e holds for the thermal band itemp_band employed for the surface temperature evaluation see chapter 9 2 iemiss 1 fixed values of surface emissivity 0 98 water 0 97 vegetation 0 96 soil iemiss 2 surface emissivity map calculated with SPECL compare chapters 10 1 5 2 5 iemiss 3 NEM or ANEM method requires multiple thermal bands see chapter 10 1 5 iemiss 4 ISAC method requires multiple thermal bands see chapter 10 1 5 iemiss 5 both NEM and ISAC but ISAC is currently only supported for flat terrain imagery dem_unit 0 m 1 dm 2 cm DEM height unit line 9 fele
160. counted as haze and the results of the haze removal are bad because the haze algorithm is applied to clouds e The water mask is not appropiate This might lead to problems for the haze removal as well as the cloud building shadow removal i e water is erroneously counted as land and included in the land mask Check the two water reflectance thresholds NIR SWIR region in the preference_parameters dat and modify them appropriately Enable the output of a haze cloud water map compare Fig 4 13 to check the water mask Read chapter 4 8 There are two possibilities to define the water mask If the average ground elevation of the scene is below 1 2 km the default option for cal culating the water mask is the negative gradient criterion for the apparent reflectance in the VNIR region see eq 9 3 This yields the best water map in most cases However in some cases urban areas with shadows cast by buildings the NIR SWIR1 water surface reflectance thresholds see chapter 9 4 yield better results If the average scene eleva tion is below 1 2 km one has to define negative reflectance values to both NIR SWIR1 thresholds to overrule the default negative gradient criterion In the latter case an in crease decrease of the absolute value of the thresholds will increase decrease the number of water pixels If the average scene elevation is above 1 2 km the gradient criterion cannot be ap plied and the NIR SWIR1 water surface reflectan
161. ct1 imagel bsq The file should have the band sequential BSQ file structure A corresponding inn file e g data2 project1 imagel ini must be available that contains all processing parameters This file will be generated during the interactive session It may be also be created by the user e g employing the program write_atcor4_inn file pro that is available on request The default output file name without the output keyword specification is the input name with an _atm bsq appended e g data2 project1 imagel _atm bsq The keyword output can be used to specify the full output name or only the output path CHAPTER 6 BATCH PROCESSING REFERENCE 143 the latter option is recommended In that case all output files are written to the specified output directory and the reflectance output file name is the name of the input file with _atm bsq appended Example output data4 project1 then the output reflectance file will be data4 project1 imagel_atm bsq The corresponding tile program atcor4f_tile in this example is called to split the image into 3 sub images in x direction and 2 in y direction compare chapter 6 2 The optional keyword vis can be used to overwrite the visibility value in the inn file For a constant visibility per scene npref 0 in the inn file the input vis value is the start value that will be iterated as described in chapter 10 4 1 In case
162. ctance spectrum from a field spectrometer has to be resampled with the channel filter curves of the selected sensor The field spectrometer file format should be converted into a simple ASCII file containing the wavelength nm or um in the first column and reflectance in the second column The resampling can then be done with the sequence Filter Resample a Spectrum from the ATCOR main panel The result is an ASCII file with 2 columns the first contains the channel center wavelength the nm and um unit is allowed the second contains the resampled reflectance value either in the 0 1 or 0 100 range CHAPTER 5 DESCRIPTION OF MODULES 93 If targetl is the name of the target ATCOR provides three ASCII files with information on target background properties and the derived calibration file These are the output of the c1 option of ATCOR s calibration module e File targetl adj contains the original target DN the adjacency corrected DNY and the ground reflectance data for each band e File target1 rdn radiance versus digital number contains the band center wavelength target radiance L and corresponding digital number This file can be used as input to a regression program cal regress that allows the calculation of the calibration coefficients with a least squares fit in case of multiple targets more than two e File targetl cal contains three columns band center wavelength offset or bi
163. cted Thus the GUI panel creates an inn file containing all input parameters The batch mode can be started after quitting the interactive session using the same IDL window It can also be started in a new IDL session after typing atcor4 on the IDL command line Then continue with gt atcor4f_batch input datal examples example_image bsq case of flat terrain or gt atcor4r_batch input datal examples example_image bsq case of rugged terrain At this stage all required input parameters are already available in the inn file in this specific case example_image inn The submitted job is a quasi batch job the corresponding IDL window is used for error and status messages and it may not be closed during the run time of the job A log file is created during processing e g example_image_atm log which contains information about the job status It contains three message levels I Info W Warning E Error followed by a two digit number between 0 and 99 and a space e g W19 followed by the appropriate information These three message levels can easily be parsed by a separate user program if desired Other information in the log file is marked with the hashmark symbol in the first column In the tiling mode the user has to specify the number of tiles in x column direction ntx and in y line direction nty e g gt atcor4f_tile input datal examples example_image bsq ntx 3
164. ctive and batch If the atcor4 sav file is copied to atcor4f_batch sav and atcor4r_batch sav a batch job can be started immediately from the IDL command line otherwise atcor4 has to be typed first We begin with a description of the batch modules and keyword driven modules 6 3 Batch modules keyword driven modules A number of modules can also immediately be started as batch jobs You have to type atcor4 on the IDL command line then the ATCOR GUI selection panel pops up Disregard this panel and continue on the IDL command line with the name of the batch job module where all the input parameters have to be specified via key words Current batch programs are e slopasp_batch input filename pixelsize 10 0 kernel 3 dem_unit 0 The filename should have the last four characters as _ele and the extension bsq Two output files slope and aspect are generated from the elevation file e g CHAPTER 6 BATCH PROCESSING REFERENCE 142 example_DEM25m_slp bsq and example_DEM25m_asp bsq The values are coded in de grees The keyword pizelsize is not required if this information is included in the map info of the ENVI header The keywords kernel and dem_unit can be omitted if the default values kernel 8 and dem_unit 0 are used The unit of pixelsize is meter For the elevation height unit three options exist dem_unit 0 height unit is meters 1 for dm 2 for cm Note Bef
165. d figure 5 11 in chapter 4 label class 0 geocoded background 1 shadow 2 thin cirrus over water 3 medium cirrus over water 4 thick cirrus over water 5 land 6 saturated blue green band 7 snow ice 8 thin cirrus over land 9 medium cirrus over land 10 thick cirrus over land 11 thin haze over land 12 medium haze over land 13 thin haze over water 14 medium haze over water 15 cloud over land 16 cloud over water 17 water 18 cirrus cloud 19 cirrus cloud thick Table 10 2 Class labels in the hcw file Depending on the available spectral channels it may not be possible to assign certain classes Table 10 2 contains one class for cloud over land meaning water cloud whereas the low optical thickness cloud is put into the thin and medium thickness haze class Thin and medium haze can often be corrected successfully Of course there is no clear distinction between thick haze and cloud We take a pragmatic view and if the haze removal is successful in areas with thick haze then these pixels can be included in the haze mask Since this is not clear at the beginning it might be necessary to run the program twice with and without haze removal A check of the results will reveal whether the haze removal was successful ATCOR contains a number of criteria to assess CHAPTER 10 THEORETICAL BACKGROUND 206 the probability of a successful haze removal and will switch off the haze opti
166. d with eq 10 100 Cirrus removal is conducted as the first step during atmospheric correction followed by the aerosol and water vapor retrievals If the average water vapor column W of a scene is less than some threshold default W 0 6 cm then the cirrus removal algorithm is switched off to avoid a misinterpretation of bright surfaces as cirrus in the 1 38 um channel Normally atmospheric water vapor completely absorbs surface features in the 1 38 wm channel but the channel might become partly transparent to surface features for very low water vapor values This water vapor threshold can be set by the user see chapter 9 4 PO pelA 4 10 101 The file xrz_out_hcw bsq haze cloud water corresponding to a scene rxx bsq contains three relative levels of cirrus optical thickness thin medium and high The corresponding thresholds are arbitrarily set depending on the statistics mean standard deviation of the apparent reflectance p 1 38um map The file xzr_out_hcw bsq is intended as a visual aid or quicklook therefore the cirrus level maps of different scenes cannot be compared quantitatively As an example a certain scene setting could be CHAPTER 10 THEORETICAL BACKGROUND 224 ACIR band 0 01 AA cr E ih A 0 00 0 05 0 19 0 15 0 20 p RED band Figure 10 19 Scatterplot of apparent reflectance of cirrus 1 38 wm band versus red band e thin cirrus thickness color coded as light yellow with 0
167. d4_fwhm dat e New Sensor Name directory name of new sensor Outputs A new sensor definition is created which is shifted against the original sensor by the values given in the smile definition file The shift is calculated as across track average of the band wise polynomial All components of old sensor other than the response files are copied to the new sensor directory One may use this function for a constant offset by creation of a text file with the number of lines number of bands and the second column containing the spectral offset to be applied 000 X Apply Spectral Shift to Sensor Select Input Sensor Definition src_idl atcor atcor_4 sensor APEX_2015_L1 sensor_APEX_2015_L1 dat Select Input Smile File poly_ord4 sre idl atcor atcor_4 sensor APEX_oub leFUHH_SH sni le dat Define New Sensor Name Directory sro_idl atoor atoar_4 sensor APEX 224 SH output sensor defined APEX_225FWHM_SH Apply Smile Apply FWHM Done ELE LE Figure 5 16 Application of spectral shift to sensor 5 2 4 BBCALC Blackbody Function This routine calculates the blackbody function as described in section 10 1 5 weighted by the spectral response curve of the thermal band used for the temperature retrieval compare Fig Bult Inputs Spectral response file Select the rsp file of the spectral band in the thermal IR to be used for temperature retrieval Exponential Fit Limits The lower and the higher limit of t
168. ddition the upper threshold defining haze or sun elint might be scene dependent However the default values usually provide good results and a solid basis for a possible iteration of these two parameters 10 5 5 Cirrus removal On the first glance images contaminated by cirrus appear similar to hazy scenes discussed in the previous section However haze usually occurs in the lower troposphere 0 3 km while cirrus clouds exist in the upper troposphere and lower stratosphere 8 16 km The effect of boundary layer haze can be observed in the visible region but seldom in longer wavelength channels gt 850 nm However cirrus also affects the NIR and SWIR spectral regions Thin cirrus clouds are difficult to detect with broad band multispectral satellite sensors in the atmospheric window regions especially over land because land scenes are spatially inhomogeneous and this type of cloud is partially transparent On the other hand water vapor dominates in the lower troposphere and usually 90 or more of the atmospheric water vapor column is located in the 0 5 km altitude layer Therefore if a narrow spectral band is selected in a spectral region of very strong water vapor absorption e g around 1 38 yum or 1 88 um the ground reflected signal will be totally absorbed but the scattered cirrus signal will be received at a satellite sensor or a sensor in a high altitude aircraft e g 20 km AVIRIS scenes So a narrow channel at 1 38 um is ab
169. defined as T lt NIR lt 0 06 thin haze 10 97 Medium haze over water is defined as 0 06 lt p NIR lt Ta medium haze 10 98 The default value is T 0 12 i e 12 This value is also one of the editable preference parameters The third step is a linear regression between haze pixels in the NIR band and each other reflective band The regression is iterated with only those pixels deviating less than half a standard deviation from the average If a and 3 denote offset and slope of the regression line respectively the de hazed pixel for each channel j can be calculated as DN corrected j DN original j aj 8 DNnir DN clear j 10 99 where DN clear j is the average of all clear water pixels in channel j The same technique is also employed to remove sun glint The main problem is the specification of the clear water threshold If the threshold is too low clear water pixels are included in the haze mask if it is set too high haze or sun glint pixels will be included in the clear pixel class There is no unique solution because CHAPTER 10 THEORETICAL BACKGROUND 222 Figure 10 18 Subset of Ikonos image of Dresden 18 August 2002 O Space Imaging Europe 2002 Left original scene right after haze removal Color coding RGB 4 2 1 NIR Green Blue bands sandy bottoms over shallow water can have a similar spectral reflectance behavior as haze so the clear water threshold is scene dependent In a
170. e 89 An accuracy of 1 2 K can be achieved if the emissivity estimate is better than 2 14 Bibliography 12 Ackerman S A Strabala K I Menzel W P Frey R A Moeller C C and Gumley L E Discriminating clear sky from clouds with MODIS J Geophys Res Vol 103 D24 32 141 32 157 1998 Adler Golden S M Matthew M W Anderson G P Felde G W and Gardner J A 2002 An algorithm for de shadowing spectral imagery Proc 11th JPL Airborne Earth Science Workshop 5 8 March 2002 JPL Publication 03 04 Pasadena U S A Asner G Canopy shadow in IKONOS satellite observations of tropical forests and savannas Remote Sensing of Environment 87 4 521533 doi 10 1016 j rse 2003 08 006 2003 Asrar G Fuchs M Kanemasu E T and Hatfield J L Estimating absorbed photosyn thetically active radiation and leaf area index from spectral reflectance in wheat Agron J Vol 76 300 306 1984 Asrar G Theory and Applications of Optical Remote Sensing J Wiley New York 1989 Baret F and Guyot G 1991 Potentials and limits of vegetation indices for LAI and APAR assessment Remote Sensing of Environment Vol 35 161 173 1991 Berk A Bernstein L S Anderson G P Acharya P K Robertson D C Chetwynd J H and Adler Golden S M MODTRAN cloud and multiple scattering upgrades with application to AVIRIS Remote Sensing of Environment
171. e dependent brightness values to the nadir value It is recommended to apply the method to imagery after atmospheric correction i e to reflectance data However if only the across track illumination gradients shall be removed without any further atmospheric correction the algorithm can also be applied to radiance DN data In this case the brightness gradient may be caused by a combination of surface BRDF and atmospheric BRDF left right asymmetry in path radiance The algorithm is intended for large field of view sensors minimum FOV 20 It computes the column means with a certain angular sampling interval 1 or 3 The input image may be geocoded or not If it is not geocoded the total field of view FOV corresponds to the number n of across track image pixels per line If geocoded the scan angle for each pixel must be provided in a separate file _sca It contains the scan angle in degree scaled with a factor of 100 and coded with 16 bits per pixel This definition is taken from the airborne ATCOR PARGE interface Schlapfer and Richter 2002 Scan angles on the right hand side with respect to flight heading are defined as negative those on the left side as positive e g a value of 2930 represents a scan angle of 29 3 on the right side CHAPTER 10 THEORETICAL BACKGROUND 232 The nadir region is defined here as the 3 scan angle range Usually a 3 angular sampling interval from 3 to FOV 2 on the left side and 3
172. e 112 Low pass filter a Spectrum s ecos sasda adora r ha eee ee eee 112 Spectral Polishing Statistical Filter 113 Spectral Polishing Radiometric Variation osoo 114 Flat Field Polishing sa ec as cies 4 aw oe ee a ee ee hs 115 Pushbroom Polishing Destriping 115 Spectral Smile Interpolation o o e 0200 e 116 Cast Shadow Border Removal 00 0 000 eee ee eee 118 ARMAS co case a ole Blah Boke bok eel DR eee eR BT A 120 TOA At Sensor Radiance Cube 0000088 120 TOA At Sensor Thermal Radiance lt c eoe c ce ee bas we bk oS we ES OR 120 CONTENTS 5 5 7 3 At Sensor Apparent Reflectance es 120 STA Resample Image Cube 2 66 bk ee ee iu eee a ee ee ew 121 ee Menmi Tlp s pa es ek me hk ce awe Goh wo ar kee eb a He ee Ee ee G 122 5 3 1 Solar Zenith and AZIMUT 2 css ba ee an Cee Boe Ow ee 122 5 8 2 Classification of Surface Reflectance Signatures 123 5 8 3 Spectral Smile Detection e sani eose 086406 Seek eee 124 5 8 4 Spectral Calibration Atm Absorption Features 128 5 8 5 Calibration Coefficients with Regression 2 2 00 4 129 5 8 6 Convert High Res Database New Solar Irradiance 131 5 8 7 Convert atm for another Irradiance Spectrum 131 5 8 8 Thermal Spectral Calibration Atm Features 133
173. e BRDF correction in rugged terrain 232 10 6 3 BRDF effect correction BREFCOR saosaoa 236 10 64 BRDP cover index e caa 24 eai sra a ae 237 10 7 Summary of atmospheric correction steps e a a 240 Irl Alport Tor AAt terrain e ora kee a op a a aai a ew ee 240 10 7 2 Algorithm for rugged terraim oo soe gb aea ke eee ee 242 10 8 Accuracy of the method si soa doa sabeko anarad aak hae dae ee oa g aa 243 References 244 A Comparison of Solar Irradiance Spectra 251 List of Figures Aol Zan 2 3 2 4 2 0 2 6 2 1 2 8 2 9 2 10 dl 3 2 4 1 4 2 4 3 4 4 4 5 4 6 4 7 4 8 4 9 4 10 4 11 4 12 4 13 4 14 4 15 4 16 5 1 5 2 5 3 5 4 Visibility AOT and total optical thickness atmospheric transmittance Schematic sketch of solar radiation components in flat terrain Wavelength shifts for an AVIRIS scene e MODTRAN and lab wavelength shifts see discussion in the text Radiometric calibration with multiple targets using linear regression Sketch of a cloud shadow geometry a eee ee ee ees De shadowing of a HyMap sub scene of Munich 0 0004 Nadir normalization of an image with hot spot geometry Left reflectance image without BRDF correction Right after empirical BRDF correction BRDF correction in rugged terrain imagery Left image without BRDF correction Center after BRDF correction with threshold an
174. e added using the Add File s button on top while entries may be removed by the Remove Entry button The button show scan angle files displays the currently selected scan angle files Model Options e Roujean Geometric Kernel the Roujean Kernel is used for the geometric part of the model instead of the Li Sparse model e Maignan Hot Spot Geometry use the improved hot spot geometry for the volumetric kernel as proposed by Maignan RSE 2003 CHAPTER 5 DESCRIPTION OF MODULES 105 eoo BREFCOR v 1 1 c ReSe Input Files Add File s Remove Entry Show Scan Angle Files cubes CASI chile run401 CASI_2013_0 705_L401_atm bsc fcubes CASI chi le run402 CASI_2013_01_17_174756_L402_atm bsq Roujean Geometric Kernel J Maignan Hot Spot Geometry l Spectral Smoothing of Model Model Options i Model Interpolation F Leave Water As Is Write ANIF Outputs Calibration Granularity w Coarse 4 Levels lt Standard 5 Levels w Fine 6 Levels w Special 7 Levels w Self Defined gt gt fefine Reflectance scale factor from 0 1 0000 0 Fitting accuracy threshold 2 12 0000 Select Working Output Directory cubes CASI chile brefcor Ext Lbcor bsq Select BRIF Model File Name cubes CASI chile brefcor brefcor_3bd_model sav Band Numbers y All Bands 3 Bands 1 1 2 24 3 130 Plot Weights Console see model log file 2014 10 27T08 10 37 br_fitmodel Fitting BRIF
175. e haze land removal is likely to yield good results The haze land algorithm is switched off if those criteria are not passed ihaze 1 However as these criteria might fail in certain cases there is the option of setting haze 1 which enforces the haze removal disregarding the termination criteria ihaze 0 no haze correction ihaze 1 haze land correction might be switched off if quality check criteria are not passed ihaze 2 haze over water removal requires clear water pixels ihaze 3 haze land and haze water removal ihaze 4 sun glint removal over water ihaze 1 haze land correction is executed disregarding quality checks ihaze 2 is treated as ihaze 2 ihaze 3 haze land removal is forced haze water removal needs clear water pixels Haze removal is performed for the visible bands sun glint removal for all bands iwat_shd 0 water pixels are excluded from de shadowing land default iwat_shd 1 water pixels are included in de shadowing land The option iwat_shd 1 might be useful if the internal water classification based on purely spectral critera fails and dark land pixels are classified as water which is excluded from de shadowing So this flag is only used if the de shadowing option is set and if no external water map is supplied Example scene is mage1 bsq and a file mage1_water_map bsq or mage1_hcw bsq exist in the same folder then the flag wat_shd is ignored because an e
176. e names without file name extension have to be specified after pressing the button Save last spectrum For each target name three files will be generated e g target1 dat surface reflectance spectrum target1 txt a description file and target1_dn1 dat the DN spectrum The sequence of target _dn dat files is used in the spectral calibration module 5 4 6 Aerosol Type The aerosol type is a parameter which is fixed for atmospheric correction This routine searches automatically for the best suited aerosol type for the currently selected image This type can then be used when selecting the atmospheric file 5 4 7 Visibility Estimate ATCOR uses the Dark Dense Vegetation DDV approach to calculate the best visibility for an image This button allows to calculate the visibility estimate for the whole image without going into the image processing Note that for the Variable Visibility option the visibility map will be calculated anyway from the image during the image processing 5 4 8 Inflight radiometric calibration module This chapter presents the GUI for the inflight calibration which may also be invoked from one of the four possible ATCOR main panels The purpose is the calculation of the radiometric calibration coefficients for spectral bands in the solar region based on measured ground reflectance spectra The user should be familiar with chapter 2 4 before using this function Note a ground refle
177. e purpose of this program is to provide a tool to resize a DEM to an image cube s dimensions This menu function as depicted in Fig 5 22 allows to calculate both layers in one step e00 Mx Prepare and Resize a DEM select Input DEM Names Yaata hunap vord_1 TEN den_30to5n_ele bsq fArbitrary 1 1 629997 50 244515 00 5 00000 5 00000 units Meters Select Input Image Names Yaata hunap vord_deno hunap_geo bsq fArbitrary 1 600 630335 00 239275 00 5 00000 5 00000 1 units Meters Window Diameter for DEM Processor Pixels Options F Write Slope Aspect Files J Write Skyview File Define Output Name of Filtered DEM data humap vord_1 EW den_20tc5n_res_ele bsq The DEM has been prepared Help Done Figure 5 22 DEM Preparation CHAPTER 5 DESCRIPTION OF MODULES 79 Inputs Input DEM file Input DEM typically large than image Input Image file This is the target image georeferenced The DEM is automatically resized to its dimensions Window Diameter for DEM Processor Size of the kernel in number of pixels the slope and aspect and sky view side outputs are calculated using this kernel size Options The writing of side outputs i e slp and asp and sky may be triggered by deselecting these options Outputs An ATCOR elevation file _ele bsq is written of the same size and location as the reference Image Optionally files of slope and aspect and sky view are created same size as DEM Atte
178. e specified to reduce the execution time Usually an undersampling factor of 3 is sufficient A high an gular resolution is more important than a low undersampling factor The output file replaces the ending ele with the ending sky e g example DEM25m_sky bsq e shadow_batch input filename pixelsize 10 0 solze 30 5 solaz 160 8 dem_unit 0 The keywords have the same meaning as for skyview_batch solze is the solar zenith angle degr and solaz is the solar azimuth angle degr In particular filename should have the last four characters as _ele and the bsq extension The output file replaces the ending ele with the ending _shd e g example DEM25m_zen31_azil61_shd bsq The rounded zenith and azimuth angles will be included in the shd file name Note The shadow and skyview calculations can be omitted in gently undulated terrain Example for maximum slopes of 25 and a solar zenith angle of 40 no DEM shadow is possible Also the local trigonometric sky view factor employed if the _sky bsq file is missing is sufficiently accurate compare figure 10 5 e atcor4f_batch input filename output file vis vis or atcor4f tile input filename ntx 3 nty 2 output file vis vis The f in atcor4f_batch means the code for flat terrain i e no DEM is employed The file name must be fully qualified i e it includes the path e g data2 proje
179. e subsequent processing Haze over water Pixels must belong to the water mask and the NIR apparent reflectance p NJR must be greater than the NIR clear water threshold Tejear atery1R defined in the preference parameter file chapter 9 4 Thin haze over water is defined as TelerwaterniR gt p NIR gt 0 06 10 63 CHAPTER 10 THEORETICAL BACKGROUND 209 Medium haze over water is defined as 0 06 lt p NIR lt Ta 10 64 where To default 0 12 is another editable parameter in the preference file The method of haze removal over water is described in chapter 10 5 4 The same technique is also employed to remove sun glint 10 3 Quality layers The previous section defined a coarse pixel classification which is useful for an atmospheric cor rection In addition it supports an assessment of the quality of the processing For example a large error in the radiometric calibration could cause a scene classification with all pixels labeled as water In this case a user can immediately identify the problem Of course a more detailed assessment is possible with an analysis of the reflectance spectra Nevertheless the classification map land water haze cloud etc is a useful product and the quality of the atmospheric correction may depend on the correct class assignment at least for some classes The previous haze cloud water land pixel classifyer is a binary decision a pixel belongs to a certain class or not In real
180. e terms G LE and H are approximated by the following three equations Parlow 1998 G 0 4 Ra 7 24 LE 0 15 Rn G 7 25 H R G LE 7 26 For low vegetation indices SAVI lt 0 1 the ground heat flux G from equation 7 17 i e the vegetation model agrees well with G from equation 7 24 i e the urban model However major CHAPTER 7 VALUE ADDED PRODUCTS 157 differences exist for the LE and H terms see table 7 1 Parameters for this table are E 800 Rn 600 Ratm Rsurface 100 Wm T 30 C and T 20 C The veg and urb indicate the heat fluxes derived from the vegetation and urban model respectively For the urban surfaces asphalt concrete the G veg H veg and LE veg values are given in brackets for comparison but the corresponding urban heat fluxes are valid because the urban criterion equations 7 23 peso gt 0 10 ps50 gt 0 10 and p650 gt Paso 0 7 applies The last row repeats the concrete case for Rsolar 800 1 0 36 512 Rn Rsolar Ratm Rsurface 512 100 412 Wm a realistic reduced Ry value compared to the asphalt where Ey 800 Rsolar 800 gt 1 0 12 700 Ry 700 100 600 Wm surface peso Psso NDVI G veg H veg LE veg G urb H urb LE urb full veget 0 05 0 40 0 78 77 87 435 partial veget 0 10 0 20 0 33 185 76 338 dark asphalt 0 11 0 13 0
181. ection Figure 5 27 shows the GUI panel NOTE this function is found in the menu Filter for ATCOR versions without support for terrain correction Input Files e infile file to be analysed requires at least 4 bands Blue Green Red NIR e calfile calibration file for current input file usually found in the sensor or cal directory of the ATCOR installation e e0_ solar solar irradiance file for current input this file is also situated in the sensor or cal directory e outfile output file of the processing Options e Include Terrain Illumination calculates the illumination based on slope and aspect files and the solar illumination angles as defined below e Calculate Skyview Estimate calculates an estimate of the local skyview fraction on the basis of a linear model CHAPTER 5 DESCRIPTION OF MODULES 82 Figure 5 25 Example of a DEM left with the corresponding sky view image right DEM File nay have 16 or 32 bit integer or Float data Input IEH FILE auto_se data atecr2 3 dero data tarugged tn_biferest ele IT Dutput Shadow File Yato_a dsta7 atcor213 dex0_dato tn rusged ta blforest_zerd9_a2s147_ hd 4 DVERWRITE Solar zenith angle degree 49 Soler azimuth angle degree Oenerth D sast etc DEM resolution x y pixel size meters zo DEM height z unit Da yd yo RUN sax vee Figure 5 26 Panel of Cast Shadow Mask Calculation SHADOW e A
182. ective and thermal region Only the required range of flight altitudes and aerosol types should be selected CHAPTER 5 DESCRIPTION OF MODULES 79 Figure 5 18 Panels of RESLUT for resampling the atmospheric LUTs CHAPTER 5 DESCRIPTION OF MODULES 76 5 3 Menu Topographic The Topographic menu contains programs for the calculation of slope aspect images from a dig ital elevation model the skyview factor and topographic shadow Furthermore it supports the smoothing of DEMs and its related layers X Airborne ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Licensed for Dani DEM Import F Global Elevation fReSe 2015 errs DEM Preparation Ceo TIFF d Arc GRID ASCII Slope Aspect Skyview Factor Cast Shadow Mask Image Based Shadows DEM Smoothing Quick Topographic no Atm Correction Figure 5 19 Topographic modules CHAPTER 5 DESCRIPTION OF MODULES 77 5 3 1 DEM Import Import Global DEM Import Global Lat Lon DEM from SRTM data to UTM WGS 84 coordinates The approx 200m 0 5 arc min resolution global DEM data is available for download through the help menu Inputs Longitude range range in longitude direction pixel edges in decimal degrees only Latidude range range in latitude direction pixel edges deg Output Resolution output pixel size default 200m Output DEM name of DEM x ele bsq to be written Restrictions The DEM nam
183. ed DEM_SRTM30_WORLD DEM has to be available in the demo_data directory of the ATCOR installation Output converted DEM as ENVI file in UTM coordinates 000 X Import Global DEM Data from src_idl atcor atcor_4 demo_data DEM_SRTM30_WORLD DEM Longitude range from 0 8000 to 1 8000 Latitude range from 6200 tot 8 6200 Output Resolution m 20 000 Help Create DEM Done Figure 5 20 Import DEM from global elevation data SRTM Import Geo TIFF This function import a Geo Tiff file to an ENVI formatted elevation data file Import ARC GRID This Procedure reads a standard ARC GRID digitial elevation model CHAPTER 5 DESCRIPTION OF MODULES 78 Inputs Filename Name of DEM to be read usually extension asc Default output value for found missing data Value to be written to the pixels as defined by the NODATA keyword in the GRID file if output value is less than zero the mean of the read data is put to the missing pixels Actions View Parameters Shows the header information of the GRID file Read DEM Reads the DEM based on the given definition and stores it to a single channel ENVI formatted file if required Output converted DEM as ENVI file in arbitrary coordinates 00 0 X Read ARC GRID Digital Elevation Model ARC Input Files F Default output value for missing data p Help View Parameters Read DEM Figure 5 21 Import DEM from ARC GRID ASCII 5 3 2 DEM Preparation Th
184. ed smile analysis tool has been improved and enhanced in various ways more spectral features in the visible range have been added an option to search for the optimized FWHM has been added and overall accuracy has been improved by continuum removal based correlation e BREFCOR improvements fixes and updates to interface and revamped added sophisticated model analysis and plotting routines e The new installation process allows for direct updates and components installation from within the software e A new batch call option of ATCOR 4 has been added This allows to call ATCOR within a processing environment directly from the computer console CHAPTER 1 INTRODUCTION 15 e The resampling of atmospheric LUTs for a user specified sensor can also be submitted as a batch job by typing reslut_batch on the IDL command line see chapter 6 3 Chapter 2 Basic Concepts in the Solar Region Standard books on optical remote sensing contain an extensive presentation on sensors spectral signatures and atmospheric effects where the interested reader is referred to Slater 1980 89 Asrar 1989 4 Schowengert 1997 86 This chapter describes the basic concept of atmospheric correction Only a few simple equations 2 1 2 25 are required to understand the key issues We start with the radiation components and the relationship between the at sensor radiance and the digital number or grey level of a pixel Then we are already able to draw some impo
185. elp Save As Landsat 5 TM Black Forest 12 Sept 1985 solar zen 49 0 Print Setup 3 solar azim 146 deg Print 0 it 0 ile type ENVI Standard data type 1 interleave bsq sensor type Unknown byte order 1 band names band 1 band 2 band 3 band 4 band 5 wavelength 0 486 0 570 0 661 0 838 1 65 a 72 e 7 Figure 5 5 Simple text editor to edit plain text ASCII files 5 1 3 Resize Input Image This tool allows to resize some georeferenced ATCOR input data to new spatial dimensions without resampling The following inputs are necessary Inputs Files e Input Image Name name of input data file to be resized e Input DEM Name name of input elevation data file this line may be left empty if no DEM is available Dimensions e Upper Left corner coordinates of upper left corner pixel ENVI convention lower left edge of upper left corner pixel is pixel 1 1 e Use Reference File the coordinates of a reference file may be used e Number of Pixels size of output file in pixels in x and y direction ncols nrows starting at upper left corner e Output name Basis name of output main file auxiliary files are stored according to ATCOR conventions i e _ele bsq etc CHAPTER 5 DESCRIPTION OF MODULES 64 Output Based on the output Name Basis the auxiliary files are stored according to ATCOR conventions i e _ele bsq etc Possible outputs are e at sensor radiance ima
186. els i e direct neighbors of center pixel DEM resolution This is the pixel size a default of 30m is assumed This needs to be entered manually DEM height unit The unit of the values in the DEM usually a DEM is stored in meters but sometimes an integer DEM is stored as dm or cm data in order to preserve disk space Outputs The two files of slope and aspect are created same size as DEM integer data 5 3 4 Skyview Factor The sky view factor of a DEM is calculated with a ray tracing program and ranges from vsky 0 to 1 with 1 indicating a full hemispherical view Data in the sky view file are scaled from 0 to 100 and coded as byte The sky view factor determines the fraction of the hemispherical diffuse sky flux and 1 vsky x y determines the fraction of radiation reflected from surrounding mountains onto the considered pixel see chapter 10 1 1 This program is also available in the batch mode see chapter 6 3 Input parameters besides the DEM file are e DEM horizontal resolution in meters the x and y resolution must be the same e DEM height unit supported units are m dm and cm e Angular resolution degrees in azimuth and elevation e The undersampling factor of the DEM in pixels For large DEM s the skyview processing may be very time consuming unless an undersampling is chosen here Figure 5 24 shows the GUI panel and figure 5 25 presents a skyview image derived from a DEM image An angular azimuth elevation resol
187. ended to speed up processing time and for fast CHAPTER 5 DESCRIPTION OF MODULES 90 760 740 Elevation m 720 700 20 40 60 80 100 Pixel Number Figure 5 35 Influence of DEM artifacts on the solar illumination image Top illumination with low pass filtered DEM middle illumination based on original DEM bottom 100 m transsect using original DEM pixel size is 6 m checks as hyperspectral image processing may be very time consuming in rugged terrain 5 4 4 ATCOR4r rugged terrain This routine is to be taken if highest accuracy is required in terrain for imaging spectroscopy in struments The functionality is analoguous as described for the other panels CHAPTER 5 DESCRIPTION OF MODULES 91 5 45 SPECTRA module The SPECTRA module see figure 5 36 serves to extract spectra of different targets of the scene as a function of the visibility These spectra can be compared to field spectra or library spectra to estimate the visibility Scene derived spectra also may indicate calibration errors in certain channels In that case a copy of the sensor calibration file can be edited to match the retrieved reflectance with the field library spectrum or the inflight calibration module may be employed see chapter 5 4 8 In most cases it is useful to check some scene derived target spectra e g water vegetation or soils before starting the processing of the image cube at the risk of blind processing and obtaining wrong
188. ength shift is listed for each spectrometer and each target The final shift is taken as the average of all target wavelength shifts In addition a new wavelength file sensor_new wul is created containing the channel center wavelengths and the FWHMs bandwidth as full width at half maximum A copy of the original radiometric calibration file e g xxz cal is provided for convenience e g zxx_new cal which contains the original radiometric calibration coefficients and the updated channel center wavelengths In case of originally non Gaussian filter curves the output FWHM values of sensor_new wul represent the equivalent Gaussian FWHM values even though the spectral re calibration is based on the original non Gaussian filter curves The corresponding sensor with the new spectral calibration has to be added to the list of existing sensors see chapter 4 6 to process imagery CHAPTER 5 DESCRIPTION OF MODULES 129 Figure 5 76 SPECTRAL_CAL spectral calibration with the updated spectral calibration In case of non Gaussion filter curves the original channel response files band rsp should be copied to the new sensor directory applying the appropriate wavelength shifts For sensors with Gaussian filter curves the gauss_rsp module see chapter 5 can be applied to the sensor_new wul file to generate the corresponding band rsp files Note that a change of the spectral calibration usually requires a r
189. er since the required processing time is much higher than for the APDA method it is currently not implemented in the ATCOR environment In addition the method requires data with a very accurate spectral and radiometric calibration otherwise its potential advantage will be lost 2 Five water vapor grid points at 0 4 1 0 2 0 2 9 and 4 0 cm are sufficient to cover the 0 5 5 0 cm range with an accuracy of about 5 10 69 10 5 Non standard conditions The non standard situations refer to scenes with a substantial amount of haze and shadow areas The non standard atmospheric conditions treat the haze removal and de shadowing employing spec tral and statistical algorithms Although bidirectional surface reflectance effects are independent of the atmospheric conditions the subject is included here because the isotropic reflector is used for the standard conditions We present some methods of BRDF correction in flat and rugged terrain 10 5 1 Haze removal In many cases of satellite imagery the scene contains haze and cloud areas The optical thickness of cloud areas is so high that the ground surfaces cannot be seen whereas in hazy regions some information from the ground is still recognizable In ATCOR the scene is partitioned into clear hazy and cloud regions Here we will treat the low altitude boundary layer 0 3 km haze as opposed to high altitude cirrus Thin boundary layer haze can be detected with broad band mul tispectral ins
190. er is then defined as Figure 10 7 Combination of illumination map left with cast shadow fraction middle into continuous illumination field right Lred Loreen maz 5 0 35 8 0 42 6 10 20 a Tilo om E Lred Papp nir where Lilue Lgreen and Lyeq are the at sensor radiance values in the true color bands and papp nir is the apparent at sensor reflectance in the near infrared band The scaling factors here 5 8 and 6 are chosen such that all three parameters are within the same range i e a value range between 0 and 2 and optimized for full cast shadow at 0 5 The parameter Pshag is then scaled to a shadow fraction number fsnaq between 0 full cast shadow and 1 no cast shadow using empirically found limits These limits may be variable between sensors In a second step this map is combined with the standard geometrically derived illumination field see Fig 10 7 The resulting illumination map serves as an input to the ATCOR method Skyview Factor Estimate The skyview factor V describes the relative amount of the unobstructed sky hemisphere This factor is highly variable on small scales e g in vicinity of forest borders The skyview factor is approximated from the cast shadow fraction such that all areas in complete cast shadows get a reduced skyview factor as 0 0 Vsky 1 TE Sohad7 595 100 lt Vsky geoms 10 21 CHAPTER 10 THEORETICAL BACKGROUND 196 where 0 is the sola
191. er specific HOME directory idl rese atcor4 so multiple users of the same license can retain their personal preference settings The path of the last input image is saved separately for the flat and rugged terrain versions of ATCOR i e preference_atcor4f_path txt and preference_atcor4r_path tat In addition the file pref erence_parameters dat contains a number of default parameters that can be adjusted to scene properties This file contains the parameters with a short description CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 175 line 1 line 2 line 3 line 4 line 5 line 6 line 7 line 8 line 9 line 10 line 11 line 12 line 13 line 14 line 15 line 16 line 17 line 18 line 19 line 20 line 21 A choice to set the water vapor values for water pixels 1 average water vapor value of land pixels is assigned to water pixels Option available for wv_model 1 and wv_model 2 see section 9 5 line average of water vapor of land pixels is assigned to water pixels Option only available with iwv model 1 see the job control parameter section 9 5 A cloud reflectance threshold Te in the blue green region to define a cloud mask Pixels belong to the cloud mask if al p blue gt Te or a2 p green gt T asterisk apparent reflectance Typical values for Te range from 15 35 If the cloud reflectance threshold is too high clouds will be included in the haze mask Thi
192. eraged over all angles i e to a good approximation of the spectral albedo BHR ANIF Piso fgeoKgeo 0 0r as FAA PBRE 10 131 Piso FgeoK geo fvol yal PBHR The bihemispherical reflectance is described by the two hemispherical averages Kyeo and Kvot weighted by the respective factors and added to the constant isotropic reflectance Piso Alter natively the anisotropy with respect to nadir BRF would be an option as done in earlier BRDF CHAPTER 10 THEORETICAL BACKGROUND 240 research 79 This option is currently not supported in BREFCOR as the BHR is the more generic spectral albedo definition for surface object characterization The corrected bihemispherical reflectance is finally calculated as ppyr PEDRE where PHDRF is the bottom of atmosphere directional reflectance after standard ATCOR 4 atmospheric compen sation as described above Sample result The BREFCOR correction is most appropriate for wide FOV sensors i e with FOV values larger than approx 20 degrees typically found for airborne imagery compare validation samples in 84 A sample result of ADS 80 is displayed in Figure 10 29 The upper image is a mosaic of two east west flown flights where strong across track gradients are visible The middle image is the continuous surface cover dependent anisotropy factor used for correction The lower image is the correction result based on the calibrated Ross Li sparse BRDF model Most BRDF effects can
193. erent cover types and noted that there is no optimum method for all cover types A drawback of the Minnaert and empirical C methods is that they do do not distinguish between the direct and diffuse solar illumination as opposed to the physically based approach of ATCOR Nevertheless the latter approach also cannot avoid problems in faintly illuminated areas CHAPTER 10 THEORETICAL BACKGROUND 234 Correction method The methods described in the above section are supplemented by an empirical method with three adjustable parameters 37 b and g as explained below This approach was tested on different rugged terrain scenes with vegetated and arid landscapes and usually yields satisfactory results It reduces overcorrected reflectance values starting at a threshold local solar zenith angle Gr greater than the scene s solar zenith angle Os Equation 10 118 defines the implemented basic geometric correction function which depends on the local solar incidence angle solar illumination 6 and the threshold angle Gr The exponent b 1 3 1 2 3 4 or 1 is the second parameter and can be selected by the user Some guidelines on the choice of b are are discussed below The third adjustable parameter is the lower bound g of the correction function see Figure 10 26 G cosB cosBr gt g 10 118 The threshold illumination angle r should have some margin to the solar zenith angle to retain the original natural variation of pixels with illumination
194. ermal LUTs were compiled for the scan angle range 0 40 with an increment of 5 to keep the interpolation error of the radiance and transmittance values smaller than one percent Transmittance and path radiance values for scan angles above 40 will be extrapolated 9 1 1 Database update with solar irradiance In the solar region any high spectral resolution database of LUTs is based on the specification of an extraterrestrial spectral solar irradiance because the values of path radiance direct and diffuse solar fluxes depend on solar irradiance Other quantities direct and diffuse atmospheric transmittances and spherical albedo are independent of the solar spectrum ATCOR s standard atmospheric database is calculated for a certain irradiance E A and the corresponding file e0_solar_xzx dat is included in the directory atm_database Beginning with the ATCOR 2011 release there is an option to switch from one extraterrestrial solar irradiance source E A to another one F2 A The delivered high spectral resolution database of atmospheric LUTs is based on the Fontenla 2011 solar irradiance spectrum Fontenla et al 2009 2011 20 21 It represents the solar irradiance for a quiet or low activity sun and is recommended as the standard spectrum The original 0 lem resolution spectrum is convolved with Gaussian filter functions FWHM 0 4 nm and mapped on an equidistant 0 4 nm grid The file name of this spectrum E A is e0_so
195. erpolated tem files have the extension temi Resampling from high spectral resolution database fauto_as data atcor43 atm_database To sensor specific atmospheric libraryt fauto_as data atcor43 atm_ ib hymap04 Select aerosol types solar region F rural F urban P maritime P desert Select range of flight altitudes From hi m 5000 to h2 m 000 above sea level The string w10 in the filename indicates a water vapor column 1 0 g cm2 or cm w20 indicates a water vapor column of 2 0 g cm2 or cm etc Show Selected Files atm_database h03000_ww04_rura bp atm_database h03000_ww10_rura bp atm_database h03000_wv20_rura bp atm_database h03000_wv23_rura bp7 atm_database h04000_wv04_rura bp atm_database h04000_wv10_rura bp7 atm_database h04000_wv20_rura bp atm_database h04000_ww29_rura bp atm_database h05000_ww04_rura bp A Reflective Region Thermal Region Selected SENSOR HYMAPO4 w All Flight Altitudes A Select Range of Flight Altitudes atm_database h05000_ww10_rura bp Z k RUN eee Cancel OK Figure 9 4 GUI panels of program RESLUT 9 2 1 Resample sensor specific atmospheric LUTs with another solar irradiance It is also possible to resample existing sensor specific LUTs atm files with another solar irra diance spectrum Input is a sensor from the atcor sensor folder example
196. ession The choice iwv_model 0 indicates the water vapor retrieval is disabled Option 1 means the water vapor retrieval is performed for the selected bands and in case of several measurement bands the one with the smallest standard deviation is selected per 940 and 1130 nm region Finally if both regions are active the average of the water vapor maps of both regions is taken Option 2 employs a linear regression of bands which yields better results for the water vapor map if the data is noisy or not accurately calibrated If iwv_model 2 and channels in the 940 nm and 1130 nm regions are specified then only the 940 nm region is evaluated with a linear regression of bands If the regression is intended for the 1130 nm region then the 940 nm channels all channels in line 30 have to be specified as 0 line 35 0 icirrus flag for cirrus removal 0 disabled 1 enabled 1 forced The value icirrus 1 enforces cirrus detection and removal i e termination criteria are ignored line 36 0 irradO flag for solar flux on ground 0 disabled 1 enabled For irrad0 2 the surface reflected leaving radiance is calculated additionally For a flat terrain ASCII spectra of the direct diffuse and global flux on the ground are provided in the folder of the input scene see chapter 10 1 4 In case of a flat terrain the global i e direct plus diffuse flux image is calculated For a rugged terrain the images of the direct and diffuse fluxes are
197. evel and UTCx is the UTC time in seconds since midnight The acquisition date is specified in the ENVI header This information enables the automatic calculation of the solar zenith and azimuth angles evaluated for the center scan line of the scene For this purpose the nav txt file must be in the same folder as the scene named scene txt if the scene name is scene bsq or scene img In addition the ENVI header of each HySpex scene contains the radiometric scaling factor in the description tag and it is used to create the radiometric calibration file scene cal with the gain cl 100 scaling unit mWem sr wm It is the same factor for all bands and the offset c0 is zero 4 12 Airborne FODIS instrument Some airborne hyperspectral sensors are optionally equipped with an add on instrument that can measure the downwelling hemispherical solar flux at the aircraft altitude This add on instrument is usually named FODIS Fiber Optic Downwelling Irradiance Sensor FODIS data could be useful for atmospheric correction especially during adverse weather conditions However currently this information is seldom used for three reasons atmospheric correction software usually does not support processing of FODIS data the data must be corrected for the roll pitch yaw movements of the aircraft and FODIS calibration can be a problem ATCOR now offers a tool to use FODIS data during the atmospheric correction The genera
198. f image cubes and spectral polishing see chapter 5 6 The Simulation menu provides programs for the simulation of at sensor radiance scenes based on surface reflectance or emissivity and temperature images see chapter 5 7 The Tools menu contains a collection of useful routines such as the calculation of the solar zenith and azimuth angles spectral classification nadir normalization for wide field of view imagery spec tral calibration conversion of the monochromatic atmospheric database from one to another solar irradiance spectrum scan angle file creation and more see chapter 5 8 CHAPTER 4 WORKFLOW 38 X Airborne ATCOR File Sensor Topographic ATCOR PRDF Filter Simulation Tools Help Licensed for Dani DEM Import F Global Elevation ffReSe 2015 nnn DEN Preparation Geo TIFF Slope Aspect Arc GRID ASCII Skyview Factor Cast Shadow Mask Image Based Shadows TEM Smoothing Quick Topographic no Atm Correction Figure 4 4 Topographic modules Finally the Help menu allows browsing of the ATCOR user manual provides a link to web resources and displays license and credits information and serves to update your software see chapter 5 9 CHAPTER 4 WORKFLOW 39 4 2 First steps with ATCOR 4 The 4ATCOR menu of Fig 4 5 displays the choices ATCORZJf flat terrain and ATCOR r rugged terrain compare Fig 4 5 The last button starts the ATCOR processing in the ima
199. f is described in chapter 5 8 2 The file name nomenclature is described in chapter 4 5 The channel for the emissivity map is defined by the user parameter itemp_band described in chapter 4 6 e for multispectral thermal bands the normalized emissivity method NEM or adjusted NEM are also implemented In the NEM 27 the surface temperature is calculated for all chan nels with a constant user defined emissivity and for each pixel the channel with the highest temperature is finally selected In the adjusted NEM ANEM 14 the assigned emissivity is surface cover dependent Here we define four surface cover classes water vegetation soil dry vegetation sand asphalt based on the following criteria vegetation Pnir Pred gt 2 and Pnir gt 0 20 soil dry vegetation Pnir Pred gt 1 4 and Pnir Pred lt 2 0 and Prea gt 0 09 sand asphalt Pnir Preg lt 1 4 and Preg gt 0 09 water Pnir lt 0 05 and P1 6um lt 0 03 CHAPTER 10 THEORETICAL BACKGROUND 202 To each class the user can assign an emissivity valid for the channel with the highest temper ature There is only one common emissivity class in case of night data or data from purely thermal channels The ANEM method provides accurate channel emissivities and surface temperatures if the classes are assigned correctly and the emissivity value assigned to the channel with the max imum temperature is close to the actual channel emissivity Maximum surface emis
200. f the NIR band reflectance Pred Q Pnir 0 1 Pnir 10 82 Similar to the empirical SWIR relationships the coefficient q 0 1 is an average empirical value yielding results in close agreement with the SWIR method in many cases However deviations from the nominal value a 0 1 can vary about 30 depending on biome Before the final step of atmospheric correction takes place the visibility of non reference pixels in the scene can be set to the average value of the reference pixels or a spatial interpolation can be applied The visibility calculated for each reference pixel range 5 190 km in ATCOR is converted into an integer called visibility index vi with range 0 182 The visibility index is closely related to the total optical thickness 6 at 550 nm the equidistant optical thickness spacing is 0 006 for a ground at sea level and smaller for increasing elevations 5 0 185 0 006 x vi 10 83 It is easy to calculate the aerosol optical thickness AOT from a known total optical thickness by subtracting the Rayleigh optical thickness and a very small trace gas optical thickness compare Fig 2 1 in chapter 2 With the MODTRAN code the AOT at 550 nm can be calculated from a given visibility VIS km as AOT exp a z b z In VIS 10 84 where z is the surface elevation and a z b z are coefficients obtained from a linear regression of In AOT versus In VIS 0 5 Total Optical Thickness 0 0 0 50 100 150 20
201. face reflection for most land covers soils vegetation The extrapolation to longer wavelengths is computed as e Ifa 1 6 wm band exists P2 0 2 5um 0 5 P1 6um if ps50 P650 gt 3 vegetation CHAPTER 7 VALUE ADDED PRODUCTS 153 P2 0 2 5um P1 6um else e If no bands at 1 6 um and 2 2 um are available the contribution for these regions is estimated as P1 5 1 8um 0 50 P0 85um if ps50 p650 gt 3 vegetation P2 0 2 5um 0 25 po g5um if p850 p650 gt 3 P1 5 1 8um P0 85um else P2 0 2 5um P0 85um else At least three bands in the green red and near infrared are required to derive the albedo product Wavelength gap regions are supplemented with interpolation The contribution of the 2 5 3 0 wm spectral region can be neglected since the atmosphere is almost completely opaque and absorbs all solar radiation The output _flx file contains the channels SAVI LAI FPAR and albedo coded as 16 bit integer with the following scale factors e SAVI range 0 1000 scale factor 1000 e g scaled SAVI 500 corresponds to SAVI 0 5 e LAT range 0 10 000 scale factor 1000 e g scaled LAI 5000 corresponds to LAI 5 0 e FPAR range 0 1000 scale factor 1000 e g scaled FPAR 500 corresponds to FPAR 0 5 e Albedo range 0 1000 scale factor 10 e g scaled albedo 500 corresponds to albedo 50 The next section presents a simplified treatment of the radiation and heat fluxes in the energy
202. fore continuing the scene processing using any available image processing software Then the edited map is employed for the processing This provides some flexibility because it is difficult to calculate a satisfactory shadow map in all cases CHAPTER 5 DESCRIPTION OF MODULES 97 nction in od frequercy MIGON tu H scoled shodow function scaled gt y 3 Figure 5 41 Quicklook of de shadowing results Top left histogram of PhiU threshold 0 15 range 0 40 iteration 1 Top right histogram of PhiU threshold 0 38 range 0 19 iteration 2 Center results for iteration 1 left to right original shadow mask de shadowed image Bottom results for iteration 2 CHAPTER 5 DESCRIPTION OF MODULES 98 5 4 10 Panels for Image Processing When pressing the button IMAGE PROCESSING in one of the main panel figure 5 32 some additional panels will pop up First the processing options are to be selected see figure 5 42 Blocked Options Are Not Available For The Selected Sensor Might also apply for a reduced set of bands Either Haze or Cirrus Removal not both Blocked Options Are Not Available For The Selected Sensor iane aleo aply for a reded set oF bards Variable Visibility aerosol optical thickness Y Yes No Variable Visibility aerosol optical thickness Yes No Variable Water Vapor esesesessosesereosesoseseesosesos Yes No O vos OR Haze or Sun Glint Remo
203. found in chapter 6 000 x Airborne ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Licensed for Daniel Version 7 0 0 c DLR ReSe 2015 race Figure 5 1 Top level menu of the airborne ATCOR 59 CHAPTER 5 DESCRIPTION OF MODULES 60 5 1 Menu File The menu File offers some collaborative tools for handling of the data and ENVI files Below a short description of the individual functions is given X Airborne ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Help Display ENVI File Version 7 0 0 c DLR ReSe 2015 Show Text File a Select Input Image Resize Input Image wpe 4 Geo TIFF Export RGBN Geo TIFF Plot Sensor Response NRGB Geo TIFF Plot Calibration File TPEG2000 Geo Show System File ENYI BIF Image Edit Preferences ENYI BIL Image QUIT ERDAS Imagine AYIRIS Figure 5 2 The File Menu 5 1 1 Display ENVI File Use this function for displaying a band sequential ENVI formatted file in a simple way Band 14 637 60000 15 4000 band 14 Band 7 530 70000 16 4000 band 7 r Band 2 454 70000 13 6000 band 2 Single Band Default RGB Default CIR Cancel Select F E 4 Figure 5 3 Band selection dialog for ENVI file display The ENVI format is a raw binary file accompanied by an ASCII header hdr in ATCOR it should be stored in band sequential order CHAPTER 5 DESCRIPTION OF MODULES 61 An initial dialog allo
204. g nitrogen and oxygen only depends on pressure level it can be calculated accurately for a known ground elevation The ozone contribution to the optical thickness usually is small at 550 nm and a climatologic geographic average can be taken This leaves the aerosol contribution as the most important component which varies strongly in space and time Therefore the aerosol optical thickness AOT at 550 nm is often used to characterize the atmosphere instead of the visibility 1 5 Coe Ear amet ee ei Leer et meee eel ace ASA AAA eee a 1 0 Lae E R A 3 L H 0 hw HO 4 i A H 0 4 0 8 CO CO al 1 gos J 80 6 a 7 E gs z E T 7 15 H E E 04 CO gt S A 7 0 5 lt l total optical thickness 550 nm sea level r 4 OZ AOT 1 H O 1 L H 0 4 0 9 11 11 i 1 AAA A A A o 50 100 150 200 0 5 1 0 5 20 2 5 Visibility km Wavelength ger Figure 2 1 Visibility AOT and total optical thickness atmospheric transmittance The atmospheric direct or beam transmittance for a vertical path through the atmosphere can be calculated as t e 2 4 Fig 2 1 right shows an example of the atmospheric transmittance from 0 4 to 2 5 ym The spectral regions with relatively high transmittance are called atmospheric window regions In absorbing regions the name of the molecule responsible for the attenuation of radiation is included CHAPTER 2 BASIC CONCEPTS IN THE S
205. g else e g spectral radiance The reflectance range can be 0 1 the intrinsic reflectance unit or the percent range 0 100 Figure 5 61 shows the graphical user interface The input spectrum is high spectral resolution data The result has the same format and dimensions as the input file eoo IN Low Pass Filter a Spectrum Tool for smoothing noisy hyperspectral target spectra during INFLIGHT CALIBRATION Pick Input Spectrum a cubes hyspex VNIR_Egypt m2_01_VNIR_1600_SN0015_FOVx2_raw txt Output Filename low pass filtered spectrum cubes hyspex VNIR_Egypt m2_01_VNIR_1600_SN0015_FOVx2_rau_filter11 dat Low pass filter size number of channels 1 RUN Low Pass Filter Status i Quit Figure 5 61 Low pass filtering of a reflectance spectrum 5 6 3 Spectral Polishing Statistical Filter Remove spectral artifacts in high spectral resolution imaging spectroscopy data Inputs Input file name A hyperspectral image cube usually the output of atmospheric correction _atm bsq Sensor Spectral Response Defines the first band of the sensor response rsp This field may be left empty in that case the wavelength information from the ENVI header is used if the wavelengths tag is properly given if no wavelength reference is there a spectrally equidistant succession of the spetral bands is assumed Note the Savitzky Golay filter is not wavelength aware and uses always the assumption of equidistan
206. ge ATCOR main input image e scan angle file _sca bsq if available e illumination file x_ilu bsq if available e elevation file if set e side layers of topography _slp _asp _sky 000 X Resize an ATCOR data set Select Input Image Name Vsro_idl atcor atcor_4 deno_data Husper_ATCOR_demo FL3_WNIR_geo bsq UTH 1 1 593455 750 6283600 250 0 50000000 0 50000000 31 North WGS 84 units Meters select Input DEM Name src_idl atcor atcor_4 demo_data Hyspex_ATCOR_demo FL3_05m_sub_ele bsq UTH 1 1 593455 750 6283600 250 0 50000000 0 50000000 31 North WGS 84 units Meters Upper Left Corner x azass 750 y 283600 250 Use Reference Fie Number of Pixels x ez y isor DeFine Output Name Basis src_idl atcor atcor_4 deno_data Huspes_ATCOR_demo FL3_VNIR_geo sutibog select files Run Done Figure 5 6 Resize ATCOR input imagery 5 1 4 Select Input Image This function allows to select the basis input image i e the band sequential uncorrected image data in ENVI format It is useful to define the starting point including the default paths of the images for further processing 5 1 5 Import A small number of standard formats is supported for importing data layers to be used with ATCOR 4 Geo TIFF Multi Band geotiff in band ascending order RGBN Geo TIFF Geotiff in 4 band configuration storage order R G B N e g for photogram metric data CHAPTER 5 DESCRIPTION OF MOD
207. ge tiling mode i e the image is divided into sub images in x and y direction as specified by the user This mode is intended for large scenes compare section 5 4 11 and the inn file with the processing parameters must already exist X Airborne ATCOR File Sensor Topographic ATCOR ERDF Filter Simulation Tools Haze removal original DN data ATCOR4F flat terrain ATCOR4r rugged terrain Start ATCOR Process Tiled from inn Licensed for Daniel DLR ReSe 2015 Figure 4 5 Top level graphical interface of ATCOR Atmospheric Correction Let us start with a scene from a flat terrain area where no digital elevation model DEM is needed Then the panel of Fig 4 6 will pop up First the INPUT IMAGE FILE has to be selected ATCOR requires the band sequential format BSQ for the image data with an ENVI header Next the acquisition date of the image has to be updated with the corresponding button We work from top to bottom to specify the required information The scan angle file is only required if the image geometry does not correspond to the original geometry as specified in the sensor dat file which contains the number of pixels per line and the sensor field of view FOV see chapter 4 6 The scale factor defines the multiplication factor for surface reflectance range 0 100 in the output file A scale factor of 1 yields the output as float data 4 bytes per pixel However a scale factor of 100 is re
208. gle Br 65 Right illumination MSD C090 oca dR eee A eek E Effect of BRDF correction in an image mosaic ADS image swisstopo Atmospheric transmittance in the thermal region 2 Radiation components in the thermal region o e Top level graphical interface ot ATCOR e acra noda we dant ie a Top level graphical interface of ATCOR File 2 0 0 0 a ee ee Top level graphical interface of ATCOR Sensor 0 0 00 000 ee eee Topographic modules a s c ioa oade E rss rea be a ee ee Top level graphical interface of ATCOR Atmospheric Correction ATCOR panel for flat terrain imagery 0 2 0 00200000200 Image processing options Right panel appears if a cirrus band exists Panel tor DEM miles scs tsi ra feed eee ORS EE RA ee OR Ee ee Es Typical workflow of atmospheric correction 2 o e a Input output image files during ATCOR processing 2004 Directory structure of ATCOR A or ss ccoo natai a Be ae eea i Supported analytical channel filter types ee es Optional haze cloud water output file 2 o o oe e o Path radiance and transmittace of a SEBASS scene derived from the ISAC method Comparison of radiance and temperature at sensor and at surface level FODIS GUI supporting CaliGeo and NERC formats 04 Top level menu of the airborne ATCOR
209. h a slightly higher shift for the 1013 hPa case The total shift plots show that some compensation effects exist e g for h 100 km the MODTRAN wavelength shift is 0 and the lab shift is largest The opposite trend is observed for h 1 km where the MODTRAN shift is largest and the lab wavelength shift is small Therefore the three altitude cases coincide on one line The total shift increases with wavelength and is largest in the thermal spectral region There is a slight dependence of the results on the assumed scale height H 8 km and the sea level pressure pO 1013 hPa The scale height actually depends on the temperature and humidity profile the average mass of atmospheric particles and location because of the acceleration of gravity It can be approximately calculated with the equation of hydrostatic equilibrium using the ideal gas law see any textbook on atmospheric physics As ATCOR is used by customers all over the world and the specific atmospheric state is usually not known a typical standard scale height of H 8 km is assumed in ATCOR For typical summer and winter conditions the scale height varies between 8 0 and 8 5 km The wavelength difference due to the air refractive index for H 8 0 km versus H 8 5 km is smaller than 0 06 nm in the wavelength region 0 4 to 10 wm Therefore the use of H 8 0 km is sufficient for practical purposes Some examples e The AVIRIS NG Next Generation spectrometer is operated under near vacuum 10
210. h spectral band using the calibrated model and the BCI 4 correct the image using the anisotropy index Further details about this methods can be found in section 10 6 3 CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 28 triangle h 100 km square 4 2 km p 600 hPa diamond h 1 km Pa 1013 hPa MODTRAN Wavelength Shift nm D 2 4 6 8 10 Wavelength zm p labJ 1013 hPa diamond h 1 km square 4 2 km p 600 hPa y q triangle h 100 km 4 p labJ 940 hPa diamond h 1 km square 4 2 km p 600 hPa triangle h 100 km a o o Laboratory Wavelength Shift nm tn 2 ta Laboratory Wavelength Shift nm 0 Z 4 6 8 10 Wavelength jm Wavelength jm p labJ 1013 hPa same shift for all h compensation effects p lab 940 hPa same shift for all h compensation effects Total Shift nm p tn o Total Shift nm o 0 Z 4 6 8 10 d Z 4 6 8 10 Wavelength zm Wavelength zm Figure 2 4 MODTRAN and lab wavelength shifts see discussion in the text CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 29 L radiance Ad 2 targets least squares fit 500 1000 1500 2000 DN digital number Figure 2 5 Radiometric calibration with multiple targets using linear regression Figure 2 6 Sketch of a cloud shadow geometry CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 30 Y s ip d Figure 2 7 De shadowing of a HyMap sub scene of Munich Color coding RGB channels 860 64
211. h940 4 right absorption channel 920 970 nm ch940 5 left window channel 1000 1040 nm ch940 6 right window channel 1000 1040 nm The left and right channel numbers for each window or absorption region may be the same Put in a zero channel number if not applicable If the 820 nm water vapor region is selected the vector ch940 holds the corresponding window and absorption channels for this region line 31 ch1130 1 6 vector with 6 channel numbers for the 1130 nm water vapor retrieval ch1130 1 left window channel 1050 1090 nm ch1130 2 right window channel 1050 1090 nm ch1130 3 left absorption channel 1110 1155 nm ch1130 4 right absorption channel 1110 1155 nm ch1130 5 left window channel 1200 1250 nm ch1130 6 right window channel 1200 1250 nm The left and right channel numbers for each window or absorption region may be the same Put in a zero channel number if not applicable line 82 chth_w1 chth_al chth_a2 chth_w2 bands for thermal water vapor retrieval 10 12 wm chth_w1 left window channel SWCVR method see chapter 10 1 5 chth_w2 right window channel CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 183 chth_al left absorption channel chth a2 right absorption channel line 33 e_water e_veget e_soil e sand surface emissivities adjusted NEM channel with Tmax line 84 0 iwv_model water vapor retrieval 1 no band regression 2 band regr
212. h99000_wv29_rura this is an example of an atmospheric look up table file with a rural aerosol and a water vapor column of 2 9 gem see chapter 9 1 If the keyword atmfile is not specified then h99000_wv10_rura will be taken e elev 500 an example of a ground elevation at 500 m above sea level If elev is not specified then elev 0 is assumed However if the keyword elev is not specified and the data1 image inn CHAPTER 8 SENSOR SIMULATION OF HYPER MULTISPECTRAL IMAGERY 164 file contains file names for the DEM elevation slope and aspect then the DEM files are taken and the TOA calculation is performed for a rugged terrain If the keyword elev is specified the simulation is always performed for a flat terrain regardless of any possible DEM file names in the inn file e s2 35 5 an example of a solar zenith angle of 35 5 not used for toarad2 e vis 25 an example of a visibility of 25 km e pirelsz 4 5 an example of a pixelsize of 4 5 m e adjrange 500 an example of an adjacency range of 500 m e scalef 10 000 scale factor for the TOA radiance The default is scalef 1 0 which provides the output file as float data of TOA radiance in units of mWem sr um If scalef gt 1 e g scalef 10 000 the output TOA radiance is stored as 16 bit unsigned integer multiplied with the scale factor The advantage is a smaller output file compared to the 32 bit float the drawback is that radiances will be trunca
213. hadow map contains a lot of artifact shadow areas Therefore the proposed method tries to find the core shadow areas in a scene and subsequently expands the core regions to obtain the final mask that includes a smooth shadow clear transition The physically scaled shadow function is then applied only to the pixels in the final mask The histogram of the unscaled shadow function can be employed to separate regions of low values of from the moderate to high values compare Fig 10 22 A threshold Pr can be set in the vicinity of the local histogram minimum and the core shadow mask is defined by those CHAPTER 10 THEORETICAL BACKGROUND 228 Figure 10 23 Cloud shadow maps of a HyMap scene Left surface reflectance image of HyMap at Chinchon Spain 12 July 2003 Colour coding RGB 878 646 462 nm channels Center standard shadow map showing a lot of artifact shadow areas grey patches which do not appear with the core shadow approach right part Right improved cloud shadow map derived from core shadow regions pixels with x y lt By The details of the choice of p are discussed below As always with thresholding some arbitrariness is involved in the final selection Once the core shadow mask has been defined it is expanded to include the surrounding shadow clear transition zone of 100 m width De shadowing with the scaled shadow function is then exclusively applied to the pixels in this final mask This me
214. have a lot of large steps The DEM resolution is often not appropriate for high spatial resolution imagery and integer coded DEM s might have CHAPTER 5 DESCRIPTION OF MODULES 89 Figure 5 33 Panel for DEM files to be resampled and stored as float data Appropiate action for resampling or low pass filtering is recommended in these cases see the tips in chapter 9 6 Figure 5 35 shows an example in terms of the DEM illumination The top image is obtained after low pass filtering the original elevation file the central image is the illumination based on the original DEM and the bottom shows a 100 pixel transsect of the original elevation data revealing the steps The original DEM had a resolution of 30 m was coded as 16 bit integer and initially resampled to the 6 m pixel size of the image with integer arithmetic After reprocessing the elevation file the other DEM derived files should also be reprocessed _tavceL continue Figure 5 34 Panel to make a decision in case of a DEM with steps The pixel size of the DEM files must be the same as the image pixel size specified on the main panel see figure 5 32 The physical units of pixel size m and adjacency range km are also used to calculate the equivalent number of pixels needed to cover the adjacency range 5 43 ATCORAf flat terrain The menus for ATCOR 4 share the same functionalities as described above in both flat and rugged terrain options The ATCOR4f variant is recomm
215. he best fitting spectral shift AA A can be found for each image column j i e the A with the highest Pearson s correlation coefficient is selected This is equivalent to minimizing the merit function Ap 5nm CiA y Y Erkin Aj A 5 7 Aj A 5nm where L j k is the average at sensor radiance of the image for column j and channel k and Lr Ax Aj k is the corresponding reference radiance for a wavelength shift A within a 5 nm interval around Az By using the continuum removed scaled radiances the stability of the routine is enhanced 7 A 4th order polynomial is fitted through the calculated spectral points and the respective polynomial parameters of eq 4 1 are stored 8 The polynomial parameters are interpolated and optionally extrapolated to all other bands within the same detector or spectrometer unit Optionally the polynomial coefficients can be set to zero in atmospheric absorption regions to expedite the processing CHAPTER 5 DESCRIPTION OF MODULES 126 The same approach is used for FWHM detection with the difference that not the position of the spectral bands is varied but the FWHM of the spectral bands is scaled in a systematic way Once the coefficients have been determined they are converted into the required file format and are placed in the respective sensor folder for a subsequent fully automatic radiometric and atmospheric processing Fig 5 75 shows the panel of the smile detection module Inputs Inp
216. he blue region is available a channel in the green 0 5 0 6 um or red part of the spectrum 0 6 0 68 um could be used as a substitute Both criteria do not uniquely define the corresponding class The water criteria allow some margin for turbid water in the NIR region The more restrictive criterion p 0 85 um lt 3 would perform better for clear water bodies However it would fail for moderately turbid or muddy waters Other common water classification criteria such as average reflectance over all bands p lt 3 or p 0 4 0 6um lt 6 may also fail So one has to compromise and tolerate a certain amount of misclassification for a fully automatic algorithm The scaled shadow map z y is written to an output file The histogram of the unscaled shadow function Fig 10 22 typically has a main peak at Braz a smaller secondary peak at 2 due to shadow pixels and a local minimum at The secondary peak can be determined by level slicing the normalized histogram We arbitrarily define a threshold Pr as the intersection of this slice line at the level of h 2 with the normalized histogram h for dy lt lt Pmaz The approach with a main peak and a smaller secondary peak is restricted to cases where the percentage of shadow pixels in the scene is less than about 25 This applies to the fully automatic processing mode If the secondary peak at Pa is not clearly defined numerically i e no local minimum found at or histogra
217. he definition of thresholds employed for the masking of cloud and water areas and options for interpolating certain spectral regions Then the parameters of the inn file are described which is employed for the interactive and batch processing Last but not least a section on problems and tips is included 9 1 Monochromatic atmospheric database This chapter presents the technical details of the atmospheric database To be capable of handling typical hyperspectral sensors with arbitrary spectral bands in the so lar and thermal spectral regions a large database of atmospheric LUTs was compiled with the MODTRAN 5 radiative transfer code in 2010 The database is called monochromatic because of its high spectral resolution compare figure 9 1 The size is currently about 6 2 GB After resam pling with the spectral response functions of any sensor a typical size of the sensor specific database is 10 50 MB Chapter 9 2 contains a description of the resampling program RESLUT In the solar spectral region 0 34 2 56 wm MODTRAN was run with different wavenumber spacings to achieve a wavelength grid spacing of approximately 0 4 nm except for the 1400 nm and 1800 nm regions This required the use of MODTRAN s p1_2008 database i e 0 1 em in the 2 1 2 5 um region In addition different RT algorithms were used in atmospheric window regions the scaled DISORT algorithm with 8 streams SD 8 was employed in absorption
218. he scattering efficiency decreases strongly with wavelength For most natural surfaces the emissivity in the 8 12 um spectral region ranges between 0 95 and 0 99 Therefore the reflected downwelling atmospheric flux contributes only a small fraction to the signal Neglecting this component for the simplified discussion of this chapter we can write L L DN L Lgg T path Co C1 path 3 2 TE TE In the thermal region the aerosol type plays a negligible role because of the long wavelength and atmospheric water vapor is the dominating parameter So the water vapor and to a smaller de gree the visibility determine the values of Lpatp and 7 In case of coregistered bands in the solar and thermal spectrum the water vapor and visibility calculation may be performed with the solar channels In addition if the surface emissivity is known the temperature T can be computed from eq 3 2 using Planck s law For simplicity a constant emissivity e 1 0 or e 0 98 is often used and the corresponding temperature is called brightness temperature The kinetic surface temperature differs from the brightness temperature if the surface emissivity does not match the assumed emissivity With the assumption e 1 0 the kinetic temperature is always higher than the brightness temperature As a rule of thumb an emissivity error of 0 01 one per cent yields a surface temperature error of 0 5K For rugged terrain imagery no slope aspect correction is performed
219. he temperatures for which a fitting function should be created Unit of radiance output Select the unit either per micron or without normalization CHAPTER 5 DESCRIPTION OF MODULES 74 Outputs A file _hs bbfit is created containing the fitting parameters 00 BBCALC Blackbody Calculations Spectral response File arc_id1 atcor atcor_23 sensor aster14_ho astert4rrsd src_idl atcor atcor_23 sensor aster14_hs aster14 src_idl atcor atcor_23 sensor aster14_hs asterl rel Exponential Fit of Planck Functions Tbb 1 a b In Lbb Low temperature T1 Kelvin fero High temperature T2 Kelvin 530 0 Spectral radiance L ml m2 sr micron Required for ATCOR wv In band radiance L l cm2 sr For general purpose only Run bbeale running coefficier s b of equation Tbb 1 a MISA Tbb 270 330K Lbb mll m2 sr micron 1 07 nts a 1 040989E 02 7 734179E 04 band 14 max error K Values are written to file asterl4_hs bbfit path src_idl atcor atcor_23 sensor aster14_hs DONE Sd p Quit y Figure 5 17 Black body function calculation panel 5 2 5 RESLUT Resample Atm LUTS from Database The monochromatic database of atmospheric LUTs has to be resampled for the specific channel filter functions of each sensor Details are given in chapters 4 6 to 9 2 Figure 5 18 repeats the panels of the LUT resampling program RESLUT The resampling has to be done separately for the refl
220. he unscaled and scaled shadow function iii a histogram thresholding of the unscaled shadow function to define the core shadow areas iv a region growing to include the surroundings of the core shadow areas for a smooth shadow clear transition and v the de shadowing of the pixels in the final shadow mask Details are published in 66 A irect 1 diffuse Zo direct attenuated solar beam Figure 10 20 Sketch of a cloud shadow geometry The method starts with a calculation of the surface reflectance image cube p p A where three spectral bands around A 0 85 1 6 and 2 2 um are selected These bands from the near and shortwave infrared region are very sensitive to cloud shadow effects because the direct part of the downwelling solar radiation flux at the ground level is typically 80 or more of the total downwelling flux Channels in the blue to red region 0 4 0 7 wm are not used for the detection of shadow regions because they receive a much larger diffuse radiation component making them less sensitive to partial shadow effects Instead visible channels serve to define a potential cloud mask The surface reflectance is first computed with the assumption of full solar illumination i e the global flux on the ground consists of the direct Egir and diffuse Eaif component If DN denotes the digital number of a pixel Lp the path radiance and 7 the atmospheric transmittance ground to sensor the surface reflectance can
221. high resolution database is updated with a higher spectral sampling distance of SSD 0 4 cm for the wavelength region 7 10 ym i e corresponding to a wavelength SSD 2 4 nm and SSD 0 3 cm for the wavelength region 10 14 9 wm SSD 3 5 5 nm instead of the former SSD 1 cm and SSD 0 5 cm e For hyperspectral thermal instruments a spectral calibration spcal_th is offered based on the atmospheric absorption features present in the scene The module uses 10 spectra from isolated pixels or small boxes evenly spaced between the image lines at nadir and calculates the spectral shift see chapter 3 1 Spectral shifts smaller than FWHM 30 usually do not require an updated sensor definition and do not require an update of the sensor specific at mospheric LUTs The module is available in the main ATCOR menu under Tools Thermal Spectral Calibration Atm Features e For hyperspectral thermal instruments with medium bandwiths about 50 100 nm it may be difficult to estimate the water vapor content The module estimate_wv may be used for this purpose see chapter 5 8 8 IF the thermal scene contains water bodies the module thermalcal can be employed to calculate new calibration gain coefficients for a specified selected box of water pixels using the theoretical spectral emissivity of water see chapter 5 8 8 e The import function for GEOTIFF and JPEG2000 variations have been updated and added e The image bas
222. ht factors of hyperspectral bands o aooaa 160 Sensor simulation in the solar region o o e e 161 Graphical user interface of program HS2MS o o e 162 Sensor simulation in the thermal region o 163 TOA radiances for three albedos 2 a 164 Monochromatic atmospheric database ee 167 solar irradlance database o a 064 fda ne a a a ee aa 168 User interface to convert database from one to another solar irradiance 169 GUI panels of program RESLUT occ o lt 22 ds 170 Main processing steps during atmospheric correction 00004 187 Visibility AOT retrieval using dark reference pixels 188 LIST OF FIGURES 10 10 3 Radiation components illumination and viewing geometry 189 10 4 Schematic sketch of solar radiation components in flat terrain 190 10 5 Radiation components in rugged terrain sky view factor 004 193 10 6 Solar illumination geometry and radiation components 194 10 7 Combination of illumination map left with cast shadow fraction middle into con tinoos Mumination field fight lt 2 666456 e8086 4 ed eG eed a 195 10 8 Effect of combined topographic cast shadow correction left original RGB image right corrected image data source Leica ADS central Switzerland 2008 courtesy o eo eae ew
223. iangle or trapezoidal method employing the thermal band surface temperature and NDVI Carlson et al 1995 Moran et al 1994 In case of mountainous terrain the air temperature T zo and water vapor partial pressure pw Zo at a reference elevation zo have to be specified The height dependence of air temperature is then obtained with linear extrapolation employing a user specified adiabatic temperature gradient OT Oz OT Ta z Tazo gz 0 2 7 28 where OT 0z is typically in the range 0 65 0 9 Celsius 100 m The water vapor partial pressure is extrapolated exponentially according to Pwvl2 Pwwlzo 107 70 2s 7 29 where z is the water vapor scale height default 6 3 km The list of all output channels of the value added flx bsq file is CHAPTER 7 VALUE ADDED PRODUCTS 158 10 11 Soil adjusted vegetation index SAVI scaled with factor 1000 Leaf area index LAI scaled with 1000 Fraction of photosynthetically active radiation FPAR scaled with 1000 Surface albedo integrated reflectance from 0 3 2 5 um per cent 10 Absorbed solar radiation flux Rsolar Wm Global radiation Ey Wm omitted for constant visibility in flat terrain because it is a scalar which is put into the log file The next channels are only available in case of at least one thermal band Thermal air surface flux difference Rinerm Ratm Rsur face Wm Ground heat
224. ic correction contains bright overcorrected areas this file should be linked to the ilu file using any available standard image processing software The ilu file contains the illumination map 6 scaled as byte data ilu 100 x cos Bi arccos ilu 100 10 120 10 121 Let us assume an example A pixel in a dark area of the ilu image has the value ilu 32 i e 8 71 The overcorrected reflectance value be pz 80 and this value shall be reduced to 40 a value typical for the flat terrain neighborhood Then the threshold angle has to be specified such that cos3 cosbrp 0 5 with exponent b 1 in equation 10 118 in this case Br 50 So if the desired reflectance reduction factor is G then the required threshold angle can be calculated from eq 10 118 with b 1 il Br arccos arecos 10 122 In many cases a separate treatment of BRDF effects for soil rock and vegetation provides better results For this purpose two modes of BRDF correction are available for vegetation compare the graphical user interface of Figure5 46 The first mode is superior in most cases Reference 70 contains a comparison of different topographic correction methods for several Landsat TM ETM and SPOT 5 scenes from different areas The proposed empirical ATCOR approach performed best in most of these cases but no method ranked first in all cases CHAPTER 10 THEORETICAL BACKGROUND 236 10 6 3 BRDF effect correc
225. ics and Optical Systems Addison Wesley London 1980 References 250 90 91 92 93 94 95 o A 97 98 99 Slater P N Radiometric considerations in remote sensing Proc IEEE Vol 73 997 1011 1985 Slater P N et al Reflectance and radiance based methods for the in flight absolute cali bration of multispectral sensors Remote Sensing of Environment Vol 22 11 37 1987 Soenen S A Peddle D R and Coburn C A gt SCS C a modified sun canopy sensor topographic correction in forested terrain IEEE Trans Geoscience and Remote Sensing Vol 43 2148 2159 2005 Sutherland R A Broadband and spectral emissivities 2 18 um of some natural soils and vegetation Journal of Atmospheric and Oceanic Technology Vol 3 199 202 1986 Teillet P M Guindon B and Goodenough D G On the slope aspect corrextion of mul tispectral scanner data Canadian J Remote Sensing Vol 8 84 106 1982 Wanner W A H Strahler B Hu P Lewis J P Muller X Li C L B Schaaf and M J Barnsle Global retrieval of bidirectional reflectance and albedo over land from EOS MODIS and MISR data Theory and algorithm J Geophys Res vol 102 no 14 pp 17143D17161 1997 Wiegand C L Gerbermann A H Gallo K P Blad B L and Dusek D Multisite analyses of spectral biophysical data for corn Remote Sensing of Env
226. ide field of view imagery adding of a synthetic blue channel for multispectral sensors with a blue band e g SPOT which is done for the atmospherically corrected surface reflectance image spectral calibration con version of the monochromatic atmospheric database from one to another solar irradiance spectrum and BIL to BSQ conversion x Airborne ATCOR File Sensor Topographic ATCOR ERDF Filter Simulation Tools Help Licensed for Daniel Version 7 0 0 Solar Zenith and Azimuth Classification Surface Reflectance Signatures Spectral Smile Detection Atm Absorption Features Spectral Calibration Atm Absorption Features Thermal Spectral Calibration Atm Features Radiometric Calibration included in ATCOR Calibration Coefficients with Regression Convert High Res Database Mew Solar Irradiance Convert atm for another Irradiance Spectrum Create Scan Angles MTF Modulation Transfer Function FODIS Processing Figure 5 70 The tools menu 5 8 1 Solar Zenith and Azimuth The routine SOLAR_GEOMETRY is used to calculate the zenith and azimuth angle for the image location and acquisition time All explanations concerning the definition of angles are included in the panel Fig 5 71 Year T1383 Month F 6 Day E Hour UTC i11 Minute F 0 Second F 0 Geo Latitude 48 12 Geo Longitude j 11 30 degree Latitude positive is North of Equator negative is South
227. ight altitude solar geometry or atmospheric parameters All parameters not specified as keywords see list of keywords below are taken from the image inn file created by ATCOR The program HS2MS can be started to resample the radiance cube to a ms image by specifying the ms sensor i e channel filter functions and the ms noise equivalent radiance NER NER 0 is allowed so the resampled image product will only include the noise of the hs scene which will be reduced due to the inherent integration over several hs bands A channel constant NER or a file with channel dependent NER values may also be employed Figure 8 3 shows the GUI panel of program HS2MS Although the input image will usually be a hyperspectral scene with n gt 50 channels and the output a multispectral scene with m lt lt n channels this program can also be employed for the case of a multispectral input image with n lt 10 channels and a panchromatic sensor with m 1 band The program supports the four cases of resampling mentioned above i e solar or thermal at sensor radiance surface reflectance or emissivity Fig 8 4 describes the sequence of processing for the sensor simulation in the thermal region The file mage bsq consists of thermal band data and possibly also reflective band data After atmospheric correction the file image_atm bsq starts with the surface reflectance channels if existing continues with the surface radiance f
228. image in the reference channel The compensated surface radiance spectrum ERE can be converted into the equivalent compensated brightness temperature spectrum where most of the atmospheric absorption features are removed Both the compensated surface radiance and compensated brightness temperature are spectrally consistent with the data and represent the best estimate for the spectral shape The emissivity spectrum isac A may exceed the value 1 in certain channels if the maximum brightness temperature of a pixel does not occur in the selected reference channel However a common reference channel is needed in this method to obtain a consistent pixel independent spectrum of unscaled path radiance qn and transmittance 7 As soon as the emissivity is set in a certain thermal band this band can be used for the surface temperature retrieval In case of multispectral thermal bands the remaining channels can be used to retrieve the emissivity spectrum The advantage of the classification approach is a flexible assignment of emissivity classes and values This approach also works for sensors with only a single CHAPTER 10 THEORETICAL BACKGROUND 204 label description emissivity 0 not classified 0 980 1 clear water 0 991 2 dark vegetation 0 980 3 average veget 0 975 4 bright veget 0 980 5 yellow veget 0 980 6 mixed veg soil 0 975 7 dark bare soil 0 970 8 bare soil 0 970 9 asphalt 0 955 10 sand soil 0 970 11 bright sand soil 0 970
229. image line in across track direction with one or a few detector elements per spectral band The forward direction is provided by the motion of the platform Secondly a pushbroom linear array can perform the same task without moving optical elements but the number of array lines each recording a certain spectral channel in the focal plane is limited The third imaging technique employs an area detector array where one direction collects the spatial information across track and the orthogonal direction covers the spectral dimension The advantage of the last two techniques is a longer pixel dwell time and a potentially improved signal to noise ratio SNR The drawback is a substantial increase in the spectral and radiometric characterization i e a change of the channel center wavelength across the columns of the array spectral smile spatial misregistration keystone and detector non uniformity problems 51 19 73 Typical representatives of the whiskbroom type are Landsat TM ETM HyMap AVIRIS and Daedalus These instruments almost show no spectral smile i e the channel center position and bandwidth do not depend on column pixel location Spaceborne hyperspectral instruments showing the smile effect are Hyperion and CHRIS Proba airborne instruments are for example CASI 1500 and APEX This section describes the ATCOR input files required for smile sensors There are only two changes compared to the non smi
230. imated by a spatial interpolation The first step is the search for dark pixels using a small local nonoverlapping window box w 3 x 3 pixels for the calculation of the haze thickness map HTM For this purpopse a blue spectral channel is employed because it is most sensitive to CHAPTER 10 THEORETICAL BACKGROUND 220 haze If no blue band exists the green band is taken The next step calculates an additional HTM map with a moderately large window size e g w 21 x 21 pixels It is used to label haze and haze free regions by thresholding this HTM map The third step calculates the correlation of the band specific HTM A maps and re scales these maps in the inteval 1 0 for A Astue 2 2m More details can be found in the reference paper 50 10 5 3 Haze removal method 2 The method 2 haze removal algorithm runs fully automatic It is a combination of the improved methods 60 100 and consists of five major steps 1 Masking of clear and hazy areas with the tasseled cap haze transformation 17 TC z x BLUE z x RED 10 91 where BLUE RED 21 and z2 are the blue band red band and weighting coefficients respectively The clear area pixels are taken as those pixels where TC is less than the mean value of TC 2 Calculation of the regression between the blue and red band for clear areas clear line slope angle a see figure 10 17 If no blue band exists but a green spectral band then the green band is used as a sub
231. in the residual gain to an offset of 0 this is the typical situation Output A cube containing the spectrally filtered copy of the original image data cube is created 5 6 6 Pushbroom Polishing Destriping This routine treats each detector pixel of a pushbroom imaging spectrometer separately and derives gain and optional offset values in comparison to its direct neighbors The routine may be used for both spectral polishing of residual gain offset errors and for destriping of pushbroom imagery Inputs Input file name A hyperspectral image cube usually the output of atmospheric correction _atm bsq Interpolation Distance Number of pixels from center pixel i e a factor of 2 uses 2 pixels on each side for calculation of residual gains The distance should be in a range of the width of visible striping artefacts CHAPTER 5 DESCRIPTION OF MODULES 116 0 3 ATCOR Pushbroom Radiometric Polishing Selec Input File Name Yhyspex bio1_2_VNIR_1600_5N0004_7940_us_2x_2007 07 16T095204_rad_atm bsq Interpolation Distance in Spatial Dimension pixels E Polishing Filter Type wv Spectral w Spatial 2D Filter Type of Correction Function w Gain and Offset Gain only Define Polished Output Data Cube hyspex biol_2_VNIR_1600_SN0004_7940_us_2x_2007 07 16T095204_rad_atm_polish bsq Run Polishing Done y A Figure 5 65 Pushbroom radiometric polishing Polishing Filter Type Three options are available for p
232. inuous BRDF cover index BCI function is used for characterization of the surface It is calculated on the HDRF of four standard bands blue at 460nm green at 550nm red at 670 nm and near infrared at 840nm This reduced selection of spectral bands makes the index applicable for most current optical remote sensing systems The BCI function characterizes the image based on intrinsic BRDF properties from strong forward scatterers water to neutral targets asphalt to backward scatterers soils and vegetation types The index implementation is using the normalized difference vegetation index NDVI as a first input for vegetation density quantification due to its known relation to the leave area index LAI which has a significant influence on the BRDF 47 The NDVI is increased in Equation 10 127 by a value of up to 0 5 using the fact that dense agricultural vegetation shows higher green reflectance than dense forests i e the NDVI is increased by Cforest for dense forests having a green reflectance in a range below 7 In a further step the BCI is decreased for soils by Csoils using the effect that soils show a relatively low blue at sensor radiance A last adaption Cwater is made for water such that clear water areas are always set to a minimum value BCI NDVI C forest Coils an Cuwater gt 1 2 10 127 Note the gt sign denotes a maximum operator between the left and the right side of the term The three correction func
233. ion of a constant visibility aerosol optical thickness and water vapor content per scene as well as the retrieval of a visibility and water vapor map if the required spectral bands are available for the specific sensor Water vapor correction on a pixel by pixel basis is usually necessary for hyperspectral imagery The section on the non standard conditions contains a short discussion on empirical correction methods for bidirectional effects It continues with the description of a statistical haze removal method The third section presents a technique to compensate shadow effects i e cloud or building shadow areas are masked and de shadowed Then an overview is presented of all major processing steps involved in the atmospheric correction After atmospheric correction the surface reflectance cube can be used for classification A simple automatic method is included here based on template reflectance spectra of different surface covers Finally the accuracy of the atmospheric correction is discussed Before going into details a brief overview of the main processing steps during atmospheric cor rection is described in the next two flow charts Figure 10 1 contains a compact summary of the main processing elements after reading the sensor specific LUTs a masking and preclassification is conducted to obtain land water haze cloud and shadow areas Then an optional haze or cirrus re moval is conducted followed by an optional shadow removal The
234. ions are ele bsq for the digital elevation file _slp bsq for the DEM slope file _asp bsq for the DEM aspect _sky bsq for the sky view file _ilu bsq for the solar illumination file in rugged terrain and cla bsq for the classification map of SPECL The interface to PARGE Schlapfer and Richter 2002 65 uses a scan angle file sca bsq where band 1 provides the scan view angle for each pixel CHAPTER 4 WORKFLOW 44 atcor4 bin sensor aviris99 hymap01 dais02 atm_database atm_lib aviris99 hymap01 HA dais02 spec_lib pS demo_data Figure 4 11 Directory structure of ATCOR 4 and band 2 the absolute scan azimuth angle The visibility index map is named visindez bsq the aerosol optical thickness is _aot bsq the cloud building shadow map is named _fshd bsq and the atmospheric water vapor map wv bsq Thermal band imagery In case of thermal band imagery the surface temperature and emissivity are stored in separate files Surface temperature is appended to the reflectance cube i e is included as the last channel in the _atm bsq file e g tmagel_atm bsq The surface temperature calculation is based on an assumption for the emissivity in one spectral band Three options have been implemented e a constant scene emissivity of e 0 98 in the channel used for the surface temperature calculation e amap of 3 emissivity classes vege
235. ironment Vol 33 1 16 1990 Wiegand C L Richardson A J Escobar D E and Gerbermann A H Vegetation indices in crop assessments Remote Sensing of Environment Vol 35 105 119 1991 Wolfe W L and Zissis G J The Infrared Handbook Office of Naval Research Washing ton DC 1985 Young S J Johnson B R and Hackwell J A An in scene method for atmospheric compensation of thermal hyperspectral data J Geophys Research Vol 107 No D24 4774 4793 2002 100 Zhang Y Guindon B and Cihlar J An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images Remote Sensing of Environment Vol 82 173 187 2002 Appendix A Comparison of Solar Irradiance Spectra The following two plots show the relative differences between two extraterrestrial solar irradiance sources e Kurucz 1997 distributed with MODTRAN Berk et al 2008 8 The previous high resolution monochromatic databases of ATCOR were calculated with this spectrum e Fontenla 2011 Fontenla et al 2009 2011 20 21 The new ATCOR release uses the improved quiet sun spectrum of Fontenla and co workers also referred to as low activity sun As explained in chapters 5 8 6 5 8 7 the user can convert the database of atmospheric look up ta bles from one solar irradiance source to another one provided that the spectral range a
236. is the water reflectance threshold for the NIR band around 850 nm Equation 10 49 is also applied if any threshold Tyater vrr Or Twater swIRi is set to a negative value In this case the elevation pixel below 1 2 km and flight altitude higher than 10 km criteria are overruled Saturated pixels These pixels fulfill the criterion DN blue gt T saturation 10 50 where DN blue is the digital number in a blue band around 470 nm and the threshold Tsaturation is defined in the preference parameter file If a blue band does not exist a green band around 550 m is used as a substitute If a green band also does not exist a red band around 650 nm is used Tsaturation b encoding default b 1 0 e g 1 0 255 255 for 8 bit sensors with encoding 255 A lower value e g b 0 9 might be used because nonlinear effects might already occur ot lower radiance levels This would provide some safety margin and indicate situations near saturation Setting b 0 9 for an 8 bit sensor means that pixels exceeding DN 0 9 255 230 will be marked as nearly saturated For a 32 bit encoding integer or float no saturation threshold is defined As saturation usually occurs in the blue to red part of the spectrum channels in this region are checked and assigned to the class saturated false color coded red in the _out_hcw bsq file However the _atm log file contains the percentage of saturated pixels for each channel Cloud over
237. ishing lt s o saw saata shee ttia d ra ee 114 Flat feld radiometric polishing o s epos ont a a a a di ee IS 115 Pushbroom radiometric polisini e oe 600 5 4b pos e ie a cs 116 Spectral smile interpolation ee a 117 Shadow border removal tool krere sac kropos 119 Simulation modules mMenu o ociosas ara a ee ee e a a 120 Apparent Reflectance Calculation sa ni eaa a ba e va painaa 121 The tools menu isc correr a Ra a me ee ee 122 Calculation OF sum angeles cocos ae a e e A 122 Examples of reflectance spectra and associated classes eee 124 SPECL spectral classification of reflectance cube 02 00000 124 Example of classification with SPECL o o e 125 Spectral smile detection gt ocioso ona e a Re eS 127 SPECTRAL CAL spectral calibration lt ce sa se ce ewe ies a a 129 CAL REGRESS radiometric calibration with more than one target 129 Convert monochromanic database to new solar reference function 131 Convert atmlib to new solar reference function o e 132 Thermal Spectral Calibration e 133 Scan angle creation panel option a top option b bottom 135 MEF and erechive GIPOVs ocios Need bee ke Bog ey ee aa eS 137 The help Med eu po a ot a ee a ee ee ea ee a ae Ea gs 138 Water vapor partial pressure e e a 155 o IE i e ak Be do Sek oe a eS ee Oe G 156 Weig
238. ity the decision is typically not unique and a class assignment has only a certain probability As the absolute probability of a class assignment is very difficult to assess we define three probability levels low medium and high coded 30 60 90 respectively These numbers might be interpreted as a percent probability but the numbers are relative and arbitrary Currently there are three quality layers cloud water and snow which are solely calculated with spectral criteria The quality file is written if the corresponding flag is set to 2 see chapter 9 4 and figure 5 11 in chapter 4 Cloud probability e low cloud probability coded 30 p blue gt 0 15 and p red gt 0 15 and p NIR p red lt 2 and p NIR gt 0 8 p red and p NIR p SWIR1 gt 1 and NDSI lt OT or DN bie gt Tsaturation 10 65 where p blue is the apparent reflectance in a blue band and DN blue is the corresponding digital number If no blue band is available a green band around 550 nm is taken as a substitute If no green band exists a red band around 650 nm is taken Note that saturated pixels in visible bands are counted as cloud although they might be something else e g snow or a specular reflection from a surface Only saturated pixels with a very high NDSI gt 0 7 are assigned to the snow class e medium cloud probability coded 60 same as for low probability but with p blue gt 0 25 and p red gt 0 18 10 66 This
239. ixels must satisfy the conditions 0 04 lt p NIR lt 0 12 and pP SWIRI1 lt 0 20 10 55 and they should not belong to the water class This may also include building shadow pixels Snow ice Pixels must satisfy the conditions p blue gt 0 22 and NDSI gt 0 6 and DN blue lt Tsaturation 10 56 The condition DN blue lt Tsaturation Means that saturated pixels in the blue spectral band are not included in the snow mask instead they are put into the cloud class If no blue band exists a green band around 550 nm is taken However if the blue or green band is saturated and NDSI gt 0 7 then this pixel is assigned to the snow class If a green band and a SWIR2 band around 2 2 um exist the following relationships are used DN blue lt Tsaturation and p blue gt 0 22 NDSI gt 0 6 or p green gt 0 22 NDSI gt 0 25 p SWIR2 p green lt 0 5 10 57 CHAPTER 10 THEORETICAL BACKGROUND 208 Again if the blue or green band is saturated and NDSI gt 0 7 then the snow class is assigned Cirrus over land and water The apparent cirrus reflectance is calculated in the cirrus band 1 38 um Cirrus classes are defined according to the apparent reflectance Thin cirrus over land is calculated with 1 0 lt p cirrus lt 1 5 10 58 employing the percent reflectance unit Medium thickness cirrus is calculated as 1 5 gt p cirrus lt 2 5 10 59 and the thick cirrus class consists of pixels with
240. l 76 250 259 2001 Coll C Richter R Sobrino J A Nerry F Caselles V Jimenez J C Labed Nachbrand J Rubio E Soria G and Valor E A comparison of methods for surface temperature and emissivity estimation In Digital Airborne Spectrometer Experiment ESA SP 499 p 217 223 Nordwijk Netherlands 2001 Corripio J G Vectorial algebra algorithms for calculating terrain parameters from DEMs and the position of the sun for solar radiation modelling in mountainous terrain Int J of Geographical Information Science Vol 17 1 23 2003 Crist E P and Cicone R C A physically based transformation of Thematic Mapper data the Tasseled Cap IEEE Trans Geosci Remote Sensing Vol GE 22 256 263 1984 Dozier J Bruno J and Downey P A faster solution to the horizon problem Computers amp Geosciences Vol 7 145 151 1981 Dell Endice F Nieke J Schl pfer D and Itten K I Scene based method for spatial misregistration detection in hyperspectral imagery Applied Optics Vol 46 2803 2816 2007 Fontenla J M Curdt W and Haberreiter M Harder J and Tian H Semiempirical Models of the Solar Atmosphere III Set of Non LTE Models for Far Ultraviolet Extreme Ultraviolet Irradiance Computation The Astrophysical Journal 707 482 502 2009 Fontenla J M Harder J Livingston W Snow M and Woods T High resolution solar spectr
241. l restrictions are e It can only be used in flat terrain processing because the direct and diffuse solar fluxes required for a rugged terrain processing are no more available as FODIS measures only the total downwelling solar flux at the aircraft level e FODIS data contain no information on the atmosphere below the flight level so the precal culated atmospheric LUTs containing path radiance direct and diffuse transmittances have to be used only the global solar flux on the ground E is re calculated based on the FODIS measurements CHAPTER 4 WORKFLOW 55 e The hyperspectral scene must be processed in the original geometry because FODIS measures one spectrum per original scan line Geocoding can be performed later if required Without FODIS data the global flux at the ground is calculated as the sum of the direct and diffuse flux E Eo Ts cos0s Egif 4 5 where Ey extraterrestial solar irradiance Ts sun ground beam transmittance 0s solar zenith angle and Ej the diffuse flux on the ground With FODIS data eq 4 5 is replaced by Eg Ef Tair Taif 4 6 where Ef is the geometrically corrected spectral FODIS flux per scan line and Tair Taif are the direct and diffuse transmittances Ef is calculated from the originally measured flux EY as E gP cos0 cos3 4 7 where 0 is the solar zenith angle and is the incident solar angle between FODIS normal and incident solar beam cosB cos0
242. l urban maritime and desert In the altitude regime 1 5 km the increment is 1 km A larger increment is adequate for the high altitudes of 10 and 20 km because of the lower optical depth increment For flight altitudes in between ATCOR will interpolate if requested by the user In addition the database contains LUTs for the 99 km altitude which can be used for satellite sensors Imagery of the commercially available standard satellite sensors such as Landsat TM SPOT or IRS LISS are already treated in the satellite ATCOR environment For each flight altitude and aerosol type files with five water vapor columns are available W 0 4 1 0 2 0 2 9 and 4 0 cm or g cm sea level to space values These represent dry to humid atmospheric conditions 62 8 They are needed for the water vapor retrieval to create interpolated extrapolated values for the range W 0 3 4 5 cm In spectral regions where water vapor absorbs the accuracy of the surface reflectance retrieval depends on the number of water vapor grid points and the interpolation method full range of W or sub interval pertaining to a pixel 69 The CO mixing ratio of the atmosphere is set at 400 ppmv the ozone column is fixed at 330 DU Dobson Units equivalent to the former 0 33 atm cm for a ground at sea level The file names for the solar region include the altitude the aerosol type and the water vapor content They have the extension atm Example h03000_wv04_rur
243. l be too high Details about the processing panels can be found in section 5 4 9 2 6 BRDF correction The reflectance of many surface covers depends on the viewing and solar illumination geometry This behavior is described by the bidirectional reflectance distribution function BRDF It can CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 27 clearly be observed in scenes where the view and or sun angles vary over a large angular range Most across track brightness gradients that appear after atmospheric correction are caused by BRDF effects because the sensor s view angle varies over a large range In extreme cases when scanning in the solar principal plane the brightness is particularly high in the hot spot angular region where retroreflection occurs see Figure 2 8 left image left part The opposite scan angles with respect to the central nadir region show lower brightness values A simple method called nadir normalization or across track illumination correction calculates the brightness as a function of scan angle and multiplies each pixel with the reciprocal function compare Section 10 6 1 The BRDF effect can be especially strong in rugged terrain with slopes facing the sun and others oriented away from the sun In areas with steep slopes the local solar zenith angle 8 may vary from 0 to 90 representing geometries with maximum solar irradiance to zero direct irradiance i e shadow The angle P is the angle between the
244. lar_fonten2011_04nm dat If R denotes the set of quantities path radiance direct diffuse solar flux based on F A then the new set Ra with the irradiance spectrum E2 A is calculated as Ro A Ri A E2 E1 9 1 Figure 9 2 presents a schematic sketch of this conversion The folder sun_irradiance contains a number of solar irradiance files that can be selected The folder of the atmospheric database DB pertaining to E A includes the corresponding irradiance file e g e0_solar_fonten2011_04nm dat and the calculated new database D By includes the Ex A file e g e0_solar_kurucz2005_04nm dat The standard or active database is named atm_database while the new database includes 10 characters from the E file name e g atm_database_kurucz2005 CONVERT ATCOR folder file sun_irradiance e0_solar_f1 e0_solar_f2 e0_solar_f3 atm_database e0_solar_fi atm_database_f2 Figure 9 2 Solar irradiance database CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 169 The ATCOR tools panel contains the program to convert from one to another spectral irradiance database see Figure 9 3 It enables an update of the monochromatic atmospheric database without the need to repeat the time comsuming MODTRAN 5 computations involving the correlated k algorithm in some spectral regions Figure 9 3 shows an example for a reduced height set of files the complete
245. le instruments e The sensor definition file e g sensor_casi1500 dat needs one more line see Table 4 3 containing the parameters smile 1 if smile sensor otherwise 0 and filter_type a number between 1 and 9 for the type of channel filter function compare section 4 6 and Fig 4 12 The filter types 1 to 8 are analytical functions filter type 9 is reserved for arbitrary user defined channel filter functions the band rsp files Center wavelength and bandwidth for each channel are defined in the wavelength file wul pertaining to the center pixel column of the detector array e For each spectral channel j the channel center wavelength A j depends on the image column or pixel position x The absolute value of A j is specified in the wavelength file used to generate the spectral channel response functions and it is also included in the sensor specific solar irradiance file e g e0_solar_casi1500 spc If n is the number of image columns the change A z j of the center wavelength Ae j with the pixel position x can be described as a 4th order polynomial using the nm unit A x j nm ao j ar j a2 j 2 as 5 asli zt 4 1 Ac z j Ac A z j 4 2 The first left hand image pixel is x 0 the last right hand image pixel is x n 1 The coefficients a j have to be stored in an ASCII file corresponding to the band sequence The first column must contain the wavelength or band
246. le can be employed for this purpose see chapter 5 4 5 Reflectance spectra of scene targets can be displayed as a function of visibility and water vapor CHAPTER 4 WORKFLOW 42 and compared with field or library spectra If calibration problems exist in a few channels a copy of the calibration file can be edited in these channels to match the reference spectrum If there are problems in many channels the inflight radiometric calibration module should be used to generate a calibration file as discussed in chapters 2 2 2 4 and 5 4 8 Define Sensor a 86 i gt Figure 4 9 Typical workflow of atmospheric correction When the calibration file is OK the user can continue with the image processing Depending on the available sensor channels there are options to process the imagery with constant or variable visibility and atmospheric water vapor For large FOV sensors an option is available to correct for across track illumination BRDF effects This is especially useful if the image recording took place in the solar principal plane In addition a spectral polishing can be performed for the atmospherically and or BRDF corrected data as indicated by the dotted lines of figure 4 9 The polishing requires hyperspectral imagery Finally a classification may be performed Figure 4 10 shows the input output image files associated with ATCOR processing On the left part the flat terrain case is treated on the right part the rugged
247. le to detect cirrus clouds and if a correlation of the cirrus signal at this wavelength and other wavelengths in the VNIR and SWIR region can be found then the cirrus contribution can be removed from the radiance signal to obtain a cirrus corrected scene The basic ideas of cirrus correction were presented in several papers 23 24 26 74 The algorithm differs for water and land pixels For water a scatterplot of the 1 38 um versus the 1 24 um channel is used for land the band correlation is determined from a scatterplot of the 1 38 um versus a red channel around 0 66 wm To obtain a high sensitivity only vegetation pixels are taken because they have a low reflectance in the red spectral region so the cirrus contribution is CHAPTER 10 THEORETICAL BACKGROUND 223 easily traced The scatterplot is computed in terms of the apparent TOA or at sensor reflectance of P1 38 Versus Preg Where the apparent reflectance is defined as TL Es cos0s where L is the recorded radiance signal the extraterrestrial solar irradiance for the selected band and 0 is the solar zenith angle Following 23 the method can be described by the following set of equations p 10 100 Te A PO 1 se A PA Here pe is the reflectance of the cirrus cloud Te the two way transmittance direct plus diffuse through the cloud p the reflectance of the virtual surface land or water surface including all effects of molecular and aerosol
248. lectance ratios for the red and blue band are then calculated as Pred 0 5 P2 2 and Pblue 0 5 Pred 10 77 CHAPTER 10 THEORETICAL BACKGROUND 214 Pred 0 25 P1 6 and Pblue 0 5 Pred 10 78 This situation is sketched in figure 10 12 The correlation factor of 0 5 between the 2 2 wm and the red region is not a universal constant but may typically vary between 0 4 and 0 6 The correlation actually also works for dark soils So the dark pixels may also include soil areas For narrow band hyperspectral sensors a band close to 2 13 um is used instead of a 2 20 wm band surface reflectance a 0 48 0 66 0 80 1 6 22 Figure 10 12 Correlation of reflectance in different spectral regions The red band is then used to calculate the visibility compare figure 10 11 as the intersection of the measured radiance with the simulated visibility dependent at sensor radiance curve Since the same visibility is employed for the blue spectral band this provides an opportunity to adjust the spectral behavior of the path radiance which is essentially the aerosol path radiance since the Rayleigh path radiance is known in the blue spectral region d l Lblue ToluePblue Eg blue T 10 79 The question of an automatic aerosol type calculation is addressed next Aerosol type estimation After calculation of the scene path radiance in the blue and red region as total minus reflected radiance using the average values obtained for the da
249. lespie A R Lithologic mapping of silicate rocks using TIMS In Proc TIMS Data User s Workshop JPL Publ 83 38 Pasadena CA pp 29 44 1986 Gillespie A et al A temperature and emissivity separation algorithm for Advanced Space borne Thermal Emission and Reflection Radiometer ASTER images IEEE Trans Geosc Remote Sensing Vol 36 1113 1126 1998 Gu D and Gillespie A Topographic normalization of Landsat TM images of forest based on subpixel sun canopy sensor geometry Remote Sensing of Environment Vol 64 166 175 1998 Guanter L Richter R and Moreno J Spectral calibration of hyperspectral imagery using atmospheric absorption features Applied Optics Vol 45 2360 2370 2006 Guanter L Richter R and Kaufmann H On the application of the MODTRAN4 atmo spheric radiative transfer code to optical remote sensing accepted for publication Int J Remote Sensing 30 6 14071424 doi 10 1080 01431160802438555 2009 Hay J E and McKay D C Estimating solar irradiance on inclined surfaces a review and assessment of methodologies Int J Solar Energy Vol 3 203 240 1985 Hu B Lucht W Li X and Strahler A H Validation of kernel driven semiempirical models for the surface bidirectional reflectance distribution function of land surfaces Remote Sens Environ vol 62 no 3 pp 201214 1997 Huete A R A soil adjusted vegetation index
250. lues in faintly illuminated areas having small values of cosfB CHAPTER 10 THEORETICAL BACKGROUND 233 Figure 10 25 Nadir normalization of an image with hot spot geometry Left reflectance image without BRDF correction Right after empirical BRDF correction Background Several approaches have been pursuit to solve this problem in the past e an empirical coefficient C is calculated based on a regression of brightness values and the local ilumination angle derived from the DEM The coefficient depends on scene content and wavelength 94 57 e the sun canopy sensor SCS geometry is employed in forested terrain instead of the solely terrain based geometry 29 e the SCS method is coupled with the C correction 92 These approaches produced good results on sample scenes with uniform cover types presented in the above papers When applying the methods to a wider range of areas some of the practical problems are e mountainous scenes often contain a number of different covers e g deciduous forest conif erous forest mixed forest shrubs meadow rocks etc e the computation of the C coefficients for different surface covers would require a pre classi fication e the correlation obtained for the C coefficients is often less than 0 7 yielding unreliable results with this method These remarks are supported by reference 57 These authors applied different correction ap proaches to a TM scene containing diff
251. lute azimuth angle to direction north degree 10 unknowns are lower zero 3 Height of airplane above ground for each pixel meters unknowns are 0 4 Optional pixel distance distance in meters from aircraft to each pixel Optional Relative azimuth angle to flight direction would be in channel 2 while shifting channels 2 3 to 3 4 Elevation file DEM extension dem _ele bsq Elevation file containing the digital terrain model used for processing Format single band ENVI file in either integer unsigned integer or floating point format Default unit meters Slope file extension dem _slp bsq Slope angle for each image pixel Format single band ENVI file byte or 16 bit integer or float data type Unit degrees Aspect file extension dem _asp bsq Aspect angle for each image pixel with respect to north Format single band ENVI file 16 bit integer or float data type Unit degrees Skyview factor file extension dem _sky bsq Skyview Factor Format single band ENVI file byte data type Unit percentage 0 100 Cast shadow mask extension dem angles _shd bsq Shadow flag 0 cast shadow 2 no shadow 1 border region for each image pixel Format single band ENVI file byte data type Illumination file extension image _ilu bsq External Illumination file Format ENVI floating point data file external illumination files are only read if they are in floating point format and if they are in the same size as the imagery Co
252. ly selected for sensors with a single thermal band The last option can be selected in case of multispectral thermal bands After the surface temperature has been calculated based on the provided emissivity map in one channel the remaining emissivity channels are computed and put into the file mage1_atm_emiss bsq The multi band emissivity file is coded with 16 bits per pixel scaled with the factor 1000 4 6 Definition of a new sensor A few steps have to be taken to include a new airborne sensor in ATCOR compare Fig 5 13 These are e A new sensor subdirectory in the atcor sensor folder has to be created This is easiest done using the routine Define Sensor Parameters as described in chapter 5 2 1 Please make sure this name does not agree with an existing multispectral sensor name in the atcor cal folder This process may also be done manually by copying and adapting an existing user defined sensor or one of the samples provided e A sensor definition file must be specified Just copy any of the existing files e g sen sor_hymap2003 dat and modify the appropriate lines see the next table or use the function above to make the changes e A wavelength file wvl has to be specified It is a simple ASCII file with three columns band number center wavelength and bandwidth compare Fig 5 13 Center wavelength and bandwidth may be given in the nm or wm unit The first line may contain an opti
253. m difference h 2 h 1 lt 0 03 then dy is defined as the intersection of the slice level 0 10 with h for lt maz More flexibility exists in the interactive mode see chapter 2 5 figure 5 40 Masking of the core shadow areas with lt dy Fig 10 22 is critical like any thresholding process a large threshold could potentially include non shadow areas a low threshold could miss shadow areas The current automatic algorithm has the three user selectable options of a small medium or large core shadow mask corresponding to thresholds set at Py 0 1 97 and 97 0 1 respectively The default value for the fully automatic algorithm is the medium size mask In addition an interactive mode for adjusting the threshold is also available CHAPTER 10 THEORETICAL BACKGROUND 230 A second tunable parameter is the minimum fractional direct illumination also called depth of shadow Theoretically it can be zero i e a completely shadowed pixel receiving only diffuse solar illumination However a too low estimate close to zero will boost the surface reflectance especially for channels in the 1 5 2 5 um region eq 10 113 since the diffuse solar radiation term Edf is very small Therefore small positive values of F are recommended The range of Phin is typically from 0 05 to 0 1 with the default set at O 0 08 The third tunable parameter is maz providing the range of stretching of the unscaled shadow function into
254. mits w extrapolate trend repeat values w to zero at borders Help Detect Smile Detect FIJHH Plot Save Report Tone Figure 5 75 Spectral smile detection CHAPTER 5 DESCRIPTION OF MODULES 128 5 8 4 Spectral Calibration Atm Absorption Features The program SPECTRAL CAL is only intended for hyperspectral sensors and employs atmo spheric absorption features to detect and remove possible wavelength calibration errors see chapter 2 2 For this purpose a certain number of target spectra have to be selected in the SPECTRA module NOTE Alternatively to this routine the spectral calibration can be done by first using the spectral smile detection routine see section 5 8 3 and then applying the shift to the sensor as described in section 5 2 3 Input to the spectral calibration module are the DN spectra of selected fields saved as ASCII files in the SPECTRA module by pressing the button Save last spectrum The files should be numbered consecutively starting with a name such as location_target1 without extension The next target has to be named location_target2 etc For each target field three files will generated for example e location_targetl dat contains surface reflectance spectrum e location_targetl txt contains target coordinates and processing parameters visibility water vapor column etc e location_target1_dn1 dat contains the DN spectrum For a given location
255. model No specific panel is displayed The routine asks for the input reflectance image All other infor mation is taken from the inn file Please make sure that the reflectance image spectral definition corresponds exactly to the chosen atmospheric library and sensor definition as of the inn file 5 7 2 TOA At Sensor Thermal Radiance This routine calculates an At Sensor Thermal Radiance Cube from an emissivity temperature cube where temperature is in the last band All parameters used for the processing are generated from the x inn file of the input cube If the function is called the cube is opened and the inn file is read which results in an at sensor cube _toarad bsq 5 7 3 At Sensor Apparent Reflectance This routine calculates an at sensor apparent reflectance from a calibrated at sensor radiance image cube This routines alculates for each image band the following output Papp DN x c co T d En cos 00 5 6 where DN stored data values c gain for conversion of DN to at sensor radiance co offset for conversion to at sensor radiance d relative sun earth distance average d 1 Eo solar irradiance top of atmosphere NOT at aircraft altitude o solar zenith angle Inputs CHAPTER 5 DESCRIPTION OF MODULES 121 e input file name e calibration file name x cal e solar radiation file e0_solar_ spc e output file name e scale factor see below e date of the year given exactly as day
256. model to cubes CASI chile run402 CASI_2013_01_17_174756_L402_atm bsq gt 2014 10 27T08 10 37 br_fitmodel Using HCW file cubes CASI chile run402 CASI_2013_01_17_174756_L402_out_hcw bsq 2014 10 27T08 10 37 br_fitmodel Using ILU file cubes CASI chile run402 CASI_2013_01_17_174756_L402_ilu bsq 2014 10 27T08 10 38 br_fitmodel Using BCI level limits 1 50000 0 3900000 0 200000 0 400000 0 900000 Calculating Correction Functions maximums 15 01234567 8910 11 12 13 14 Done Process completed 2014 10 27T08 10 42 br_fitmodel Calibration was tried for 5 out of 5 calibration levels 2014 10 27T08310342 br_calmodel Created BRIF model file cubes CASI chile brefcor brefcor_3bd_model sav 7 N Help Calibrate Model Do Image Correction Done Figure 5 53 BREFCOR correction panel airborne version e Spectral Smoothing of Model Smooth the weighting functions in the spectral dimension this option is useful for hyperspectral instruments only to avoid spectral artifacts e Model Interpolation Interpolate missing values in the model from neighbors by linear inter polation Default no interpolation e Use Precalculated Model use an existing model file fitting to your data NOTE the model file should have the same number of bands and have the same calibration granularity as of the current settings e Write ANIF outputs By default the corrected image is written Use this option to get the side outputs i e the files anif ani
257. month eg 26 7 for July 26th used for sun earth distance calculation e solar zenith angle use Tools Solar Zenith and Azimuth for its calculation Output A cube containing the scaled apparent reflectance in is stored The data format is driven by the scaling factor as follows e scale lt 10 byte e scale gt 10 integer e scale gt 500 unsigned integer e scale lt 1 floating point effective value unit wavelength reference and FWHM are taken from the file e0_solar_ spc e ATCOR Apparent Reflectance Calculation _ setect Input File Name Ydata huperion Bern_02 Htperian_subl67 bsg setect Calibration File Ysrc_idl atcor atcor_23 sensor huyperion157 huperion_187 cal setect Solar Reference File 0 sro_idl atcor atcor_28 sensor huperion167 e0solar_hyperionl67 p Ie in Name of Output Cube data hyperion Bern_02 Hyper ion_sub_rhoapp bsq Scale factor x Refl E Date dm puos Solar Zenith deg E Help Done Figure 5 69 Apparent Reflectance Calculation 5 7 4 Resample Image Cube This routine allows to simulate a multispectral image from imaging spectroscopy data A detailed description of this routine is given in chapter 8 CHAPTER 5 DESCRIPTION OF MODULES 122 5 8 Menu Tools The Tools menu contains contains a collection of useful routines such as the calculation of the solar zenith and azimuth angles spectral classification nadir normalization for w
258. n This is the recommended standard yielding good results in most cases e ibrdf 22 correction with sqrt cos of local solar zenith angle eq 10 118 with b 1 2 for soil sand Vegetation eq 10 118 with exponent b 3 4 and b 1 for A lt 720 nm and A gt 720 nm respectively i e option b in the BRDF panel see Figure 5 46 strong correction e betathr threshold local solar illumination angle 8r where BRDF correction starts If beta_thr 0 and ibrdf gt 0 then the angle 8r is calculated in ATCOR depending on the solar zenith angle and its value can be found in the corresponding _atm log file e thr_g g lower boundary of BRDF correction factor see chapter 10 6 2 eq 10 118 10 119 line 24 1 0 820 0 780 0 600 lai model a0_vi al_vi a2_vi Parameters for the LAI model to be used if ksolflux gt 0 see chapter 7 line 25 0 900 0 950 0 380 c_fpar a_fpar b_fpar parameters for the fpar model to be used if ksolflux gt 0 see chapter 7 line 26 20 0 0 83 air temperature C air emissivity see chapter 7 Parameters for the net flux calculation used for flat terrain ignored for rugged terrain line 27 20 0 0 50 0 65 15 0 6 3 t air z0_ref tgradient p wv zh_pwv chapter 7 t air air temperature Celsius at elevation z0_ref z0_ref reference elevation for t_air km asl tgradient air temperature gradients Celsius per 100 m height CHAPTER 9 IMPLEMENTATION REFERENCE AND SENS
259. n appropriate feature by clicking the start and end points of the line with the left and center mouse buttons respectively After clicking Calculate MTF the MTF is plotted below for up to four fixed channels as well as the effective GIFOV Ground Instantanous Field of View defined as one half of the reciprocal of the spatial frequency at which the MTF is 0 5 The effective GIFOV is also called effective instantaneous field of view EIFOV and it is specified in pixels Low values of the EIFOV represent a good capability at resolving high spatial frequencies The slider Line averaging can be adjusted between 3 and 9 to evaluate up to 9 parallel lines centered on the user defined line to average the phase effect Results MTF LSF DN_profile Effective GIFOV for all bands can be saved as ENVI spectral library files slb The Status widget indicates the mode of calculation LSF or ESF 5 8 11 FODIS Processing The reader is referred to chapter 4 12 for the description and presentation of the graphical user interface for FODIS Fiber Optic Downwelling Irradiance Sensor data processing CHAPTER 5 DESCRIPTION OF MODULES Figure 5 82 MTF and effective GIFOV Quit Select image file Red Green Blue Display RGB a zoom sub scene Line averaging Le E lines to average Calculate MTF Save MTF and DN Profiles Status gt gt LSF evaluated lt lt
260. n case of thermal bands an emissivity selection panel will appear The first two options are available for instruments with only a single thermal band the NEM and ISAC options require multiple thermal bands and are not shown if there are less than 5 thermal bands The surface classes water vegetation soil etc are calculated on the fly employing the surface reflectance spectra from the reflective bands Figure 5 44 shows the panel with the two options for haze over land processing as explained in chapter 10 5 3 The panel of figure 5 45 pops up when the spatially varying visibility option was selected and if the sensor has a 2 2 um or 1 6 wm band or at least a red and NIR band required for the automatic masking of the dark reference pixels compare chapter 10 4 2 CHAPTER 5 DESCRIPTION OF MODULES Figure 5 45 Reflectance ratio panel for dark reference pixels 99 CHAPTER 5 DESCRIPTION OF MODULES 100 The panel of figure 5 46 pops up for rugged terrain and contains the input parameters to incidence BRDF compensation due to terrain variations as discussed in chapter 2 6 Figure 5 46 Incidence BRDF compensation panel Figures 5 47 and 5 48 are associated with the value added products as described in chapter 7 This value added file contains up to 10 channels if the sensor has thermal bands In case of a flat terrain the air temperature has to be specified For a rugged terrain air temperature at some base elevation and the
261. n the 8 5 13 5 um region are taken into account to avoid strong atmospheric absorption regions Since MODTRAN look up tables are used the resulting wavelength shift also depends on the accuracy of these LUTs The default temperature range is 280 310 K but the user can specify it with the keyword trange e g trange 270 320 The temperature increment is fixed at 1 K Input to the spectral calibration is the thermal scene ENVI band sequential format in the original geometry i e not geocoded The program will select 10 pixels from 10 image lines in the image center nadir calculate the wavelength shift and the mean and standard deviation Additionally there is an optional keyword box where the averaging over a specified box of pixels can be specified to reduce the influence of noise The default is box 1 no pixel averaging box 3 performs an averaging over 3 x 3 pixels A wavelength shift lt FWHM 30 will have a negligible effect and usually does not require an update of the sensor response functions and sensor specific atmospheric LUTs When starting the spectral calibration program either in the GUI or batch mode see chapters 5 and 6 respectively for an image named scene bsq the corresponding scene inn must already be available because the sensor name and atmospheric LUT s are taken from this file Note this scene inn file contains the sea level water vapor column in its name e g the string wv10 in h02
262. n wavelength grid Function parameters are filename is the full name of the surface reflectance file fpname is the full name of smile_poly_ord4 dat i e including the path number is the above option number 1 3 and if the keyword silent is set the progress about the band processing is not issued to the command line This module is also available in the interactive mode see main menu Filter Spectral Smile Interpolation Image Cube chapter 4 e dehaze4 input filename water iw ipm ipm ihm ihm ikeep ikeep Here the filename is the name of the image level 1 data original DN stored as ENVI bsq band sequential including the path The sensor name is read from the scene inn file e g sensor ads40 If keyword water 0 or not keyword set then only land pixels are dehazed with water 1 also water pixels will be dehazed Keyword ipm specifies the interpolation method ipm 1 is default and uses bilinear interpolation for the bright areas very fast ipm 2 uses triangulation very slow ihm 0 1 2 specifies the dehazing option ihm 1 standard dehazing ihm 2 stronger dehazing ihm 0 both options are executed the results contain the identifier dh1 and dh2 respectively Afterward the better result is renamed with dh and the inferior result is deleted if ikeep 0 or if this keyword is not specified If scene bsq is the name of the input image then the
263. name xxx with the corresponding spectral response files rsp and a high resolution solar irradiance file from the atcor sun_irradiance directory example e0_solar_kurucz2005_04nm dat CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 171 Output is a new sensor subdirectory example sensor xxx_kurucz2005 where the first 10 char acters of the e0_solar_kurucz2005_04nm starting after the e0_solar_ are appended to the input sensor name The contents of the input atcor sensor xxx are copied to the output directory e0_solar_xxx spc is deleted in the output directory and replaced by the new e0_solar_xxx_kurucz2005 spc A comparison of e0_solar_xxx spc with e0_solar_xxx_kurucz2005 spe shows the influence of the change of the irradiance spectrum In addition a new atm_lib xxx_kurucz2005 is created where all the LUTs atm from the input atm_lib xxx are replaced with the resampled selected irra HRs diance spectrum This new folder also contains a file irrad source txt identifying the selected irradiance source 9 3 Supported I O file types Below a list is compiled of all input and output files to the atcor main process 9 3 1 Main Input The input image to ATCOR 4 must have the band sequential BSQ ENVI format Several data types exist for the encoding The following data types of an input image are supported e byte or unsigned 8 bi
264. ncluding proper treatment of cloud areas 4 spherical albedo correction The retrieval of surface temperature and emissivity includes the maps of visibility index water vapor if water vapor bands exist elevation and scan angle No slope aspect correction is performed in the thermal region CHAPTER 10 THEORETICAL BACKGROUND 243 10 8 Accuracy of the method There is not a single figure that can be given to summarize the accuracy for all situations because the radiometric accuracy of the method depends on several factors the calibration accuracy of the sensor the quality of geometric co registration of the spectral bands the algorithm for ortho rectification relying on auxiliary information such as attitude and GPS DGPS the accuracy of the radiative transfer code MODTRAN 5 the correct choice of atmospheric input parameters the terrain type flat or rugged and the surface cover Solar region In the solar region wavelength lt 2 5 um assuming a flat terrain and avoiding the specular and backscattering regions an accuracy of the retrieved surface reflectance of 2 for reflectance lt 10 and 4 reflectance units for reflectance gt 40 can be achieved 65 For rugged terrain the most important parameter is an adequate spatial resolution of the DEM or DSM digital surface model and the exact ortho rectification of the imagery It would be desirable to have a DEM of a quarter of the sensor s spatial resolution or at
265. nd sampling distance agrees with the template spectra in the sun_irradiance directory of ATCOR Currently irradiance spectra of Kurucz 1997 Kurucz 2005 distributed with MODTRAN 8 and Fontenla 2011 are offered The plots show the detailed information line structure contained in the Fontenla spectrum The curves with 2 8 nm and 10 nm represent results based on a moving average of the the 0 4 nm data over 7 and 25 spectral points respectively 251 APPENDIX A COMPARISON OF SOLAR IRRADIANCE SPECTRA Relative Difference Relative Difference black FWHM 0 4 nm green FWHM 2 8 nm red PWHM 19 0 nm 100x K1997 F2011 F2011 0 4 0 5 0 6 0 7 0 8 0 9 Wavelength jam black FWHM 0 4 nm green FWHM 2 8 nm red FWHM 10 0 nm 100x K1997 F2011 F2011 1 0 Ez 1 4 1 5 1 8 2 0 ZE Wavelength jam 252 giaa 2 4
266. nd surface energy fluxes Baret and Guyot 1991 Choudury 1994 The normalized difference vegetation index NDVI is defined as NDVI 2850_ P650 7 1 P850 P650 where p650 and pg5p are surface reflectance values in the red 650 nm and NIR 850 nm region respectively The soil adjusted vegetation index SAVI is defined as Huete 1988 Baret and Guyot 1991 with L 0 5 ps50 p650 1 5 SAVI pss50 p650 0 5 7 2 The leaf area index LAI can often be approximated with an empirical three parameter relationship employing a vegetation index VI SAVI or VI NDVI VI ao ay exp ag LAT 7 3 151 CHAPTER 7 VALUE ADDED PRODUCTS 152 Solving for LAI we obtain 1 VI LAI non ag at 7 4 Sample sets of parameters are ay 0 82 a 0 78 a2 0 6 cotton with varied soil types ag 0 68 a 0 50 a2 0 55 corn and a9 0 72 a 0 61 a2 0 65 soybean with VI SAVI Choudury et al 1994 Note Since it is difficult to take into account the parameters for different fields and different seasons it is suggested to use a fixed set of these three parameters for multitemporal studies Then the absolute values of LAI may not be correct but the seasonal trend can be captured Plants absorb solar radiation mainly in the 0 4 0 7 wm region also called PAR region photo synthetically active radiation ASRAR 1989 The absorbed photosynthetically active radiation is called APAR and the fraction of abs
267. ne the term hemispherical conical reflectance factor HCRF is also used but for small instantaneous field of view sensors directional is a sufficiently accurate geometrical description However for simplicity we will use the abbreviation reflectance in this manual In spectral regions dominated by scattering effects the terms of equation 10 1 are calculated with the scaled DISORT option discrete ordinate radiative transfer 54 in regions with strong atmospheric absorption the more accurate correlated k algorithm is used in combination with CHAPTER 10 THEORETICAL BACKGROUND 190 DISORT 7 The results are stored in look up tables LUT Since MODTRAN calculates the path radiance including the diffuse reflected ground radiation in the form _ Taig Eg 0 p m Lpatn p Lpatn 0 1 1 ps Lpath 0 Taif Eg p p r 10 2 two MODTRAN runs with surface reflectance p 0 and p 0 15 are required to calculate the diffuse ground to sensor transmittance Tg and spherical albedo s from equation 10 2 eee Lath Pr Lath 0 T i pr Eq pr 10 3 Eq pr E 10 4 E 0 s 1 E p 10 5 For image data the pixel reflectance p may differ from the background reflectance p In this case the signal at the sensor consists of three components as sketched in Fig 10 4 e component 1 scattered radiance path radiance e component 2 radiation reflected from pixel under consideration e component 3 radiation
268. nearly mapped from the unscaled min Pmax interval onto the physically scaled 0 1 interval where the scaled shadow function is named gra Ue ik lt a 10 111 Pmaz Pmin d 1 if D gt Do 10 112 The smallest value of the scaled shadow function is P 0 which means no direct illumination However to avoid overcorrection and to cope with scenes containing merely partial shadow areas it is advisable to set gt at a small positive value This value of 7 i e the minimum fractional direct illumination deepest shadow in a scene typically ranging between 0 05 and 0 10 is scene dependent see the detailed discussion below histogram 1 0 j 1 0 0 5 0 0 0 5 1 0 unscaled shadow function Figure 10 22 Normalized histogram of unscaled shadow function In principle the de shadowing could now be performed with the physically scaled function which represents the fraction of the direct illumination for each pixel in the p vector i e the complete scene without cloud and water pixels However since the matched filter is not a perfect shadow transformation it is much better to restrict its application to the potential most likely shadow areas This is an important processing step to reduce the number of mis classifications or false alarms If omitted it will cause strange shadow pixels scattered all over the image An example can be found in the central part of Fig 10 23 where the standard s
269. ness difference Interpolate Borders The border pixels are interpolated from their neighbours overwriting the original values Shadow Border Width the with of the border to be corrected interpolated Output A cube containing the filtered image data is generated and the ENVI header is copied to the new file CHAPTER 5 DESCRIPTION OF MODULES 119 Figure 5 67 Shadow border removal tool CHAPTER 5 DESCRIPTION OF MODULES 120 5 7 Menu Simulation The Simulation menu provides programs for the simulation of at sensor radiance scenes based on surface reflectance or emissivity and temperature images Xx Airborne ATCOR File Sensor Topographic ATCOR ERDF Filter Simulation Tools Help Licensed for Daniel Yer TORA At Sensor Radiance Cube input reflectance TOA At Sensor Thermal Radiance input emissivity temperature At Sensor Apparent Reflectance Resample Image Cube n channels gt m lt n channels Figure 5 68 Simulation modules menu 5 7 1 TOA At Sensor Radiance Cube This routine calculates an At Sensor Radiance Cube from an reflectance image cube All parameters used for the processing are generated from the inn file of the input cube If the function is called the cube is opened and the x inn file is read which results in an at sensor cube _toarad bsq Note that this routine does not consider adjacency effects and is a simple forward propagation based on the given parameters and the given standard
270. nge i e the center wavelength and spectral response curve of each channel are valid as obtained in the laboratory or it was already updated as discussed in chapter 2 2 The radiometric calibration uses measured atmospheric parameters visibility or optical thickness from sun photometer water vapor content from sun photometer or radiosonde and ground re flectance measurements to calculate the calibration coefficients cy c of equation 2 7 for each band For details the interested reader is referred to the literature Slater et al 1987 Santer et al 1992 Richter 1997 Depending of the number of ground targets we distinguish three cases a single target two targets and more than two targets Calibration with a single target In the simplest case when the offset is zero cy 0 a single target is sufficient to determine the calibration coefficient c1 L a DN Lpatn Tp Eg T 2 21 Lpath T and E are taken from the appropriate LUT s of the atmospheric database p is the measured ground reflectance of target 1 and the channel or band index is omitted for brevity DNf is the digital number of the target averaged over the target area and already corrected for the adjacency effect Solving for c yields L _ Lpath Tp E T DN DN c 2 22 Remark a bright target should be used here because for a dark target any error in the ground reflectance data will have a large impact on the accuracy of c1 C
271. ngths This feature is also available for bath processing sp_calth see chapter 6 3 Figure 5 80 Thermal Spectral Calibration Another module provides the capability of radiometric calibration for thermal band imagery ther malcal if the scene contains water bodies In this case the theoretical spectral water emissivity is used and the user has to specify the file name of the input scene the water target pixel coordinates xpos ypos and the box size Output is the new calibration gain cl assuming that the offset c0 is zero for all bands see chapter 6 3 A related subject is the estimation of the water vapor column from purely hyperspectral imagery Here the water vapor is estimated by automatically selecting 10 pixel spectra from the scene covering a temperature interval Tmin Tmax but avoiding extreme temperatures Then the difference between measured and simulated at sensor radiance spectra for these 10 pixels is calculated and CHAPTER 5 DESCRIPTION OF MODULES 134 the water vapor LUT which minimizes the difference is recommended for the processing see chapter 6 3 5 8 9 Create Scan Angles Creates a scan angle file from input geometry Two procedures are supported a Prefered If a MAP file or an ENVI GLT file is available for the imagery the scan angle file may be created from the geometric information contained in that file This results in a pixel accurate scan angle files If no MAP GLT
272. ning the unbinned spectral response is required as an input Outputs A numbered series of band_x rsp files are written to the path indicated by the output directory The files contain wavelength reference and the relative response in two ASCII formatted columns 000 IX Generate Spectral Filter Functions V3 0 2015 Envi Header hdr or 3 Columns band number center wavelength bandwidth micron or nm Wavelength File src_idl atcor atcor_4 deno_data Husper_ATCOR_demo FL3_WNIR_geo hdr Select Type of Filter Function w 1 Butterworth order 1 slow drop off wv 3 Butterworth order 3 between Gauss rectangular y 4 3 Butterworth order 4 close to rectangular wv 5 Gauss y 6 Rectangular w7 Triangular wv 8 Shape changes from near rectangular first bands to triangular last bands due to binning Spectral Binning Factor fa Dutput Directory Varc_idl atcor atcor_4 sensar Hyspex_WNIR_1800 HELP Generate Filter Files rsp QUIT Figure 5 15 Spectral Filter Creation CHAPTER 5 DESCRIPTION OF MODULES 73 5 2 3 Apply Spectral Shift to Sensor This is a tool to shift the spectral response of a predefined sensor or to change the FWHM values of the sensor based spectral position and FWHM detection results from the ATCOR smile detection routine Inputs e input sensor definition file sensor_ dat e smile file from smile detection routine or FWHM file smile_poly_ord4 dat or smile_poly_or
273. nsor xx or specl_tile input filename sensor rx nte ntx nty nty The spectral classification based on template reflectance spectra is also available in the batch mode and with the tiling option The xx is a keyword for the sensor type e g xx hymap04 The complete list of sensor keywords is shown when typing specl_batch on the IDL command line without the sensor specification The ntx nty keywords have the meaning explained for the ATCOR tile programs above e smile_interp4_batch input filename fpoly fpname option number silent silent Purpose The atmospheric correction accounts for the column dependent smile shift as spec ified in the smile_poly_ord4 dat of the corresponding sensor folder but the image columns of each band belong to slightly different wavelengths This function interpolates the pixel reflectance values for each band to a specified reference wavelength Three options exist for the reference wavelength grid 1 use wavelength corresponding to the center of the detector array 2 use average wavelength over all detector columns per band 3 use nominal wavelength specified in the ENVI header of the reflectance cube CHAPTER 6 BATCH PROCESSING REFERENCE 145 The new reference center wavelengths are included in the header of the output file If the input filename is path1 image_atm bsq the output name is path1 image_atm_smcorr bsq indicating the smile corrected commo
274. nsor radiance DN standard dev DN Radiance is in mWem sr ywm The standard deviation indicates the spatial uniformity within the box e angles sun month day hour minute lat lon Calculate solar position from parameters the keyword are self explanatory The output angles is an array containing zen azi doy Zenith angle and azimuth angle in degrees and day of the year as a number CHAPTER 6 BATCH PROCESSING REFERENCE 150 e at_cresca reffile startpoint endpoint fov frawdims scafile Create scan angle file from parameters PARAMETERS reffile Reference file for output dimensions startpoint first point of flightpath x y z endpoint last point of flightpath x y z fov total FOV of image in degrees rawdims n_pixels n_lines of raw image as two element array KEYWORDS scafile name of output scan angle file default input _sca bsq e gc_gittosca gltfile fov heading alt scafile Create scan angle file from GLT PARAMETERS gltfile GLT reference file in BIL format or MAP file in BSQ format fov total FOV of image in degrees heading average heading to direction north of flight north 0 east 90 alt altitude of aircraft for first and last line two element vector if only one value is provided it is assumed to be constant KEYWORDS scafile name of output scan angle file default input _sca bsq Chapter 7 Value Added Products As a by product of atmospheric c
275. ntains the pressure during the measurement of the spectral response functions in the lab and the instrument pressure during the flight The latter value is preceded by R relative or A absolute Example 1 of file pressure dat 1013 0 RO 0 The first value is the lab pressure mbar the second value RO 0 means the pressure relative to the ambient pressure at the flight altitude is zero i e the instrument is exposed to the ambient pressure Example 2 of pressure dat CHAPTER 1 INTRODUCTION 14 940 0 R200 0 This means the lab measurements were conducted at 940 mbar and during flight the instru ment maintains a pressure of 200 mbar above ambient Example 3 of pressure dat 940 0 A13 0 This means the lab measurements were conducted at 940 mbar and during flight the instru ment maintains an absolute pressure of 13 mbar independent of flight altitude If no file pressure dat exists the program will write a default file with lab pressure 1013 mbar and instrument pressure RO 0 i e the instrument is exposed to the ambient flight altitude pressure which is the usual case see chapter 2 3 for details e The high resolution database is updated based on MODTRAN5 3 3 and HITRAN 2013 in stead of the previous HITRAN 2009 e The column water vapor W 5 cm is included in the high resolution database i e the bp7 and bt7 files covering a larger water vapor range e The thermal
276. ntents a value of zero is a complete cast shadow whereas a value of 1 is in full illumination corresponding to the definition of the cos B of the incidence angle where 8 Odeg is a 90 degree incidence direction Calibration file extension sensor cal Calibration file containing wavelength c0 and cl for each spectral band for conversion of image data to calibrated radiance L c0 cl x DN units mW cm sr um Format 3 column ASCII one header row Response Function extension bandxxx rsp Spectral response function for one spectral band with wavelength reference and relative re sponse Format 2 column ASCII no header Sensor Description extension sensor_ type dat Sensor description file as created by the Function File New Sensor Format ASCII Solar Reference extension e0_solar_ type dat Solar reference function for the sensor as created when compiling the atmospheric LUT for a sensor Format ASCII three columns center wavelength bandwidth extraterrestrial solar irradiance CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 173 9 3 3 Main output The default output image data type is byte if the input is byte data Then a scale factor s 4 is employed i e the per cent reflectance value of each pixel is multiplied with s 4 and rounded to byte As an example a surface reflectance value of 20 2 will be coded as 81 However the user can modify the scale factor on ATCOR s main panel A value of s 10 to s
277. ntion Both the DEM and the image should be represented in the same coordinate system and cover the same area If the DEM is too small the remaining parts of the image are filled with a zero value 5 3 3 Slope Aspect Slope and aspect are to be calculated from the DEM before ATCOR is run for rugged terrain This menu function as depicted in Fig 5 23 allows to calculate both layers in one step 00 Slope and Aspect Calculation V 2 0 DEM File may have 16 or 32 bit integer or float data Input DEM FILE Verc_1d1 atcor atcor_23 deno_data tn_rueged tn_blforest_ele bsq QUIT SLOPE File Ysre_idl atcor atcor_22 deno_data tn_rugged tn_blforest_slp bsq OVERWRITE ASPECT File src_idl atoor atoor_28 deno_data tn_rugged tn_blforest asp bsq Kernel size box for averaging 1B DEM resolution x y pixel size meters 30 0 DEM height z unit An yim vem k RYN k hessaces Figure 5 23 Slope Aspect Calculation panel Inputs Input DEM file The standard DEM file used for atmospheric correction This DEM should be in meters CHAPTER 5 DESCRIPTION OF MODULES 80 Output file names The names for the output files are entered automatically and can t be changed as ATCOR asks these files to be named exactly according to the conventions Kernel Size Box Size of the kernel in number of pixels the slope and aspect is calculated as gradient of the pixels at the edges of this box the default value is 3 pix
278. nts in the image These effects can be removed with an empirical method that normalizes the data to nadir reflectance values In addition for rugged terrain areas illumi nated under low local solar elevation angles these effects also play a role and can be taken care of with an empirical method included in the ATCOR package The ATCOR software was developed to cover about 80 of the typical cases with a reasonable amount of coding It is difficult if not impossible to achieve satisfactory results for all possible cases Special features of ATCOR are the consideration of topographic effects and the capability to process thermal band imagery There are two ATCOR models available one for satellite imagery the other one for airborne imagery 71 72 An integral part of all ATCOR versions is a large database containing the results of radiative transfer calculations based on the MODTRAN 5 code Berk et al 1998 2008 While ATCOR uses the AFRL MODTRAN code to calculate the database of atmospheric look up tables LUT the correctness of the LUTs is the responsibility of ATCOR Historical note For historic reasons the satellite codes are called ATCOR 2 flat terrain two geo metric degrees of freedom DOF 59 and ATCOR 3 three DOF s mountainous terrain 62 They support all operationally available small to medium FOV optical and thermal satellite sensors with 12 CHAPTER 1 INTRODUCTION 13 a sensor specific atmospheric dat
279. o using float data before resampling it is recommended to calcu late the slope aspect maps on the original coarse spatial resolution data followed by the high resolution resampling step for all DEM files elevation slope aspect Do not employ the sequence of resampling the elevation followed by a slope aspect calculation of the high resolution elevation map because this approach enhances artifacts Steps to reduce slope aspect striping FLOAT ELEVATION file if it is stored as integer Calculate SLOPE and also ASPECT with a low pass filter of 5x5 pixels Resize SLOPE ASPECT file factor 4 larger Apply a lowpass filter 7x7 pixels o A WwW N Fe Resize with factor 0 25 using nearest neigbor to obtain original size Chapter 10 Theoretical Background Standard books on optical remote sensing contain an extensive presentation on sensors spectral signatures and atmospheric effects where the interested reader is referred to Slater 1980 89 Asrar 1989 4 Schowengerdt 2007 86 This chapter contains a description of the concepts and equations employed for the atmospheric correction We start with the basic equations in the solar and thermal spectral region for clear sky conditions standard case then move on to non standard conditions comprising bidirectional reflectance BRDF effects hazy scenes and a treatment of shadow areas caused by clouds or buildings Stan dard atmospheric conditions include the opt
280. ocessing of airborne imaging spectrometry data Part 2 atmospheric topographic correction Int J Remote Sensing Vol 23 2631 2649 2002 Richter R and M ller A De shadowing of satellite airborne imagery Int J Remote Sensing Vol 26 3137 3148 2005 Richter R Schl pfer D and Miller A An automatic atmospheric correction algorithm for visible NIR imagery Int J Remote Sensing Vol 27 2077 2085 2005 Richter R Bachmann M Dorigo W Mueller A Influence of the adjacency effect on ground reflectance measurements IEEE Geoscience Remote Sensing Letters Vol 3 565 569 2006 Richter R and Schlapfer D Considerations on water vapor and surface reflectance re trievals for a spaceborne imaging spectrometer IEEE Trans Geoscience Remote Sensing Vol 46 1958 1966 2008 Richter R Kellenberger T and Kaufmann H Comparison of topographic correction methods Remote Sensing Vol 1 184 196 2009 Richter R and D Schl pfer Atmospheric topographic correction for satellite imagery ATCOR 2 3 User Guide DLR IB 565 01 13 Wessling Germany 2013 Richter R and D Schl pfer Atmospheric topographic correction for airborne imagery ATCOR 4 User Guide DLR IB 565 02 13 Wessling Germany 2013 Richter R Schlapfer D and M ller A Operational atmospheric correction for imaging spectrometers accounting the smile effect
281. of Equator Longitude to 360 is East of Greenwhich negative West of Gr Longit 90 equivalent to Longit 270 RESULTS Solar Azimuth 171 3 Solar Elevation 65 10 Solar Zenith 24 90 degree Solar Azimuth North 0 East 90 Solar Elevation sunrise 0 Solar Zenith sunrise 90 Day of Year 170 Error message 1 DO Calculation QUIT Figure 5 71 Calculation of sun angles CHAPTER 5 DESCRIPTION OF MODULES 123 5 8 2 Classification of Surface Reflectance Signatures The spectral classification SPECL is a hierarchical classifier based on surface reflectance data employing the Landsat Thematic Mapper TM wavelengths It is not a land use classification but a classification into certain spectral categories e g dark and bright bare soil and different vegetation classes see figure 5 72 The following set of rules is used where b1 b2 b3 b4 b5 and b7 indicate the surface reflectance in the TM bands 0 48 0 56 0 66 0 84 1 65 2 2 wm respectively or the nearest corresponding channel snow b4 b3 lt 1 3 and b3 gt 0 2 and b5 lt 0 12 cloud b4 gt 0 25 and 0 85 lt b1 b4 lt 1 15 and b4 b5 gt 0 9 and b5 gt 0 2 bright bare soil b4 gt 0 15 and 1 3 lt b4 b3 lt 3 0 dark bare soil b4 gt 0 15 and 1 3 lt b4 b3 lt 3 0 and b2 lt 0 10 average vegetation b4 b3 gt 3 0 and b2 b3 gt 0 8 or b3 lt 0 15 and 0 28 lt b4 lt 0 45 bright vegetation
282. of a variable scene visibility npref 1 the vis parameter is ignored if the scene contains enough dark reference pixels If not the program switches to the constant visibility mode and vis is used as a start value A negative vis value means the value abs vis is used for processing even if it causes a large percentage of negative reflectance pixels e atcor r_batch input filename output file vis vis or atcor4r_tile input filename ntr 3 nty 2 output file vis vis The r in atcor4r_batch means the code for rugged terrain i e a DEM is employed as well as other DEM related files e g slope aspect skyview Otherwise the same explanations hold as for the flat terrain ATCOR The corresponding tile program atcor4r_tile in this example is called to split the image into 3 sub images in x direction and 2 in y direction compare chapter 6 2 The keywords output and vis are described in atcor4f_batch above e Note optional keywords for atcor4f_batch atcor4r_batch atcor4f_tile atcor4r_tile There are four keywords concerning spectral interpolation to overwrite the interpolation set tings in file preference_parameters dat 1725 1 no interpolation for 725 820 nm channels i725 1 interpolation 1760 1 no interpolation for 760 nm channels i760 1 interpolation 1940 1 no interpolation for 940 nm channels i940 1 nonlinear interpolation i940 2 linear i1400 1 no interpolation for 1400
283. of all bands of a sensor being present as a spectral filter file response file rsp For spectroscopic instruments the band characteristics are often only available by band center and width at FWHM Full width half maximum This function creates the response curves from the latter information compare Fig 5 15 Inputs Wavelength File An ENVI header file standard hdr or a wavelength reference file Format of ASCII File 3 columns no header column 1 band number column2 center wavelength column 3 band width Unit nm or um same for columns 2 and 3 Note if the FWHM is not contained in the ENVI header or in the ASCII file a bandwidth resolution is assumed which corresponds to 1 2 times the spectral sampling interval CHAPTER 5 DESCRIPTION OF MODULES 72 Type of Filter Function The type defines the basic shape of each of the created response curves Options are Butterworth Order 1 slow drop off Butterworth Order 2 close to Gauss Butterworth Order 3 between Gauss Rect Butterworth Order 4 close to Rectangular Gaussian Rectangular Triangular Decreasing Binning from Rectangular to Triangular Spectral Binning Factor This factor allows binning of channels e g a factor 4 will combine four spectral response functions to produce the equivalent new channel filter function This is a convenient feature for programmable instruments e g HySpex or Specim AISA For using this function a wavelength reference file contai
284. of them are listed in chapter 5 4 10 They are mostly self explaining and are not discussed here They also contain default settings which can be used in most cases When the main panel Fig CHAPTER 4 WORKFLOW 41 Blocked Options Are Not Available For The Selected Sensor Might also apply for a reduced set of bands Either Haze or Cirrus Removal not both Blocked Options Are Not Available For The Selected Sensor ES Variable Visibility aerosol optical thickness Q Yes No Variable Visibility aerosol optical thickness Yes No Variable Water Vapor ssessssssesese rrsrnsorcrorssss ses OYes No ted dais Y tes O to Haze or Sun Glint Removal ccoorrsrrcccronsrorsrn soe O Yes ONo Haze or Sun Glint ROMWal ss ccsessssssscssessseeesese ese Yes Q No Shadow Removal Clouds Buildings csseceeeceereeees Y Yes No Shadow Removal Clouds Buildings sssesesseeessseee Y Yes No Value Added Products ssecsccsseroceereceserscescee Y Yes No Value Added Products ooorrrsorcroncorerrorsorsrrroronss Q Yes No Cirrus Removal ssseseeseseseeeresseseseeeeseseseresses Yes No Solar Flux at Ground oororoccororcrorosserocconcorssss Q Yes No Solar Flux at Ground scccosccccecsscccccvcessccsceece Q Yes No Figure 4 7 Image processing options Right panel appears if a cirrus band exists Update DEM Path Path Yauto_as data atcor43 demo_data gruber Mandatory Files Elevation puto tysens ele DEM height
285. olation in the 1400 nm region start stop wavelengths for interpolation in the 1900 nm region haze sun glint over water apparent NIR reflectance T clear T gt haze reduce over under correction in cast shadow 0 no 1 yes keep atmi files in sensor specific atm lib 0 no 1 yes Note on the non linear influence of vegetation in water vapor calculations This option applies to the APDA regression algorithm and only if the 940 nm region is selected for the water vapor retrieval The retrieval is based on a linear interpolation across the absorption region and errors can occur due to the non linear behavior of the reflectance of vegetated surfaces in this region A simple empirical correction to the water vapor map W is applied using the NDVI calculated with the apparent reflectances in the red NIR channels W new W old 0 1 NDVI 0 7 cm 9 2 The correction is only performed for pixels with N DVI gt 0 25 and values NDVI gt 0 7 are reset to 0 7 CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 176 Note on cut off limit for surface reflectance The default limit is set to a high value 150 reflectance to avoid a truncation in spectra of high reflectance surfaces e g snow and or surfaces with specular reflectance The previous default cut off before 2012 was set to 90 Note on factor b Factor b is a relative saturation factor applied to the maximum radiometric encoding e g for 8 bit data
286. olynomial 5 deg image based HDRF functions for each class cross track combinations in HDRF LUT best fit of absolute deviation from mean RMS f_vol f_geo f_iso for each band BCI level exclude outliers bad fits and incomplete levels level averages over all scenes parameter set for correction Figure 10 27 BRDF model calibration scheme Model calibration For the calibration of the model the BCI is divided into a number of 4 6 discrete classes The evaluation has shown that increasing the number of classes often leads to worse fitting results and less stable BRDF correction whereas keeping the number of classes small is more stable A second outcome was that it is hardly feasible to define generic class limits for any kind of data acquisition and sensor The calibration follows the scheme shown in Figure 10 27 Differences in limits can be attributed to the fact that the higher resolution images allows for a more accurate and statistically more relevant calibration of the model whereas for lower resolution the number of classes should be reduced The classes can be denominated as water artificial materials soils sparse vegetation grassland and forests For each of the classes the optimum kernel weights are calculated and stored for each image of a campaign All weights are then averaged while bad fitting classes with relative errors greater than 10 are excluded from averaging No BRDF co
287. on or when a batch job is submitted all input parameters are read from this file It is suggested not to edit this file because it might create inconsistent input data The file might contain empty lines when input is not required for a specific case The IDL routine for writing this file is available on request for users who want to run ATCOR batch jobs without employing the interactive GUI panels The contents of an inn file are line 1 20 08 1989 Date dd mm year i e 20 08 1989 occupies the first 10 columns CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 178 line 2 100 0 scale factor reflectance 0 100 range The default reflectance scale factor in the output file is s 100 for 16 bit data and s 4 for 8 bit data i e a reflectance value of say 21 75 is coded as 2175 and 87 for s 100 and s 4 respectively For 8 bit input data and s 4 reflectance values above 63 75 are truncated at 255 If higher values are expected a scale factor of s 100 should be selected For s 1 the output file will be coded as 32 bit float data requiring twice the disk storage as for s 100 With s gt 0 negative reflectance values are truncated to 0 With s 100 or s 1 the negative values are not truncated This option may sometimes be useful to identify areas in the scene where overcorrection exists This is usually caused by a too low visibility or in shadow areas line 3 5 0 pixel size m line 4 casi04 casi04 name of at
288. on USGS_Feb11 subset HYP11Nov2009_167_bsq_smi_atm bsq Smile polynomial file Ysrc_idl atcor atcor_23 sensor hyperion_167_smi le smile_poly_ord4 dat Options w 1 reference wavelength grid wavelength for center of detector array for each band wv 2 reference wavelength grid wavelength average over all detector columns per band 3 reference wavelength grid nominal position center wavelengths of ENVI header gt SSS QUIT Figure 5 66 Spectral smile interpolation This routine is used after smile dependent atmospheric correction It applies a linear interpolation on the reflectance data in order to bring the spectral bands to a common reference in across track direction The inputs are as follows see Fig 5 66 Inputs Input Image A hyperspectral image cube usually the output of atmospheric correction in smile mode x_atm bsq Smile polynomial file The file smile_poly_ord4 dat as of the sensor definition used for the smile aware atmospheric correction CHAPTER 5 DESCRIPTION OF MODULES 118 Options Three options for the spectral reference wavelength grid to be used for interpolation may be selected center of detector array The spectral position of the center pixel in across track direction of the detector is taken as the reference wavelength for each spectral band average over all detector columns For each spectral band the average of all smiled center wavelengths is calculated
289. on if the chances are not good This automatic haze termination works in most cases but a success cannot always be guaranteed The two cirrus cloud classes are merged with the normal cloud class for the surface reflectance retrieval during the treatment of the adjacency effect here the reflectance of the cloud pixels is replaced with the average reflectance of the non cloud pixels to avoid an overestimation of the adjacency effect The classes are currently defined with the following criteria Water class If the surface elevation of a pixel is lower than 1 2 km above sea level and the flight altitude higher than 10 km then a water pixel has to fulfill the criteria p blue lt 0 20 and p blue gt p green 0 03 Pp NIR lt p green and p 1 6um lt Twater SWIR1 10 48 where Tivater sw 1r1 is the water threshold reflectance in the SWIR1 band around 1 6 jum as de fined in the preference parameter file see chapter 9 4 Basically the gradient of the apparent water reflectance has to be negative If the pixel elevation is higher than 1 2 km or the flight altitude lower than 10 km the criterion of a negative gradient for the apparent reflectance does not properly work as the path radiance in the visible especially in the blue becomes small and the following criterion based on surface reflectance instead of apparent reflectance is used p NIR lt Twater NIR and p SWIRI lt Tater STR 10 49 where Tuvater y 1R
290. on on ATCOR s main panel to execute the aerosol type retrieval irrespective of the atm name in the inn file line 15 temfile atmospheric LUT file name thermal region empty if no thermal band line 16 1 0 adjacency range km line 17 35 0 visibility km line 18 0 7 mean ground elevation km asl not used in case of rugged terrain where elevation file applies line 19 33 0 178 0 solar zenith solar azimuth angle degr line 20 4 2 230 0 flight altitude km asl heading deg north line 21 0 0 1 0 0 0 1 npref iwaterwv ihaze iwat_shd ksolflux ishadow icl_shadow seven parameters controlling the processing options npref 0 constant visibility npref 1 variable visibility based on dark reference areas in the scene npref 1 variable visibility for each sub image during batch job with tiling iwaterwv 0 no water vapor correction or no water vapor bands available iwaterwv 1 water vapor correction using bands in the 940 nm region iwaterwv 2 water vapor correction using bands in the 1130 nm region iwaterwv 3 940 and 1130 nm bands are employed CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 180 e Haze removal is enabled by setting the parameter haze gt 0 no haze removal is specified with ihaze 0 Separate parameter values define haze removal over land haze sunglint removal over water and the combination Some criteria exist to check whether th
291. onal header with text This wavelength file will be used to create the spectral response function for each band as a numerical table the band rsp files compare Fig 5 13 Eight analytical filter shapes can be selected from the top level graphical interface Fig 4 1 when selecting the menu Sensor then choose Create Channel Filter Files button Then the menu of Fig 4 12 will pop up and one of these 8 filter shapes can be selected Filter numbers 1 to 4 are of the Butterworth type the slow drop off for the Butterworth order 1 is truncated at the 0 05 response and set to zero The filter type 9 parameter filter_type in Table 4 3 is reserved for arbitrary user specified channel filter functions e A calibration file has to be provided e g hymap2003 cal in the new sensor sub directory e The RESLUT resample atmospheric LUTs program has to be run to generate the atmo spheric LUTs for the new sensor employing the monochromatic atmospheric database in atcor4 atm_database These resampled atm files will automatically be placed in a sub directory of atcor4 atm_lib with the name of the selected sensor RESLUT will also create the resampled spectrum of the extraterrestrial solar irradiance in the appropriate CHAPTER 4 WORKFLOW 46 sensor hymap2003 folder see chapter 9 2 e g e0_solar_hymap2003 spc In addition the first use of RESLUT will create the pressure dat file
292. opographic ATCOR BRIF Filter Simulation Tools BREFCOR Correction 7 0 0 c DLR ReSe 2015 Nadir Normalization BRDF Model Analysis BRDF Model Plot Licensed for Daniel Mosaicking Figure 5 52 BRDF top Menu 5 5 1 BREFCOR Correction This module calculates an observer BRDF correction using a model based approach see chapters 2 6 10 6 3 Figure 5 53 shows the corresponding GUI panel The BREFCOR is implemented for the airborne version of ATCOR for sensors with a large FOV It works best if a number of scenes can be taken for model calibration but it may also lead to good results for single scenes as long as the statistics are uniform within scene The Ross Thick Li Sparse reciprocal BRDF model kernels are used for correction of the imagery The various viewing angles of the individual images provided are used as reference points to calibrate the kernel weighting factors and the isotropic component of the BRDF models This model requires a sufficient number of images for good calibration as a variety of incidence and viewing angles are required The following inputs are necessary Inputs Files A list of atmospherically corrected input files has to be compiled _atm bsq The ATCOR inn file is used for the meta data information Also a scan angle file is required for each of the images i e _sca bsq Both files should be named according to the ATCOR convention and situated at the same folder as the _atm file Files ar
293. or the thermal channels and contains the surface temperature as the last channel The corresponding emissivity data is stored as a separate file im age_atm_emiss bsq 16 bit integer which is scaled with a factor of 1 000 Program TOARAD2 corresponds to TOARAD but calculates thermal at sensor radiance imagery Again program HS2MS converts the hyperspectral imagery into multispectral scenes However the noise is now CHAPTER 8 SENSOR SIMULATION OF HYPER MULTISPECTRAL IMAGERY Radiance Resampling hs sensor ms sensor At Sensor Radiance al atmospheric parameters hs sensor surface oo reflectance Reflectance Resampling Figure 8 2 Sensor simulation in the solar region 161 specified in terms of NEAT noise equivalent delta temperature which is specified in K Kelvin For the emissivity resampling the noise equivalent emissivity NEAe is internally computed from the NEAT OLw 0T NEAc NEAT Lop 8 3 Since the temperature radiance relationship for thermal bands is calculated with an exponential fit equation see eq 10 36 Ly expl 7 a 0 we can express NEAe using T 300 K as NEAT NEAT bT2 6 3002 where b is a channel dependent coefficient NEAc Keywords for batch programs toarad toarad2 CHAPTER 8 SENSOR SIMULATION OF HYPER MULTISPECTRAL IMAGERY 162 Figure 8 3 Graphical user interface of program HS2MS
294. orbed photosynthetically active radiation is abbreviated as FPAR These terms are associated with the green phytomass and crop productivity A three parameter model can be employed to approxiate APAR and FPAR Asrar et al 1984 Asrar 1989 Wiegand et al 1990 1991 FPAR C 1 A exp B LAI 7 5 Typical values are C 1 A 1 B 0 4 Again since it is difficult to account for the crop and seasonal dependence of these parameters a constant set may be used for multitemporal datasets to get the typical FPAR course as a function of time The wavelength integrated surface reflectance in a strict sense the hemispherical directional reflectance weighted with the global flux on the ground E is used as a substitute for the surface albedo bi hemispherical reflectance It is calculated as 2 5um l POE AAA a 27 7 6 E J AJdA 0 3um Since most airborne sensors cover only part of the 0 3 2 5 um region the following assumptions are being made for extrapolation Extrapolation for the 0 30 0 40 um region P0 3 0 4um 0 8 Po 45 0 50um if blue a band 0 45 0 50 um exists e P0 3 0 4um 0 8 Po 52 0 58um green band no blue band available Extrapolation for the 0 40 0 45 um region P0 4 0 45um 0 9 Po 45 0 50um if a blue band 0 45 0 50 um exists e P0 4 0 52um 9 9 Po 52 0 58um green band no blue band available The reflectance reduction factors in the blue part of the spectrum account for the decrease of sur
295. ore running ATCOR with a DEM please check the results of the slope image We often encounter severe horizontal and vertical striping in the slope image in case of low quality DEMs or if coarse DEMs of 25 m have to be resampled to say 5 m Additional appropriate filtering is required in these cases A simple way might be to try a larger kernel size e g kernel 5 or kernel 7 A simple quality check on the derived DEM solar illumination file is also performed at the start of ATCOR see the discussion below e skyview_batch input filename pixelsize 10 0 dem_unit 0 unders unders azi_inc azi_inc ele_inc ele_inc filename is the full file name including the path filename should have the last four char acters as _ele and the extension bsq to indicate a digital elevation file and to enable an automatic processing e g example DEM25m_ele bsq pixelsize is specified in meters dem_unit is the integer code for the DEM height unit 0 represents m 1 means dm 2 means cm The option dem_unit 0 is default and can be omitted The keyword unders specifies the undersampling factor in pixels to reduce the execution time for very large files The default angular azimuth resolution is azi_inc 10 degrees and the default elevation incre ment is ele_inc 30 degrees However the recommended resolution is 10 degrees for azimuth and 5 degrees for elevation In case of large files an undersampling factor gt 1 can b
296. oretical background of atmospheric correction Information on the IDL version of ATCOR can be found on the internet http www rese ch What is new in the 2015 version e An all new haze removal algorithm has been added which works on the raw DN data by statistical analysis It can be used as a pre processing step to the atmospheric correction It works on the original digital numbers of Level 1 products While the previous dehazing algorithm is embedded in ATCOR and performs haze removal and atmospheric correction the new algorithm is independent and can also be run without a subsequent atmospheric correction Additionally an atmospheric correction can be conducted after dehazing This de hazing can be run as batch or from a GUI e The wavelength depends on the refractive index of air and thus on the pressure during lab measurements of the channel spectral response functions If the instrument is flown on an airborne platform and if it is exposed to the altitude dependent pressure level then the wave length of the spectral response functions has to be adapted Some instruments maintain their own pressure levels e g AVIRIS NG operates under near vacuum conditions 13 mbar An other example is the APEX spectrometer it has an internal pressure regulation unit which maintains a 200 mbar overpressure in relation to the ambient pressure To account for these effects the sensor folder now has an additional file pressure dat It co
297. orrection a number of useful quantities can readily be calculated The first group of value added products include vegetation indices based on surface reflectance instead of at sensor radiance simple parametrizations of the leaf area index and wavelength integrated reflectance albedo The second group comprises quantities relevant for surface energy balance investigations which are a useful supplement for studies in landscape ecology and related fields e g as input for regional modeling of evapotranspiration These include global radiation on the ground absorbed solar radiation net radiation and heat fluxes Emphasis is put on simple models based on the reflectance temperature cube derived during the atmospheric correction No additional data with the exception of air temperature is taken into account All value added products are written to a file with up to 11 bands The file structure is band sequential If the input file name is example bsq the output reflectance file name is example_atm bsq and the value added file name is example_atm_flx bsq the flx indicating the most important part of the calculation i e the radiation and heat fluxes 7 1 LAI FPAR Albedo Many vegetation indices have been introduced in the literature Only two are presented here because these are often used for simple parametrizations of the leaf area index LAI the fraction of absorbed photosynthetically active radiation FPAR a
298. ory to the IDL search path The graphical user interface of Fig 4 1 will pop up A large number of processing modules is available from this level as described in chapter 5 Most of them can be used without reading a detailed manual description because they contain explanations in the panels themselves However the next section guides the ATCOR newcomer during the atmospheric correction of a sample scene The functions in the File menu allow the display of an image file the on screen display of calibration files sensor response curves etc see Fig 4 2 More details about this menu are given in chapter 5 1 000 x Airborne ATCOR File Sensor Topographic ATCOR BRIF Filter Simulation Tools Licensed for Daniel Version 7 0 0 c DLR ReSe 2015 Sn Figure 4 1 Top level graphical interface of ATCOR The Sensor menu of Fig 4 1 contains routines to create spectral filter curves rectangular Gaussian etc from a 3 column ASCII file band number center wavelength bandwidth one line per channel provided by the user calculates atmospheric look up tables LUTs for new sensors and computes the radiance temperature functions for thermal bands see Fig 4 3 and chapter 5 2 The Topographic menu contains programs for the calculation of slope aspect images from a digital elevation model the skyview factor and topographic shadow Furthermore it supports the import and smoothing of DEMs and its related layers see chapter 5 3 3
299. ovides an estimate for the recommended aerosol type e g rural maritime urban derived from the scene This module also provides a visibility value for each aerosol type based on reference pixels dark vegetation in the scene The VISIB ESTIMATE CHAPTER 4 WORKFLOW 40 INPUT IMAGE FILE Yexport data data71 hym_040607 DPAF Ref 040607_0P_1_rad_sub2 bsq Date dd mm year 07 06 2004 Scan Angle File i no file required if original scan geometry not geocoded OUTPUT IMAGE FILE Pexport data data 1 hym_040607 0PAF Ref 040607_OP_1_rad_sub2_atm bsq L OVERWRITE Scale Factor 100 0 Help Flight and Solar Geometry Band selection Selected SENSOR HYMAPO4_NEW_DB Nadir pixel size m 5 00 CALIBRATION FILE export data data atcor43 sensor hymap04_new_db hymap04_ori_1000_4000 ATMOSPHERIC FILE 3 hO3000_wv10_rura ATM FILE for thermal band s Adjacency range km 0 30 Help Zones la Visibility km 50 0 SPECTRA AEROSOL TYPE VISIB ESTIMATE INFLIGHT CALIBRATION Help WATER VAPOR IMAGE PROCESSING Output file already exists change name or press OVERURITE MESSAGES QUIT Figure 4 6 ATCOR panel for flat terrain imagery button provides a visibility value for the selected aerosol type by checking dark scene pixels in the red band vegetation water and NIR band water It is assumed that the lowest reflectance in the red band is 0 01 1 percent and 0 0 in the NIR band
300. pply Shade Pixel Filter filters single pixels within full cast shadow areas e Write all Side Layers creates a file containing all indices calculated during the processing in 5 7 layers This function may be useful to find the appropriate range limits for a sensor system or imaging situation e Limits of Index The lower and upper boundary can be set in order to derive a continuous fractional shadow from index Larger values will result in large fractions of shadows being detected The range can not exceed 0 to 2 in any case default values are 0 5 to 1 Parameters used for terrain illumination and skyview estimate e Slope File Name Name of input Slope file to be used for illumination calculation the corre sponding _asp bsq file needs also to be present e Solar Zenith Azimuth solar angles zenith azimuth for this data set and optionally the flight altitude above ground airborne ATCOR version only A combined index is created based on NIR brightness and two red blue and green blue indices which results in a scaled number between 0 for full cast shadow and 1 for full illumination which is proportional to the cast shadow CHAPTER 5 DESCRIPTION OF MODULES 83 eoo IX ATCOR Shadow Detection Select Input File Names Youbes ads _temp TueDec101409512013_876 201208191022NRGBNO0A1308L2_0_0 bsq Calibration File Ysrc_idl atcor atcor_4 sensor ads80 ads_standard cal select Solar Reference File E0 src_idl atcor atcor_4 sensor ads80 e
301. processing option Format 1 band ENVI byte file containing the indices Water vapor Name outputname _wv bsq Columnar amount of water vapor calculated from image Format ENVI single band file 16 bit signed integer unit cm i e g cm2 1 000 i e scale factor 1 000 Illumination Name outputname _ilu bsq Illumination file created during processing in case no external floating _ilu has been pro vided Format Scaled illumination in byte format from 0 to 100 single band Haze cloud water Name outputname _out_hcw bsq Haze cloud and water mask Format ENVI byte image classes Fractional shadow Name outputname fshd bsq Fractional cloud building shadow optional Format ENVI Integer image Scaled shadow between 0 and 1000 Diffuse irradiance Name outputname _edif bsq Diffuse irradiance component 1 band 16 bit signed integer unit Wm nm Direct irradiance Name outputname _edir bsq Direct irradiance component 1 band 16 bit signed integer unit Wm 2nm Global irradiance Name outputname _eglo bsq Global irradiance sum of edir and edif 1 band 16 bit signed integer unit Wm nmt Value Added Vegetation Name outputname atm flx bsq Multi Layer file containing side outputs for vegetation i e flux fapar savi etc Format ENVI signed 16 bit integer with scale factors as specified in the header 9 4 Preference parameters for ATCOR The preference parameters are now located in a us
302. provided a narrow channel around 1 38 um exists e cloud over land cloud over water e snow requires a 1 6 wm channel e saturated pixels using the criterion T gt 0 9 DNmaz where T is a threshold set at 0 9 times the maximum digital number This criterion is only used for 8 bit and 16 bit signed or unsigned data no threshold is defined for 32 bit integer or float data As an example for an 8 bit pixel data encoding we obtain T 0 9 255 230 and pixels with a grey value greater than 230 are flagged as truly or potentially saturated Although the saturation nominally starts at DNmaz some sensors already show a non linear behavior around 0 9 x DNmaz so the factor 0 9 is a precaution to be on the safe side This saturation check is performed for two channels in the visible region blue band around 470 500 nm and a green band around 550 nm For multispectral sensors the blue band is usually the critical one concerning saturation For hyperspectral sensors with many blue bands the one closest to 450 nm is taken Although the haze cloud water file contains saturated pixels based on two visible bands the percentage of saturated pixels for all bands will be given in the corresponding log file However the check of the blue and green channel normally captures all saturated pixels The thresholds for the cirrus class definition are described in chapter 10 2 CHAPTER 4 WORKFLOW 52 Note cloud or building shadow pixels are not
303. qualization are available In the right part two windows are provided to display the cl spectrum and the box averaged target DN spectrum The ymin ymax widgets allow the user to scale the graphical display The parameters visibility and adjacency range can be varied and their influence on the calibration curve can be studied CHAPTER 5 DESCRIPTION OF MODULES 95 5 4 9 Shadow removal panels The interactive session of the de shadowing method enables the setting of three parameters that influence the results compare Figures 5 39 5 40 1 a threshold r for the unscaled shadow function PhiU to define the core size of the shadow regions see chapter 10 5 6 for details 2 the maximum range _ for re scaling the unscaled shadow function PhiU into the 0 1 interval of the scaled shadow function 3 the last parameter sets the minimum value of the scaled shadow function 6 PhiS typically in the range PhiS 0 02 0 10 i e the darkest shadow pixels of the scene are treated as being illuminated with a fraction PhiS of the direct solar irradiance histogram 1 0 0 5 0 0 0 5 1 0 unsealed shadow function Figure 5 39 Normalized histogram of unscaled shadow function The first two parameters are most important The last parameter is not very critical and a default value in the range PhiS 0 04 0 08 covers most cases of interest The linear type of re scaling of PhiU is recommened The exponential option e
304. r d takes into account the sun to earth distance d is in astronomical units since the LUTs with path radiance and global flux are calculated for d 1 in ATCOR Step 2 The second step calculates the average reflectance in a large neigborhood of each pixel range R 0 5 1 km 13 p N2 Dis Pig 10 9 i j 1 where N corresponds to the number of pixels for the selected range R of the adjacency effect 61 68 The exact choice of R is not critical since the adjacency influence is a second order effect Instead of the range independent weighting in eq 10 9 a range dependent function can be selected with an exponential decrease of the weighting coefficients 62 The range dependent case requires more execution time of course Except for special geometries the difference between both approaches is small because the average reflectance in a large neighborhood usually does not vary much and the influence is a second order effect p z y pO x y alo pla y 10 10 The function q indicates the strength of the adjacency effect It is the ratio of the diffuse to direct ground to sensor transmittance The range dependent version of eq 10 10 is R p x y pM a y a o a y Al exp r r ar 10 11 0 Here R is the range where the intensity of the adjacency effect has dropped to the 10 level i e r R 2 3x rs where r is a scale range typically r 0 2 0 4 km R 0 5 1 km p r is the reflectance at range
305. r vapor absorption around 940 nm These effects are most likely caused by a spectral mis calibration In this case an appropiate shift of the center wavelengths of the channels will remove the spikes This is performed by an optimization procedure that minimizes the deviation between the surface reflectance spectrum and the corresponding smoothed spectrum The merit function to be minimized is x 6 D Aoi 8 peroo 2 11 i 1 where part 9 is the surface reflectance in channel i calculated for a spectral shift 5 p th is the smoothed low pass filtered reflectance and n is the number of bands in each spectrometer of a hyperspectral instrument So the spectral shift is calculated independently for each spectrometer In the currently implemented version the channel bandwidth is not changed and the laboratory values are assumed valid More details of the method are described in 30 A spectral re calibration should precede any re calibration of the radiometric calibration coefficients see section 5 8 4 for details about this routine Figure 2 3 shows a comparison of the results of the spectral re calibration for a soil and a vegetation target retrieved from an AVIRIS scene 16 Sept 2000 Los Angeles area The flight altitude was CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 22 20 km above sea level asl heading west ground elevation 0 1 km asl the solar zenith and azimuth angles were 41 2 and 135 8 Only part of the spectrum
306. r zenith angle and the geometrical skyview factor Vexy geom had been calculated based solely on the digital terrain model Application in ATCOR Workflow In ATCOR 4 the local illumination angle y is first calculated on the basis of the terrain model using an efficient vector algebra based method The topographically corrected reflectance Propo is then retrieved from the atmospherically corrected ground leaving excitance My using the direct irradiance qir the diffuse illumination field Taif and the terrain illumination Fer as My Iuircos p 0 1 aj pcos p 0 9L 4 p Vsky Iter Ptopo 10 22 where the factors 0 1 and 0 9 account for the relative amount of circumsolar irradiance estimated to be 10 of the total diffuse irradiance The local illumination factor cos y is now enhanced by the cast shadow fraction such that shaded areas are not affected by direct and circumsolar irradiance Figure 10 8 Effect of combined topographic cast shadow correction left original RGB image right corrected image data source Leica ADS central Switzerland 2008 courtesy of swisstopo After cast shadow correction the border pixels of the shadows are often under overcorrected which is visible as black or bright borders in the cast shadow areas A filter has to be applied to remove this artifact compare Fig 10 9 A simple approach to this problem is an interpolation of all border pixels However a considerable data loss may be
307. r_23 sensor huperion167 bando01rsp Number of polishing bands on each side E Smoothing Factor 0 no smoothing aay Polishing Filter Type y Neighbour Derivatives w Lowpass Filter w Savitzky Golay Def in Polished Output Data Cube data hyperion Bern_02 Hyperion_subl67 polish boa Help Run Polishina Tone Figure 5 62 Statistical spectral polishing 5 6 4 Spectral Polishing Radiometric Variation A module that was originally developed for the airborne version of ATCOR is the spectral polishing The algorithm is only intended for hyperspectral imagery INPUT FILE sate Yaata7 atcor42 deno_data dais98 bar_topo da s_bar_atn bsq OUTPUT IMAGE FILE U data atcor42 demo_data dais99 bari_topo dais_bari_atn_polish bsq J OVERWRITE ESA 55 NT ana file already exists change name or press OVERWRITE 1 QUIT Figure 5 63 Radiometric spectral polishing Input to the spectral polishing program is the reflectance cube calculated with ATCOR It em ploys the vegetation index 0 lt NDVI lt 0 33 NDVI PNIR PRED PNIR PRED to mask soil pixels A soil spectrum is a slowly varying function of wavelength therefore a spectral smoothing will only remove spikes without disturbing the spectral shape Then the average re flectance spectrum over all soil pixels is calculated and smoothed with a 5 channel filter except for CHAPTER 5 DESCRIPTION OF MODULES 115 the atmospheric water vapor regions
308. ransfer calculations for the ATCOR look up tables LUTs are performed on the basis of wavenumber w cm which is converted into wavelength A um using 10000 A 2 14 w nh ate CHAPTER 2 BASIC CONCEPTS IN THE SOLAR REGION 23 For a spaceborne sensor we have n 1 but for airborne sensors we have to account for the refractive index n h in two respects e The MODTRAN wavenumber has to be converted into a wavelength A using eq 2 14 taking care of the refractive index for the corresponding flight altitude h or pressure level p Eq 2 13 is used to convert the flight altitude into the corresponding pressure Switching to wavelength is required because the high resolution spectral database of atmospheric LU T s has to be convolved with the channel filter functions delivered as wavelength data e The lab measured wavelength of the channel filter functions spectral response files also has to be adapted to the refractive index at the flight altitude We use the equation n h 1 0 000293 exp h H 2 15 Therefore the MODTRAN wavelength conversion is 10000 Ao A pe 2 16 The lab wavelength conversion for pressure Pap and height hiap is Ao Asen h ab cza N 2 17 hiab a 2 17 Eq 2 13 is used to calculate the pressure p for a given flight altitude h and vice versa Using the parameter h to indicate the pressure dependence of the refractive index n h we get Ao Asen Riab n hiab Asen h a ala 2 18
309. red regions cannot be retrieved with optical sensors because the signal contains no radiation component being reflected from the ground In shadow areas however the ground reflected solar radiance is always a small non zero signal because the total radiation signal at the sensor contains a direct beam and a diffuse reflected skylight component Even if the direct solar beam is completely blocked in shadow regions the reflected diffuse flux will remain see Figure 10 20 Therefore an estimate of the fraction of direct solar irradiance for a fully or partially shadowed pixel can be the basis of a compensation process called de shadowing or shadow removal The method can be applied to shadow areas cast by clouds or buildings CHAPTER 10 THEORETICAL BACKGROUND 225 This section describes a de shadowing method based on the matched filter approach which is complementary to the scene based method described in section 10 1 2 The proposed de shadowing technique works for multispectral and hyperspectral imagery over land acquired by satellite airborne sensors The method requires a channel in the visible and at least one spectral band in the near infrared 0 8 1 um region but performs much better if bands in the short wave infrared region around 1 6 and 2 2 wm are available as well The algorithm consists of these major components i the calculation of the covariance matrix and zero reflectance matched filter vector ii the derivation of t
310. regions the more accurate SD 8 with the correlated k algorithm was selected 31 Since the wavenumber grid is not equidistant in wavelength the LUTs were resampled with an equidistant 0 4 nm grid of Gaussian filter functions of FWHM 0 4 nm to speed up subsequent calculations So the new LUT database should be sufficient for instruments with bandwidths gt 2 nm covering the solar spectral region from 340 to 2540 nm The thermal high resolution database employs a spectral sampling distance of SSD 0 4 cm for the wavelength region 7 10 um i e corresponding to a wavelength SSD 2 4 nm and SSD 0 3 em for the wavelength region 10 14 9 um i e corresponding to a wavelength SSD 3 5 5 nm A triangular weight function is used with a spectral bandwidth of twice the SSD The Isaacs s 166 CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 167 2 stream method is employed including the correlated k algorithm The Isaacs s algorithm is much faster than DISORT and yields the same results for our cases in the thermal region All files bt7 are calculated for view or scan angles from 0 nadir to 40 off nadir with a 5 increment to enable an accurate interpolation ATCOR RESLUT gt MODTRANS5_ gt Spectral Database gt 50MB 0 4 nm Figure 9 1 Monochromatic atmospheric database The database comprises the flight altitudes 0 1 1 2 3 4 5 10 and 20 km above sea level and the aerosol types rura
311. rk reference pixels the ratio of Lp blue scene to Lp red scene can be compared to the corresponding ratio for the MODTRAN standard aerosols rural urban maritime desert Lp blue scene Ly red scene 10 d F blue MODTRAN Lp red MODTRAN 10 80 The aerosol type for which the double ratio dp is closest to 1 is the best approximation for the scene It approximates the corresponding MODTRAN aerosol type However some fine tuning is subsequently performed to be able to modify the wavelength behavior of the path ra diance compared to the standard aerosol types If Lp blue scene deviates more than 5 from Lp blue MODTRAN then Lp blue scene is used as the valid path radiance In addition the path radiance for any other bands in the blue to red region is linearly re scaled with the factor Ly blue scene Ly blue MODT RAN see Figure 10 13 Here the path radiance in the red band CHAPTER 10 THEORETICAL BACKGROUND 215 is used as a fixed tie point For wavelengths greater than 700 nm a posssible typical 10 difference in path radiance between the selected aerosol type after fine tuning and the actual aerosol is usu ally not important because path radiance contributes only a small fraction to the total radiance If the sensor has no blue spectral band but a green band than the green band is substituted and for the dense dark vegetation the surface reflectance relationship is used p green 1 3 p red 10 81
312. rom global elevation data SRTM Import DEM from ARC GRID ASCII o 002 0205 20504 DEM PYeparaliom e shoe a i wrk a de bbe a ee ee ee a a ee A Slope Aspect Calculation panel 4 205422 ek ee a be eee ae eee Panel or Sty VUE 4420 Sah ae EY ee REDE RS dE Oo a ee ee Example of a DEM left with the corresponding sky view image right Panel of Cast Shadow Mask Calculation SHADOW Panel of Image Based Shadows 1 a e Panel of DEM smoothing so e aooe ma e ed 4e a Cee eee e ee beck a Topographic correction only no atmospheric correction 0004 The Atm Correction Men 254 4 4864 rascarse Aa OER EE ES ATCOR haze removal module ATOOR panel 24 4454 eos Hew saong AGE RA EE Oa ee Rae aS Panelior DEM tilesy lt sores liad eal Be Bo we iy Se ee we Se Goals aw BP Panel to make a decision in case of a DEM with steps Influence of DEM artifacts on the solar illumination image SPECTRA Module s 6 2 ma s re ae ah we Sas A ee Peed eae a Radiometric calibration target specification panel o Radiometric CALIBRATION module e Normalized histogram of unscaled shadow function 00004 Panel to define the parameters for interactive de shadowing Quicklook of de shadowing results 2 0 a es Image processing options Right panel appears if a cirrus band exists
313. rosol types contained in the monochromatic database because the program might run an hour but restrict the resampling to the cases you need If you want to resample a larger number of files let the program run over night Example A sensor operates exclusively in the altitude region from 1 3 km in areas where rural and maritime aerosols are likely to occur In this case it makes no sense to resample the atmospheric files for the altitude regions above 3 km Also a restriction to the files with rural and maritime aerosols will reduce the computation time of the RESLUT program considerably In case of sensors with solar and thermal bands the option Thermal Region on the first GUI panel of RESLUT has to be selected as well The second GUI panel will look slightly different because the aerosol type selection is missing In the thermal region the aerosol type can usually be neglected Therefore all files in the monochromatic database were calculated with the rural aerosol but the aerosol identifyer is not included in the file name because only the rural aerosol is available Note Any altitude interpolated files that were created by ATCOR will be deleted as soon as the user exits the program This is done intentionally to avoid garbage collection Any interpolated file is generated again within a few seconds if necessary Interpolated atm files for the solar region have the extension atmi for the thermal region the int
314. rrection Left Functions G eq 10 118 for different values of the exponent b Right Functions G of eq 10 118 for b 1 and different start values of Bp The lower cut off value is g 0 2 235 1 27 BRP model calibration scheme scor lt 4 045 kkor Fe ee eee Pe eee 238 10 28Image correction scheme 2 1 239 10 29BREFCOR correction Top uncorrected Middle anisotropy index Bottom cor rected ADS 80 image mosaic c swisstopo 241 10 30Weighting of q function for reference pixels ee 242 List of Tables 2 1 Default file pressure dat to be edited if necessary o 24 41 Sensor definition file no thermal bands s ea s e i soa asor ta a ra 46 4 2 Sensor definition file instrument with thermal bands 47 4 3 Sensor definition file smile sensor without thermal bands 49 44 Class label demution of hew tiles oo se iaa a pare eee Boe lee See eS eS 51 7 1 Heat fluxes for the vegetation and urban model 2 00200845 157 10 1 Example of emissivity values for a 11 wm channel o 204 10 2 Class labels in the hew fle e s 63 45 4 Se Bee eRe a ER ESE RSE OS 205 10 3 Visibility iterations on negative reflectance pixels red NIR bands 212 11 Chapter 1 Introduction The objective of any radiometric correction of airborne and spaceborne imagery of optical sensor
315. rrection is applied for classes without any fitting parameters i e if less than 3 bands out of 4 within the class could be calibrated The averaged model is stored for later application to the imagery CHAPTER 10 THEORETICAL BACKGROUND 239 Image correction Finally the derived BRDF model calibration data are to be applied to the image data For applica tion on the imagery the BCI has to be calculated from each image and is used to get a continuous correction function HDRF image ATCOR output BRDF cover index BCI scan angle file zenith azimuth image meta data calibrated model derive model interpolate model kernel subset to BCI range calculate model weighting factors for each pixel combine into relative anisotropy map ANIF multiplicative BRDF correction corrected image spectral albedo BHR Figure 10 28 Image correction scheme The image processing procedure is following the below steps compare Figure 10 28 e calculate the BCI from image e calculate the scene specific angular kernels subsets e interpolate the calibration data from BCI levels to a continuous BRDF model e calculate an anisotropy map by scaling the kernels using the BCI the scan angles observation zenith and azimuth angle and the interpolated BRDF model and e apply the anisotropy map on a per pixel basis The anisotropy factor is derived as relation of the directional model for each pixel to the same model av
316. rtant conclusions about the radiometric calibration We continue with some remarks on how to select atmospheric parameters Next is a short discussion about the thermal spectral region The remaining sections present the topics of BRDF correction spectral radiometric calibration and de shadowing For a discussion of the haze removal method the reader is referred to chapter 10 5 3 Two often used parameters for the description of the atmosphere are visibility and optical thick ness Visibility and optical thickness The visibility horizontal meteorological range is approximately the maximum horizontal distance a human eye can recognize a dark object against a bright sky The exact definition is given by the Koschmieder equation 1 1 3 912 IS l eS 7 2 1 where is the extinction coefficient unit km at 550 nm The term 0 02 in this equation is an arbitrarily defined contrast threshold Another often used concept is the optical thickness of the atmosphere 9 which is the product of the extinction coefficient and the path length x e g from sea level to space in a vertical path Px 2 2 The optical thickness is a pure number In most cases it is evaluated for the wavelength 550 nm Generally there is no unique relationship between the horizontal visibility and the vertical total optical thickness of the atmosphere However with the MODTRANO radiative transfer code a certain relationship
317. s a green band is used as a substitute If a green band and a SWIR2 band exist the rules are DN blue lt Tsaturation and p blue gt 0 22 and NDSI gt 0 6 or p green gt 0 22 and p SWIR2 p green lt 0 3 10 74 This is very similar to the snow assignment in the hcw bsq file except for the threshold for Pp SWIR2 p green e high snow ice probability coded 90 same as for medium probability but with a more stringent NDSI threshold of 0 7 p blue gt 0 22 and NDSI gt 0 7 and DN blue lt Tsaturation 10 75 If no blue band exists a green band is used as a substitute Again if a green band and a SWIR2 band exist the rules are DN blue lt Tsaturation and p blue gt 0 22 and NDSI gt 0 7 or p green gt 0 22 and p SWIR2 p green lt 0 2 10 76 10 4 Standard atmospheric conditions Standard conditions comprise scenes taken under a clear sky atmosphere This means the visibility aerosol optical thickness can be assumed as constant over a scene or it might vary within a certain range excluding haze and a visibility map can be calculated It also includes situations with constant or spatially varying water vapor column contents CHAPTER 10 THEORETICAL BACKGROUND 212 10 4 1 Constant visibility aerosol and atmospheric water vapor This is the easiest case for atmospheric correction Still it can often be applied if homogeneous atmospheric conditions exist These might be encoun
318. s O T gt 7 11 where es is the surface emissivity o 5 669 x1078 Wm K is the Stefan Boltzmann constant and T is the kinetic surface temperature For sensors with a single thermal band such as Landsat TM an assumption has to be make about the surface emissivity to obtain the surface temperature Usually s is selected in the range 0 95 1 and the corresponding temperature is a brightness temperature A choice of s 0 97 or s 0 98 is often used for spectral bands in the 10 12 um region It introduces an acceptable small temperature error of about 1 2 C for surfaces in the emissivity range 0 95 1 Examples are vegetated or partially vegetated fields e 0 96 0 99 agricultural soil e 0 95 0 97 water e 0 98 and asphalt concrete e 0 95 0 96 Emissivities of various surfaces are documented in the literature Buettner and Kern 1965 Wolfe and Zissis 1985 Sutherland 1986 Salisbury and D Aria 1992 The atmospheric longwave radiation Ratm emitted from the atmosphere toward the ground can be written as Ram a O T 7 12 where is the air emissivity and T is the air temperature at screen height 2 m above ground sometimes 50 m above ground are recommended For cloud free conditions Brutsaert s 1975 equation can be used to predict the effective air emissivity 1 24 ae 7 13 CHAPTER 7 VALUE ADDED PRODUCTS 155 Here Pwv is the water vapor partial pressure millibars
319. s are included at the same position as output channels In the ENVI header they are labeled DN 60 0 512 across track FOV degree pixels per line 128 first last reflective band 0 35 2 55 um O first last mid IR band 2 6 7 0 um O first last thermal band 7 0 14 wm no tilt in flight direction required dummy SS DO OE Table 4 1 Sensor definition file no thermal bands Line 5 of the sensor definition file is retained for compatibility with ATCOR 4 versions below 3 0 The tilt parameter is always zero i e tilt sensors are not supported in the atmospheric database In case of tilt sensors the old ATCOR 4 version below 3 0 must be used which requires a MODTRANO 5 license to calculate the atmospheric LUTs because no database is available for tilt sensors Line 6 is a required dummy to be compatible with previous versions CHAPTER 4 WORKFLOW 60 0 1 73 74 0 0 77 512 72 73 79 across track FOV degree pixels per line first last reflective band 0 35 2 55 um first last mid IR band 2 6 7 0 um first last thermal band 7 0 14 um no tilt in flight direction required dummy temperature band itemp_band 77 Table 4 2 Sensor definition file instrument with thermal bands 47 CHAPTER 4 WORKFLOW 48 4 7 Spectral smile sensors Imaging systems can employ different techniques to record a scene the whiskbroom design uses a rotating or oscillating mirror to collect an
320. s cos is applied to calculate the slope m and intercept b After defining C m b the topographic correction map A is calculated cos0s C ho Alz y 10 27 US ospe y C he y o en Finally the surface reflectance p is computed according to a Ls x y z A x p z y 9 2 Ar y 10 28 T x y z Edir x y z cos Eat 2 Y z where T is the total ground to sensor transmittance and Egir Eaif are the direct irradiance and diffuse solar flux on the ground respectively So the ATCOR version of IRC contains some improvements with respect to the original method the path radiance varies spatially mainly caused by terrain height variations possibly also due to visibility variations and the sky view factor can be provided from a ray tracing analysis instead of the local slope angle Note the IRC method usually performs well However due to the statistical evaluation of the regression analysis unphysically large gt 1 reflectance unit or small lt 0 surface reflectance values might happen for some pixels usually in areas with topographic shadow or low local sun elevations 10 1 4 Spectral solar flux reflected surface radiance The spectral solar fluxes on the ground can be calculated by setting the parameter irrad0 1 in the inn file or using the graphical user interface The fluxes depend on solar geometry terrain elevation topography and atmospheric conditions All fluxes and the surface reflected radiance
321. s coss sin0s sinOs cos ds of 4 8 The FODIS slope and azimuth angles 0 df are computed from the FODIS attitude angles roll Oro Pitch Opiten yaw or heading Oyaw f y Oo Pitch 4 9 bf Oyaw 180 arctan Orou Ppitch mod 360 4 10 The last equation holds for the standard case of the aircraft nose up and the sign conventions are e roll angle is positive for the aircraft right wing up e pitch angle is positive for the aircraft nose up e yaw or heading ranges between 0 and 360 90 east The panel TOOLS contains a graphical user interface to process FODIS data in the Specim CaliGeo and NERC Natural Environment Research Council UK formats These are the currently supported formats and more will be added on demand Input to the GUI is the image file example scene bsq sensor and FODIS format see Fig 4 16 The FODIS nav file s and irradiance file are included automatically if the mandatory file name conventions are kept as described below The output example scene_fodis slb is the geometrically corrected FODIS at sensor flux in the ENVI spectral library format stored as one reference spectrum per image line unit mWem um The data format is specified in the ENVI header parameter lines contains the number of image lines samples contains the number of bands parameter bands 1 and data type 4 i e binary float data If the scene_fodis slb file exists
322. s is the extraction of physical earth surface parameters such as spectral albedo directional reflectance quantities emissivity and temperature To achieve this goal the influence of the atmosphere so lar illumination sensor viewing geometry and terrain information have to be taken into account Although a lot of information from airborne and satellite imagery can be extracted without radio metric correction the physically based approach offers advantages especially when dealing with multitemporal data and when a comparison of different sensors is required In addition the full potential of imaging spectrometers can only be exploited with this approach Although physical models can be quite successful to eliminate atmospheric and topographic ef fects they inherently rely on an accurate spectral and radiometric sensor calibration and on the accuracy and appropriate spatial resolution of a digital elevation model DEM in rugged terrain In addition many surfaces have a bidirectional reflectance behavior i e the reflectance depends on the illumination and viewing geometry The usual assumption of an isotropic or Lambertian reflectance law is appropriate for small field of view FOV lt 30 scan angle lt 15 sensors if viewing does not take place in the solar principal plane However for large FOV sensors and or data recording close to the principal plane the anisotropic reflectance behavior of natural surfaces causes brightness gradie
323. s will reduce the performance of the haze removal algorithm A surface reflectance threshold pw1 for water in the NIR band Pixels belong to the water mask if p NIR lt pw only NIR band available A surface reflectance threshold pw2 for water in the 1600 nm region if band exists Pixels belong to the water mask if p NIR lt pu1 and pi600 lt Pw2 The defaults pu 5 and pwz 3 allow some margin for turbid water interpolate bands in 760 nm oxygen region 0 no 1 yes interpolate bands in 725 825 nm water region 0 no 1 yes interpolate bands in 940 1130 nm water region 0 no 1 nonlinear 2 linear smooth water vapor map box 50m 50m 0 no 1 yes The water vapor map is calculated on a pixel by pixel basis a moderate spatial smoothing 50m 50m or at least 3 3 pixels reduces the noisy appearance interpolate bands in 1400 1900 nm nm water region 0 no 1 yes cut off limit for max surface reflectance default 150 out_hew bsq file haze cloud water land 0 no 1 yes 2 hcw quality file water vapor threshold to switch off the cirrus algorithm unit cm define saturation with factor b DN saturated gt b DN max b 0 9 to 1 include non linear influence of vegetation in water vapor calculation yes no Only for water vapor retrieval with regressein iwv_model 2 start stop wavelengths for interpolation in the 940 nm region start stop wavelengths for interpolation in the 1130 nm region start stop wavelengths for interp
324. scattering below the cirrus and se is the cloud base reflectance of upward radiation Eq 10 101 can be simplified because of se p lt lt 1 yielding PA pelA TA PA 10 102 With the assumption that the cirrus reflectance p A is linearly related to the cirrus reflectance at 1 38 um we obtain P A pe 1 38um y 10 103 where y is an empirical parameter derived from the scene scatterplot of p1 38 versus Preg land or p1 24 water It depends on the scene content cirrus cloud height and solar and viewing angles Fig 10 19 shows an example of such a scatterplot The red line is the left side boundary of data points that are not influenced by ground surface reflection i e cirrus contaminated pixels are clustered around this line and its slope represents the correlation coefficient y the blue line represents the first of several iterations Papers on the cirrus algorithm often restrict eq 10 103 to the wavelength interval 0 4 lt A lt 1 um but we will extend this relationship into the SWIR region Substituting eq 10 103 into eq 10 102 yields TA PA PA pel1 38um y 10 104 Neglecting the cirrus transmittance Te i e setting Te 1 we obtain the cirrus path radiance corrected apparent reflectance image index cc Peel p A pel1 38um y 10 105 As the cirrus is almost on top of the atmosphere we have p 1 38um p 1 38um and the apparent cirrus reflectance can be calculate
325. sen for this procedure CHAPTER 5 DESCRIPTION OF MODULES Figure 5 31 ATCOR haze removal module 87 CHAPTER 5 DESCRIPTION OF MODULES 88 5 4 2 The ATCOR main panel Figure 5 32 top shows the input parameters required for ATCOR The lower part of the panel contains buttons for selecting SPECTRA determining the aerosol type employing inflight radio metric CALIBRATION and starting the image processing The processing options are shown in the separate panel as described in section 5 4 10 The trivial panels e g band selection spatial subimage etc will not be shown here The panels should be filled or clicked in the top down direction The message widget at the bottom will display hints warnings or errors INPUT IMAGE FILE Yexport data data71 hun_040507 OPRF Ref 040607 0P_1_rad_sub2 bsq Date dd mm year 07 06 2004 Scan Angle File no file required if original scan geometry not geocoded OUTPUT IMAGE FILE Vexport data data71 hun_040507 OPAF Ref 040607 0P_1_rad_sub2_atn bsq 3 OVERWRITE Scale Factor 100 0 Help Flight and Solar Geometry Band selection Selected SENSOR HYMAPO4_NEW_DB Nadir pixel size m 5 00 CALIBRATION FILE Vexport data data atcor43 sensor hymap04 neu db hymap04_ori_1000 4000 ATMOSPHERIC FILE h03000_ww10_rura ATM FILE for thermal band s Adjacency range km 10 30 Help Zones 2 Visibility km 50 0 SPECTRA AER
326. sensor in degrees default as defined in sensor model CHAPTER 5 DESCRIPTION OF MODULES 135 Scan Angle Output output file name to be created The routine creates a standard 3 layers scan angle file ENVI format as it is used by ATCOR for radiometric processing containing pixel zenith angle in degrees 100 pixel azimuth angle in degrees from north towards east 10 sensor altitude meters asl Figure 5 81 Scan angle creation panel option a top option b bottom CHAPTER 5 DESCRIPTION OF MODULES 136 5 8 10 MTF PSF and effective GIFOV A useful representation of the spatial resolution of a sensor is the transfer function describable in terms of a modulation transfer function MTF and a phase transfer function The transfer function describes how the system modifies the amplitude MTF and shifts the phase of the input spatial frequencies Usually only the MTF is given as a figure of merit or its counterpart the point spread function PSF The MTF is the 2 D Fourier transform of the PSF 86 Fig 5 82 presents the GUI for the MTF PSF evaluation The image is loaded in the left part The user should click with the left mouse button in the area where the MTF evaluation is intended This area will be shown in top middle part of the GUI Then the MTF can be evaluated for a line or an edge yielding the line spread function LSF or edge spread function ESF respectively The user is responsible to select a
327. sivities usually lie in the 10 5 13 wm region After calculating the surface temperature the emis sivities for all channels are computed e the ASTER TES algorithm is available for sensors with at least 5 channels in the thermal interval Az Ay with Ay FWHM 2 gt 8 08 um and Ay FWHM 2 lt 13 0 um to exclude channels in strong absorption regions The TES algorithm implemented here consists of 3 major parts Gillespie et al 1998 28 the NEM algorithm described above the ratio module It calculates relative emissivities 8 channel i by ratioing the NEM emissivity values e to their average n Ej bi gt n Y Es i l i 1 n 10 39 Here n is the number of channels in the allowed thermal interval maximum minimum distance MMD module The maximum and minimum values of the relative emissivities spectrum are calcu lated to find the spectral contrast MMD maz 8 min 8 10 40 Then an empirical relationship is used to predict the actual emissivities e from the MMD employing a regression with laboratory or field emissivity spectra Emin a b MMD 10 41 For small values MMD lt 0 03 e g water vegetation the value min is set to 0 983 The 3 parameters a b c of the regression can be specified by the user the default values are taken from 28 i e a 0 994 b 0 687 c 0 737 The final step calculates the actual emissivities using the spectrum and Emin Bi 2
328. sotropy map and the bci which is the BRDF correction index used to discriminate the BRDF classes Calibration Granularity For calibration of the model the image is segmentated in a number of BRDF classes The number of pre defined classes can be selected calibration on a bigger number of classes leads to better accuracy of the correction while it requires good image statistics Reflectance scale factor Constant Factor which is to be applied to the input image which has to be directional reflectance to convert the image DNs to absolute reflectance values between 0 and 1 CHAPTER 5 DESCRIPTION OF MODULES 106 Output Definition The directory for all outputs the BRDF model file name and the file name appendix can be defined here Actions e Calibrate Model the model calibration file is created without applying a correction to the images e Run Model Based the model based BRDF correction is performed 5 5 2 Nadir normalization Wide FOV Imagery This module NADIR_REFL performs an empirical BRDF correction by normalizing the across track radiance gradients to the nadir brightness value see chapters 2 6 10 6 1 Figure 5 54 shows the corresponding GUI panel The statistical nadir normalization works best for wide FOV airborne data and requires a minimum field of view of 20 e090 IN NADIR_REFL Nadir Normalization of Reflectance Radiance Cube V 2 0 INPUT IMAGE Reflectance Radiance Ycubes hymap vord_1 Vordemw
329. stitute 3 Haze areas are orthogonal to the clear line i e a haze optimized transform HOT can be defined as Zhang et al 2002 HOT BLUE x sina RED x cosa 10 92 4 Calculation of the histogram of HOT for the haze areas 5 For bands below 800 nm the histograms are calculated for each HOT level j The haze signal A to be subtracted is computed as the DN corresponding to HOT level j minus the DN corresponding to the 2 lower histogram threshold of the HOT haze areas The de hazed new digital number is see figure 10 17 DN new DN A 10 93 So the haze removal is performed before the surface reflectance calculation Two options are available the use of a large area haze mask eq 10 94 which is superior in most cases or a compact smaller area haze mask eq 10 95 HOT gt mean HOT 0 5 x stdev HOT 10 94 HOT gt mean HOT 10 95 In addition the user can select between haze removal of thin medium haze or thin to moder ately thick haze the last option is superior in most cases The algorithm only works for land pixels so the near infrared band NIR is used to exclude water pixels The current implementation provides a mask for haze over land see the _out_hew bsq file The haze over water mask is treated in the next section Figure 10 18 shows an example of a subset of an Ikonos scene of Dresden where the haze removal algorithm was applied More images with the resul
330. stored as separate files The visibility calculation based on the reference pixels has to account for the adjacency effect because reference areas are embedded in non reference areas see the sketch below Since the weighting fraction of reference to non reference area within the adjacency range is not known for each pixel the visibility calculation is performed with an average adjacency weighting factor of 0 5 q CHAPTER 10 THEORETICAL BACKGROUND 241 Figure 10 29 BREFCOR correction Top uncorrected Middle anisotropy index Bottom corrected ADS 80 image mosaic c swisstopo Ladj cor Co DN 0 54 DN DNav ctear 10 132 L VIS Lp Tpref Eg T Ladj cor 10 133 Next the visibility is converted into the nearest visibility index vi range 0 182 compare Fig 10 14 to store the visibility index map as byte data Spatial interpolation is performed to fill the gaps for non reference pixels or the average vi value can be taken to close the gaps A moving average window of 1 5 km x 1 5 km is employed to reduce the influence of noise The cloud building shadow map is stored separately fshd bsq file containing the fraction of direct solar irradiance per pixel scaled with the factor 1000 The scaled value 1000 indicates full solar irradiance smaller values a corresponding fractional value e an update of the path radiance in the blue to red spectral region is performed if required provided a blue spectral band exis
331. t Help New Sensor Delete Update Sensor Rename Done Figure 5 14 Definition of a new sensor Sensor Selection Select any of the already defined sensors from within ATCOR a sensor is selected by its sensor definition file sensorx dat within a sensor directory Use the function New Sensor in case your sensor has not yet been defined Inputs Sensor Type This is to be selected first smile sensor and thermal sensors require additional inputs as displayed in the panel Sensor Total FOV deg edge to edge FOV in across track direction in degrees Number of Across Track Pixels Nominal number of pixels in the unrectified data First last Reflective Band Band numbers starting at one none 0 First last Mid IR Band Band numbers starting at one none 0 First last Thermal IR Band Band numbers starting at one none 0 CHAPTER 5 DESCRIPTION OF MODULES 71 Applied scaling factor Enter the constant scaling factor which had been applied to the data to fit it to an integer data type from mW cm srum Typical values are 100 500 or 1000 The x cal file will be created with constant values for all bands according to this value If the scaling factor is not constant the cal file is to be edited manually NOTE a scale factor of zero or below inhibits to write a cal file Calibration Pressure absolute pressure in hPa of instrument during spectral calibration in lab oratory Instrument Pressure pressure
332. t and optionally skyview and cast shadow are used to calculate the solar illumination map cosine of the local solar zenith angle which is stored as byte data range 0 100 scale factor 100 If the file name of the input scene is scene bsq then the corresponding illumination file is named scene_ilu bsq However there is an option to process an external illumination map and skip the internal calcula tion In this case the requested condition is a float coded file range 0 1 with the nomenclature scene_raw_ilu bsq first priority or scene_ilu bsq second priority to distinguish it from an internal calculation if an internal calculation was performed previously In case of a rugged terrain the illumination based de shadowing algorithm stores results in a multi layer file and the first layer is the float coded illumination Two de shadowing methods are implemented in ATCOR the matched filter approach see chapter 10 5 6 and the illumination based approach see chapter 10 1 2 The latter delivers a float coded CHAPTER 4 WORKFLOW 57 INPUT IMAGE FILE Yexport data data71 fodis gali leo 0617 1542_AISA_rad bsq Solar zenith angle degrees i 37 6 range 0 70 Solar azimuth degrees 236 0 range 0 360 east 90 Selected SENSOR aisa_galileo Select FODIS Format irradiance and roll pitch yaw 2 Standard SPECIM CaliGeo w NERC FODIS irradiance scale factor p 0020 converts into mb cm 2 um 1
333. t constantly increasing bands Number of polishing bands on each side Adjacent bands to be used for calculation on each side of the target band e g factor 3 uses 7 bands for polishing 3 on each side plus central band Smoothing Factor smoothing applied stand alone or in combination with the derivative filter 0 no smoothing 1 slight smoothing filter 1 4 1 2 moderate smoothing filter 1 2 1 3 standard smoothing filter 1 1 1 4 and more standard smoothing with moving average Polishing Filter Type Four options are available for statistical spectral polishing Derivative Filter all spectral bands of the given window size are taken into account to calculate deriva tive used to reconstruct the value of the center band Neighbour Derivative all spectral bands except for the center itself are taken into account to CHAPTER 5 DESCRIPTION OF MODULES 114 calculate derivative used to reconstruct the value of the center band Lowpass Filter Only the smoothing is performed no derivatives are calculated Savitzky Golay Filter to perform a numerical polinomial fit of 4th degree through the selected total window size Output A cube containing the spectrally filtered copy of the original image data cube is generated compare Paper Earsel SIG IS Workshop Edinburgh 2011 00 A ATCOR Derivative Polishing a Select Input File Names data hyperion Bern_02 Hyper ion_subl67 bs9 Selec Sensor Spectral Response Yarc_id1 atcor atco
334. t integer ENVI data type 1 e signed 16 bit integer ENVI data type 2 e unsigned 16 bit integer ENVI data type 12 e signed 32 bit long integer ENVI data type 3 e float 32 bit ENVI data type 4 9 3 2 Side inputs Before running the main processor certain files have to be provided the DEM derived files are not required for a flat terrain Parameter file extension inn This file is written automatically while using the graphical user interface It may also be written externally for use in batch processing See Section 9 4 for a detailed description Scan angle file extension image sca bsq Data layers defined in map geometry or in raw geometry with scan zenith scan azimuth angle and the absolute distance from aircraft to the pixel for each geocoded pixel The first band of this image is used as standard input for ATCOR 4 radiometric processing whereas the second band is only required for a potential future BRDF correction Only band 1 is mandatory for use with ATCOR 4 Format 3 bands 16 bit integer ENVI image 1 Sensor zenith angle degree 100 0 deg is downward looking nadir unkown invalid background values are greater than 9000 assigned value 9100 pixels to the right with respect to the flight direction have negative values this is sometimes confusing in raw data formats as it depends on the scan direction of the instrument CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 172 2 Abso
335. t pixels in the NIR with DN gt DN mean o o standard deviation are removed from the list of shadow pixels e a 7 x 7 pixel convolution filter is being applied to the shadow mask e a transition region shadow sunlit is introduced e already dark pixels DN lt DN mean o in the NIR are not reduced in brightness during BRDF correction The threshold is evaluated for a NIR channel but the non reduction of brightness reflectance is applied to all channels 2 Note on keeping the atmi files In the interactive mode the temporary atmi files in the sensor specific sub directory of the atm_lib are deleted to avoid an accumulation of these temporary files However in the batch mode it might be necessary to keep these files if subsequent runs with the same flight altitude exist these need the files and they might have been deleted by the previous run Then the user should delete the atmi files after processing the imagery of the flight campaign The GUI Edit Preferences should be used to change the default setting 9 5 Job control parameters of the inn file If the file name of the input image is example_image bsq then a file erample_image inn is created during the interactive ATCOR session When all image processing parameters have been defined by the user this inn file is written to the directory of the corresponding image When the image is re loaded during a later sessi
336. t shows the shadow map scaled between 0 and 1000 The darker the area the lower the fractional direct solar illumination i e the higher the amount of shadow The proposed de shadowing technique works for multispectral and hyperspectral imagery over land acquired by satellite airborne sensors The method requires a channel in the visible and at least one spectral band in the near infrared 0 8 1 um region but performs much better if bands in the short wave infrared region around 1 6 and 2 2 um are available as well A fully automatic shadow removal algorithm has been implemented However the method involves some scene dependent thresholds that might be optimized during an interactive session In addition if shadow areas are concentrated in a certain part of the scene say in the lower right quarter the performance of the algorithm improves by working on the subset only The de shadowing method employs masks for cloud and water These areas are identified with spectral criteria and thresholds Default values are included in a file in the ATCOR path called preferences preference_parameters dat As an example it includes a threshold for the reflectance of water in the NIR region p 5 So a reduction of this threshold will reduce the number of pixels in the water mask A difficult problem is the distinction of water and shadow areas If water bodies are erroneously included in the shadow mask the resulting surface reflectance values wil
337. tabase 2 irradiance f2 Convert Database Input database corresponding to e0_solar_kurucz2005_04nm export data data atcor43 atm_database Output database corresponding to e0_solar_thu2003_RSL_ku2005_04nm export data data atcor43 atm_database_thu Number of files to be converted 20 File 1 of 20 File 10 of 20 File 20 of 20 DONE time 18 sec All bp files converted on export data data7 atcor43 atm_database_thu2003_RS Output directory also contains reference irradiance e0_solar_thu2003_RSL_ku2005_04nm dat i 3 SSS a QUIT Figure 9 3 User interface to convert database from one to another solar irradiance 9 2 Sensor specific atmospheric database This database is created by resampling the files of the monochromatic database with the sensor s spectral response functions employing program RESLUT see figure 9 4 Usually only a subset of the total number of files of the monochromatic database is needed for a new sensor The sensor specific database will contain the altitudes and aerosol types that were specified during the resampling with program RESLUT There are 5 water vapor files per altitude and aerosol type CHAPTER 9 IMPLEMENTATION REFERENCE AND SENSOR SPECIFICS 170 9998 that have to be resampled The folder with the atm files also contains a file irrad_source txt identifying the underlying solar irradiance spectrum Attention Do not resample all flight altitudes and ae
338. tation soil others depending on surface cover The surface cover class is calculated on the fly based on the vegetation index and red NIR reflectance values for each pixel Typical average emissivity values were assumed vegetation e 0 97 soil asphalt concrete e 0 96 others e 0 98 The file name of the 3 class emissivity map is magel_atm_emi3 bsq the 3 indicating the 3 classes if the file name of the scene is imagel bsq e a detailed map of up to 15 emissivity classes compare table 10 1 The user has full control over the assignment of an emissivity value for each class i e the file emissivity dat can be edited by the user This emissivity assignment pertains to the channel for which the surface temperature is calculated compare the sensor definition file in chapter 4 6 The emissivity CHAPTER 4 WORKFLOW 45 map for each scene can be calculated with program SPECL prior to the processing of the thermal bands but after processing of the coregistered reflective bands SPECL classifies the surface reflectance spectrum of each pixel by comparing it to a set of template surface cover spectra and assigns the class for which the best match is found If no sufficient match is found the pixel is assigned to not classified The file name is imagel_atm_cla_emi bsq It is generated simultaneously with the classification file im age1_atm_cla bsq see chapter 5 8 2 One of the first two options is usual
339. ted at 65 000 which might happen for bright surfaces e g snow vegetation in the NIR with scalef 10 000 see Figure 8 5 Therefore the easiest way to avoid scale problems is to use the default scalef 1 0 and have a float radiance output cube sz 30 deg olbedo 0 9 9 3 0 05 1 000 9 190 9 010 Radiance mW cm sr pm 9 001 0 5 1 0 1 5 2 0 25 Wavelength am Figure 8 5 TOA radiances for three albedos and a solar zenith angle of 30 MODTRAN calculation for a mid latitude summer atmosphere rural aerosol visibility 15 km ground elevation 500 m above sea level For convenience a log and an ini file are created for the documentation of the processing parameters e g datal image_toarad ini In addition the corresponding sensor calibration file CHAPTER 8 SENSOR SIMULATION OF HYPER MULTISPECTRAL IMAGERY 165 will be created Example sensor casi96 scalef 1000 then file casi96_scalef1000 cal will be created on the atcor4 sensor casi96 directory with the radiometric calibration coefficient c 0 001 for each band Chapter 9 Implementation Reference and Sensor Specifics This chapter discusses miscellaneous topics associated with the current implementation of ATCOR First the user is acquainted with the structure and handling of the atmospheric database Second the supported input output file types are given The next item discusses the preference parameters e g t
340. tered for small area scenes i e high spatial resolution imagery If the sensor has no channels in atmospheric water vapor regions results of atmospheric correction are not sensitive with respect to the selected water vapor content and a climatological value e g midlatitude summer US standard or tropical water vapor profile is usually sufficient For hyperspectral instruments the processing has to include the image derived pixel by pixel water vapor map The program performs a check whether the specified visibility leads to negative reflectance pixels for dark surfaces in the red band 660 nm vegetation and NIR band 850 nm water If this is the case the visibility is iteratively increased up to VIS 120 km to reduce the percentage of negative reflectance pixels below 1 of the scene pixels During an interactive ATCOR session the user is notified and can continue with the recommended visibility update or with the initial visibility if the input visibility in the inn file is positive For a negative visibility in the inn file no visibility iterations are performed A corresponding notice on the final visibility is given in the atm log output file The set of visibility grid point is given in Table 10 3 The upper visibility threshold of 80 km is a trade off although higher visibilities are possible they are not very likely and even if a situation with a higher visibility say VIS 120 km is encountered results of a c
341. th uses the standard smoothing i e lowpass filter in the spatial domain Median uses a median filter for data correction e g to remove noise or outliers from the DEM CHAPTER 5 DESCRIPTION OF MODULES 84 6005 3 Smooth a DEM Select Input DEM File Name Ysrc_idl atcor atcor_23 demo_data tm_rugged tm_blforest_ele bsq Dimensions 500 500 Diameter of DEM Filter Pixels 3 Pe Output Name of Filtered DEM Ysrc_idl atcor atcor_23 demo_data tm_rugged tm_blforest_sm3_ele bsq Help Smooth Median Tone y Al Figure 5 28 Panel of DEM smoothing ATTENTION The xilu file is not smoothed automatically by this routine If the ilu has already been calculated before it should be either removed or be smoothed separately 5 3 8 Quick Topographic no atm Correction The quick topographic correction routine TOPOCOR tries to eliminate the slope aspect topo graphic effects and neglects the atmospheric influence The program runs very fast and the output image contains the modified digital numbers Processing with TOPOCOR can be done with or without display of images The DEM slope and aspect files have to be specified on a separate panel that pops up after the input image has been specified The topographic correction implemented here multiplies the DN value with a factor f that depends on the local solar zenith angle 6 and a weighting coefficient w The wavelength depending weighting w is based on a typical value of
342. the calibration coefficients Next the target box size and the corresponding ground reflectance CHAPTER 5 DESCRIPTION OF MODULES 94 file have to be specified The button for the file name of target 2 is insensitive because the single target mode was selected here Then the file name for the calibration results should be specified The default name is test cal However it is recommended to include the name of the ground target here Now the target s can be clicked in the zoom window that pops up automatically Target 1 has to be clicked with mouse button 1 mbl left target 2 with mouse button 2 mb2 center The zoom window is moved in the main window by pressing mb1 for target 1 and mb2 for target 2 Alternatively the target coordinates x y column line can be specified In addition to the file xxx cal the files xxx rdn radiance versus digital number and xzz adj original and adjacency corrected DN s are automatically created Select display bands file e ice Visibility bn 35 0 Ata fale jorra el miN cr ar micrometer Create Zoon Window Contrast stretching Gaustias w Histo MIN Bo m Fae Return Figure 5 38 Radiometric CALIBRATION module The appearance of the inflight calibration module is similar to the SPECTRA module In the left part the image is loaded A zoom window can be created and two contrast stretching options Gaussian and histogram e
343. the result of such a process Thus a more sophisticated approach is required As the border pixels are usually isolated a filter approach has been used which compares the pixel brightness p to the brightness of its direct neighbors Pprox in a 3x3 or 5x5 box respectively The brightness of the shadow border pixel is then adjusted by the relative brightness difference of the whole spectrum such that Pfiltij Pij ene 10 23 Pij This method proved to successfully remove shadow borders for high resolution imagery and an urban environment However for terrain shadows on a larger scale the border pixels are not such clearly isolated and often can not be corrected using this method or only after increasing the size of the border pixel filter significantly CHAPTER 10 THEORETICAL BACKGROUND 197 Figure 10 9 Effect of cast shadow correction middle and shadow border removal right for building shadows The updated processing leads to improved terrain correction as displayed in Fig 10 8 Advantages of this method are e terrain and forest cast shadows are corrected e water and cast shadows are discerned in most cases e operational usability has been proven on large ADS data sets and e aconsistent physically based method applicable to photogrammetry and imaging spectroscopy is implemented 10 1 3 Integrated Radiometric Correction IRC The IRC method was published by Kobayashi and Sanga Ngoie 43 44 to provide a combined
344. tion BREFCOR A generic BRDF effects correction routine BREFCOR 85 has been included in ATCOR The idea is to apply a scaling of the volume scattering and the geometric scattering component within a well accepted BRDF model A fuzzy surface cover index of the complete image image is used for this purpose which covers all surface types from water to asphalt and concrete soils sparse vegetation and dense vegetation The Ross Li sparce reciprocal BRDF model has been selected as basis for the correction of re flectance anisotropy 33 This model is mainly developed for vegetation but we use it in a scaled way for all kind of surfaces Literature mainly related to MODIS atmospheric correction routines showed the superior performance of this model if compared to others However for high spatial resolution instruments also other models may be applicable Selected BRDF kernels The BRDF correction scheme is based on the Ross Thick Li Sparse Model RTLS potentially enhanced by the Hot Spot function as proposed by Maignan et al 49 For the correction a formulation of the model for the Bidirectional Reflectance Factor BRF is used The BRF is well suited for correction of the HDRF as both quantities are defined as 1 0 for a 100 reflecting target at the same observation geometry and as only the second dimension observation direction relative variation of the BRF is used for the correction The generic RTLS equation of the BRF for each pi
345. tions in Equation 10 127 are given as follows first for forests using the absolute HDRF value in the green Pyreen 0 5 C A forest 004 0 2 10 07 pgrcenlo ga LN DVI 0 55 8 0 10 128 The upper and lower values at the square brackets indicate a truncation at these values The upper values could be adapted for better representation of biome types For surface covers having a BCI below 0 1 i e mostly soils a reduction factor is found from the relation between blue and red HDRF as Oo u 10 NDVI Cie 10 129 re This factor accounts for the variability of non vegetated areas in the visible Finally a summand to account for water is added starting with BCI soi NDVI Cforest Csoiis It takes into account the relatively higher reflectance of water in the green spectral band in relation to the blue for discrimination to other surface targets such as shadows and dark asphalt Cu See 08 gt 0 3 BC Iso 0 5 lt 0 10 130 2Pblue The range of the final BCI function is defined between values of 1 20 and 1 50 The BCI index calculated in each image pixel can then be used for BRDF model calibration and subsequently for image correction CHAPTER 10 THEORETICAL BACKGROUND 238 HDRF images RTLS model calibration set option Maignan BCl calculation RTLS kernels f_vol variation BCl level Cl levels 1 t03 f_geo variation 0 25 to 0 75 level analysis o fit p
346. to FOV 2 on the right side is adequate except for geometries close to the hot spot geometry In the latter case a 1 sampling interval can be selected If bnadir denotes the averaged brightness value for the nadir region i e reflectance or radiance then the nadir normalized brightness value of a pixel with column number j is calculated as bnorm j uj pa 10 117 where the function fg is obtained with three processing steps e The first step is the averaging over each interval 3 or 1 It yields a function f with m 1 grid points for the m off nadir intervals plus the nadir interval e Two cases are distinguished now if the image is not geocoded an interpolation from function fi m 1 to a function fo ncols is performed where ncols is the number of column pixels of the image If the image is geocoded an interpolation from the 3 grid to the 1 grid is performed no hot spot case e The third step is a filter with a moving average window applied to the f2 function The following cases are distinguished if the image is not geocoded the window is 9 pixels without hot spot and 3 pixels with hot spot option If the image is geocoded the moving window extends over a 5 angular interval no hot spot and over a 3 interval with hot spot option Figure 10 25 shows part of a HyMap image acquired 3 June 1999 Barrax Spain 12 09 UTC containing the hot spot geometry The solar azimuth was 181 and the sensor scan
347. to a certain ms band is weighted with the value of the ms response curve at the corresponding hs wavelength compare Fig 8 1 After summing all contributions the result is normalized with the sum of the hs filter values Lms i _ k 1 E Rs Ajo where L denotes at sensor or TOA radiance R the ms response function of channel i and ni is the number of hs channels covered by the i th ms filter function A similar equation is used for the resampling of surface reflectance or emissivity The weight factors wz for each hs channel are calculated with eq 8 2 and they are documented in the corresponding log file created by program HS2MS 159 CHAPTER 8 SENSOR SIMULATION OF HYPER MULTISPECTRAL IMAGERY 160 o r Do T e a T 1 1 i 1 1 1 i 1 1 1 1 1 e NS T e lo T Normalized Response Function o L L 4 4 L L L 1 1 4 0 62 0 64 0 66 0 68 Wavelength jem L ni 0 70 Figure 8 1 Weight factors of hyperspectral bands The solid curve shows the response function R of a ms channel and the dashed lines indicate the hs center wavelengths AD i 1 k wp i Frans 8 2 E RIA j 1 Fig 8 2 describes the sequence of processing for the sensor simulation in the solar region After atmospheric correction with ATCOR the image_atm bsq contains the surface reflectance cube Program TOARAD then calculates the at sensor radiance for a different fl
348. to measure the absorption depth see chapter 10 4 3 Otherwise if a sensor does not possess spectral bands in water vapor regions e g Landsat TM or SPOT an estimate of the water vapor column based on the season summer winter is usually sufficient Typical ranges of water vapor columns are sea level to space tropical conditions wv 3 5 cm or g cm midlatitude summer wv 2 3 cm dry summer spring fall wv 1 1 5 cm dry desert or winter wv 0 3 0 8 cm 2 2 Spectral calibration This section can be skipped if data processing is only performed for imagery of broad band sensors Sensor calibration problems may pertain to spectral properties i e the channel center positions and or bandwidths might have changed compared to laboratory measurements or the radiometric properties i e the offset co and slope c1 coefficients relating the digital number DN to the at sensor radiance L cy cy x DN Any spectral mis calibration can usually only be detected from narrow band hyperspectral imagery as discussed in this section For multispectral imagery spectral calibration problems are difficult or impossible to detect and an update is generally only performed with respect to the radiometric calibration coefficients see chapter 2 4 Surface reflectance spectra retrieved from narrow band hyperspectral imagery often contain spikes and dips in spectral absorption regions of atmospheric gases e g oxygen absorption around 760 nm wate
349. tomatically excluded from this mask because of the pnir gt 0 10 condition and soil pixels are excluded with the combination of all four conditions If the percentage of reference pixels is smaller than 2 of the scene the search is iterated with VIS 120 km covering the very clear conditions of visibility 40 60 km Again if the percentage is smaller than 2 the search is iterated with VIS 10 km to cover higher aerosol loadings VIS 8 15 km Each visibility iteration is supplemented with an iteration of the threshold peq which is decreased in steps of 0 005 down to prea 0 025 to include only the darkest vegetation pixels see 67 for details Currently the algorithm terminates if less than 2 reference pixels are found after these two iterations In this CHAPTER 10 THEORETICAL BACKGROUND 216 case the user has to employ the constant visibility option specifying the value of the visibility for the scene During batch mode operation the program takes the specified visibility from the inn file Then a check for negative reflectance pixels is performed with dark pixels in the red band 660 nm vegetation and the NIR band 850 nm water and the visibility is iteratively increased up to VIS 60 km to reduce the percentage of negative reflectance pixels below 1 of the scene pixels A corresponding notice is given in the atm log output file The third step calculates the surface reflectance in the red band as a fraction a o
350. trieval of precipitable water from observations in the split window over varying surface temperature J Applied Meteorology Vol 29 851 862 1990 Kobayashi S and Sanga Ngoie K The integrated radiometric correction of optical remote sensing imageries Int J Remote Sensing Vol 29 5957 5985 2008 Kobayashi S and Sanga Ngoie K A comparative study of radiometric correction methods for optical remote sensing imagery the IRC vs other image based C correction methods Int J Remote Sensing Vol 30 285 314 2009 Kriebel K T Measured spectral bidirectional reflection properties of four vegetated sur faces Applied Optics Vol 17 253 259 1978 Li Z L et al A new appooach for retrieving precipitable water from ATSR2 split window channel data over land area Int J Remote Sensing Vol 24 3163 3180 2003 Luo Y Surface bidirectional reflectance and albedo properties derived using a land cover based approach with Moderate Resolution Imaging Spectroradiometer observations J Geo phys Res vol 110 no 1 p DO1106 2005 Liang S Falla Adl H Kalluri S Jaja J Kaufman Y J and Townshend J R G An operational atmospheric correction algorithm for Landsat Thematic Mapper imagery over the land J Geophys Res Vol 102 D14 17 173 17 186 1997 Maignan F Bron F M and Lacaze R Bidirectional reflectance of Earth targets evaluation of
351. trum This program serves for the general purpose of resampling It requires an ASCII file with two columns as input The first column is wavelength nm or ym unit the second is reflectance or emissivity or something else e g spectral radiance The reflectance range can be 0 1 the intrinsic reflectance unit or the percent range 0 100 Figure 5 60 shows the graphical user interface The input spectrum is high spectral resolution data After specifying the first band of the sensor the resampling is performed for all spectral bands and the result is written to an output file again with two columns wavelength and resampled spectrum Pick Input Spectrum Vexport data data atcor43 spec_l1b asd_spectra asd_asphalt dat Pick Response File first band of sensor Vexport data data atcor43 sensor hynapo4 bando01 rsp Dutput Filename resampled spectrum Yexport data data7 atcor43 spec_11b asd_spectra asd_asphalt_hynap04 dat RUN Resampling 1 ay Status 2 ri AA Quit Figure 5 60 Resampling of a reflectance spectrum 5 6 2 Low pass filter a Spectrum This program serves to prepare target reference spectra for IFCALI inflight calibration Unwanted spectral noise is removed by lowpass filter i e spectral smoothing It requires an ASCII file with CHAPTER 5 DESCRIPTION OF MODULES 113 two columns as input The first column is wavelength nm or um unit the second is reflectance or emissivity or somethin
352. truments while a detection of thin cirrus requires specific narrow bands around 1 38 um or 1 88 um compare chapter 10 5 5 As a first approximation haze is an additive component to the radiance signal at the sensor It can be estimated and removed as described below Cloud areas have to be masked to exclude them from haze areas and to enable a successful haze removal The treatment of cloud shadow regions is discussed in chapter 10 5 6 Two de hazing algorithms are available method 1 is based on the haze thickness map 50 method 2 employs the haze optimized transform 100 Method 1 can be submitted from a GUI graphical user interface or as a batch job and performs the de hazing on the original digital number DN data This is an optional pre processing step to ATCOR An atmospheric correction can be performed as an independent next step to obtain surface reflectance data Method 2 performs a de hazing embedded in ATCOR so it is always combined with the atmospheric correction It is available in the GUI and batch modes 10 5 2 Haze removal method 1 This method automatically detects and removes haze in medium and high spatial resolution mul tispectral images The dark object subtraction algorithm is further developed to calculate a haze thickness map The haze thickness is computed for each spectral band excluding very bright ob ject because these can be misinterpreted as hazy areas The haze thickness for these bright object pixels is appox
353. ts CHAPTER 10 THEORETICAL BACKGROUND 242 Figure 10 30 Weighting of q function for reference pixels water vapor retrieval using the previously calculated visibility map If the scene contains no reference areas the user has to specify a constant visibility that enters the water vapor calculation reflectance spectrum retrieval with pixel based water vapor and visibility map Iterations for adjacency effect and spherical albedo are included For the adjacency correction the reflectance of cloud pixels is replaced with the scene average reflectance to avoid an overcor rection of the adjacency effect temperature emissivity retrieval if thermal bands exist 10 7 2 Algorithm for rugged terrain The algorithm for rugged terrain basically consists of the same processing step as in the flat terrain but every step has to take into account some or all DEM information During the calculation of the visibility index map the DEM information elevation slope aspect skyview factor is taken into account The retrieval of the water vapor map has to include the terrain elevation The empirical BRDF correction is based on the local illumination map local solar zenith angle derived from the slope aspect and shadow channels The retrieval of the spectral reflectance cube consists of the steps 1 three iterations for terrain reflectance 2 empirical BRDF correction depending on illumination map if enabled 3 adjacency correction i
354. ts of the haze removal method are shown on ATCOR s web page http www rese ch or http www op dlr de atcor CHAPTER 10 THEORETICAL BACKGROUND 221 69 50 E 40 D GN E 30 E m clear line z slope a 19 9 mom m 60 65 70 75 BO 85 930 49 45 59 55 60 DN blue band HOT level Figure 10 17 Haze removal method Left regression between red and blue band for clear areas Right calculation of Delta A as a function of the HOT haze level example Landsat TM band 1 10 5 4 Haze or sun glint removal over water The haze removal over water uses a near infrared NIR band to estimate the spatial distribution of haze The principal method is described in 77 We use a modified version of this approach without an interactive definition of haze polygons First the water pixels are masked either using spectral criteria or taking an external water map If the scene is named scene1 bsq the external map must be named scenel_water_map bsq a 1 channel 8 bit pixel or 16 bit pixel file where water is coded with an arbitrary positive number The external map is automatically taken if it is placed in the same folder as the scene The second step is the definition of clear water pixels using the apparent reflectance in the NIR band Pixels are labeled as clear if p NIR lt T clear pixels 10 96 The default value is T 0 04 i e 4 The value is one of the editable preference parameters see chapter 9 4 Thin haze over water is
355. ument changes during the mission a spectral re calibration might be necessary from the image data or from on board calibration facilities using well defined absorption features Onboard spectral calibration devices such as interference or rare earth filters would be well suited for this purpose However such devices are often not available in sensor systems Therefore atmospheric gas absorption features or solar Fraunhofer lines have to be taken as a reference from the imagery itself The processing steps are 1 A calibrated image is averaged in along track direction leading to a signature image of the size of the detector array 2 The surface reflectance is calculated atmospheric correction and smoothed 3 The spectral bands within the spectral matching range are selected CHAPTER 5 DESCRIPTION OF MODULES 125 Figure 5 74 Example of classification with SPECL Left true color image of HyMap right result of classification 4 Spectral shifts with intervals between 0 01 0 05 nm are calculated and applied to the selected spectral band response functions 5 An appropriate pre calculated fine spectral resolution atmospheric LUT is selected which serves for the calculation of at sensor radiance values for the series of spectrally shifted re sponse functions using the surface reflectance spectrum from step 2 6 The derived spectral signatures are then correlated to the observed column averaged signal in the image such that t
356. ushbroom spectral polishing Spectral Polishing is done in spectral dimension only one adjacent band on each spectral side is taken for residual calculation Spatial Only the spatial filter is applied use this option for destriping of imagery 2D Filter Do both dimensions spatial spectral simultaneously the filter size only applies to the spatial dimension however Type of Correction Function For each detector pixel correction parameters are generated Gain and Offset Calculate average residual gain and offset for each pixel and apply them as correction function Gain Only Constrain the residual gain to an offset of 0 this is the typical situation Output A cube containing the spectrally filtered copy of the original image data cube is created As a side output a gain file is written name _gain bsq containing the following three layers 1 offset of linear recalibration offset function 2 gain of linear recalibration offset function 3 gain of linear recalibration offset function if function is constrained to zero offset such that the corrected spectral band is Lpotish Lori Log fs gain Lori smooth 5 6 7 Spectral Smile Interpolation For sensors affected by spectral smile the surface reflectance cube is calculated accounting for the smile shift function in the column across track direction of the detector array The smile shift is specified as a 4th order polynomial function i e the file smile_poly_ord4
357. ut file calibrated ENVI image file The file should be statistically as uniform as possible in order to get valid averages Atmospheric Database File non resampled atmospheric database file most appropriate for the selected image take care of the flight altitude and aerosol model Sensor spectral response First band of sensor spectral response file s rsp The correspond ing pressure definition file pressure dat is selected automatically from the same directory as the rsp files if available Output smile coefficients file name of ASCII file which is written and contains the 4th order polynomial coefficients This file may be copied to the sensor definition as smile_poly_ord4 dat for use as smile definition with a sensor Detection resolution finest spectral resolution used for correlation analysis results will be resolved by this resolution Search range maximum total spectral range used for smile detection ie 20 nm is 10 nm search distance For FWHM detection it is the maximum factor to be applied A factor of 2 e g means to search between FWHM scaling from 0 5 to 2 Band range bands which shall be used for smile detection and for which the coefficients are written to the output file Split band First band of second detector for sensor having more than one detector starting numbering at 1 NOTE for imagers with more than two detector it is recommended to derive the smile separately for each detector to a
358. ution of 10 degrees 5 degrees is recommended For large images it causes a high execution time which can be reduced by selecting an undersampling factor of 3 pixels A high angular resolution is more important than a low undersampling factor 5 3 5 Cast Shadow Mask The calculation of the cast shadow map is done by ATCOR after reading the DEM files If the shadow map is computed on the fly it is kept in memory and it is not stored as a separate file If the user wants to inspect the DEM shadow map the program shadow has to be started before running ATCOR The program accepts float values of the solar zenith and azimuth angles The output file name of the DEM cast shadow map includes the zenith and azimuth angles rounded to integer values The DEM cast shadow map is a binary file where shadow pixels are coded with 0 and sunlit pixels with 1 It includes self shadowing and cast shadow effects Self shadowing consists of pixels oriented away from the sun with slopes steeper than the solar elevation angle The cast shadow calculation is based on a ray tracing algorithm and includes shadow regions caused by higher surrounding mountains Figure 5 26 shows the GUI panel CHAPTER 5 DESCRIPTION OF MODULES 81 Figure 5 24 Panel of SKYVIEW 5 3 6 Image Based Shadows This routine detects the fractional shadows in the image using a multi index based approach A floating point illumination file is saved to be used as input for atmospheric corr
359. val sccccocesecsserserevesees Y Yes No HazerorSuniGlintiRemoval wusessssacrdicccess esecirasetee Yes No Shadow Removal Clouds Buildings ssscccccesesesseecs Y Yes No Shadow Removal Clouds Buildings sesseeeeersreeeeees Y Yes No Value Added Products ssssseesssesesessesesossesoseree Y Yes No Value Added Products essesssesseeseeessessseseeoseese Y Yes No Cirrus Removal ssscsccccccrcccccsessecssccsevoveves O Yes No Solar Flux at Ground sscsccccscesvesvereeeesocscoocs Y Yes No Solar Flux at Ground sssccccccscccescesvevserseesees Y Yes No Figure 5 42 Image processing options Right panel appears if a cirrus band exists Options that are not allowed for a specific sensor will appear insensitive If the haze removal option is selected in combination with Variable Visibility the visibility index proportional to total optical thickness map is coded with the values 0 182 The value visindex 0 corresponds to visibility 190 km each integer step of 1 corresponds to an AOT increase of 0 006 The array serves as a fast method of addressing the radiative transfer quantities transmittance path radiance etc in case of a spatially varying visibility i e in combination with the DDV algorithm IF the Haze or Sunglint Removal button is selected the next panel will ask for haze removal over land option 1 haze or sunglint removal over water option 2 or haze removal over land and water option 3 I
360. values at intersection bands are not reliable If intersections of spectra cannot be avoided a larger number of spectra should be used if possible to increase the reliability of the calibration 2 5 De shadowing Remotely sensed optical imagery of the Earth s surface is often contaminated with cloud and cloud shadow areas Surface information under cloud covered regions cannot be retrieved with optical sensors because the signal contains no radiation component being reflected from the ground In shadow areas however the ground reflected solar radiance is always a small non zero signal because the total radiation signal at the sensor contains a direct beam and a diffuse reflected skylight component Even if the direct solar beam is completely blocked in shadow regions the reflected diffuse flux will remain see Fig 2 6 Therefore an estimate of the fraction of direct solar irradiance for a fully or partially shadowed pixel can be the basis of a compensation process called de shadowing or shadow removal The method can be applied to shadow areas cast by clouds or buildings Figure 2 7 shows an example of removing cloud shadows from HyMap imagery It is a sub scene of a Munich flight line acquired 25 May 2007 with a flight altitude of 2 km above ground level Occasional clouds appeared at altitudes higher than the aircraft cruising altitude After shadow removal many details can be seen that are hidden in the uncorrected scene The bottom par
361. void artefacts in the transition range Visibility horizontal visibility as of modtran conventions km Solar Zenith angle measured from zenith to sun Mean Ground Elevation Ground altitude in km Flight Altitude in km a s l Feature wavelength 17 selectable features used for smile detection the per band smile is inter polated from these feature wavelengths Interpolation type interpolation used to expand the feature wavelength results to all spectral bands Extrapolation type specifies how the bands outside of the selected features are treated Repeat repeats the last value toward the borders to zero gradually decrease to zero at border of detector CHAPTER 5 DESCRIPTION OF MODULES 127 Actions Detect Smile The module will perform the smile detection calculation Detect FWHM The FWHM variations are calculated instead of smile variations by the same technique Plot Smile Starts a plotting window to check the smile and lets you select suitable features for calibration Save Report Saves an informational report about the smile detection Outputs An ASCII file which may be used as smile or FWHM description file in the respective sensor directory Note that this file should be named smile_poly_ord4 dat or smile_poly_ord4_fwhm dat in order to be automatically recognized by ATCOR As a side output an IDL save dump x sav is written in parallel which contains all used param eters and the effectivel
362. ws to select the band s to display either a true color CIR color or a single band mode may be selected After display the following options are available within from the menu File Display ENVI Image Displays an additional ENVI image in a new window sorry no link ing available File Show ENVI Header Displays the ENVI header of the current image in a new editable window This allows to make changes to the ENVI header Note that the file needs to be loaded from scratch if changes have been made File Band Selection Allows to select a new combination of spectral bands and updates the dis play File Display TIFF Image Loads a multi band TIFF image in a new window File Close Closes the window Edit Equalize Image Performs a histogram equalization on the three bands Edit Scale Image Applies standard linear scaling on the imagery on 5 levels Edit Scale Zoom Applies standard linear scaling on the imagery on 5 levels based on the statistics of the Zoom Window Edit No Scaling Reverts to unscaled display of the image Edit Scale to Range Scales a single band image linearly to a range entered as lower and upper limit only applicable in single band displays Edit Load Color Table Applies standard linear scaling on the imagery on 5 levels Profile Horizontal Opens a window for a horizontal profile through the image of the first band only The profile is updated for the cursor location in the zoom window whenever the zoom window
363. xaggerates the de shadowing results but it can be used to enhance the trends for a quick visual inspection When pressing the button Run Interactive De Shadowing a reduced size image of the original scene the shadow mask and the de shadowed image will pop up with the histogram of PhiU containing the threshold for core areas point 1 of Fig 5 40 the range of re scaling of PhiU point 2 and the current value for PhiS point 3 Now these three parameters can be modified and the results will again be shown as the corresponding quicklook image and histogram When good results have been obtained the parameters can be saved and the final image processing can take place Figure 5 41 presents an example with two iterations Results for iteration 1 contain too many shadow pixels black areas in the central section of the image therefore the threshold 0 15 was decreased to 0 38 parameter 1 of Fig 5 40 After de shadowing most areas in the shadow mask were overcorrected in iteration 1 therefore the maximum range 0 40 was decreased to 0 19 parameter 2 of Fig 5 40 see diagonal line from lower left to upper right in histogram of Fig 5 41 The shadow mask for iteration 2 is appropriate and no overcorrection effects can be observed CHAPTER 5 DESCRIPTION OF MODULES 96 Figure 5 40 Panel to define the parameters for interactive de shadowing Note when leaving the panel of Fig 5 40 it is possible to edit the cloud shadow map be
364. xel and spectral band is given as PBRF Piso Sua Kvoli fgeoK geo 10 123 where piso is the isotropic reflectance defined at nadir for both illumination and observation angle The kernel factors fy and fyeo are weighting coefficients for the respective kernels They depend on the ground coverage BRDF whereas the kernels are fixed functions which define a fully bi directional reflectance property The kernels have been selected according to the findings of BRDF literature 95 For the volume scattering the Ross Thick kernel is modified to include the hot spot extension by Maignan i e Kool G c cos sin gt 10 124 37 cos 0 cos 0r where arccos cos 6 cos 0 sin 0 sin 8 cos The angle 6 is the incident solar zenith angle 0 is the observation zenith angle and is the relative azimuth angle i e the difference between incidence and observation azimuth The extension of this volumetric kernel by Maignan is given as Koo Koi 5 1 ae 10 125 The reciprocal Li Sparse kernel is used for the geometric part It is defined as 1 4 1 4 1 cos cos cos6 2cos6 cos 0 1 1 1 Keo t sin t cos t T 10 126 cos cos 0 where tan26 tan26 2 tan 6 tan 9 cos tan 6 tan O cos q t arccos x i ro D 1 cos 6 cos Or CHAPTER 10 THEORETICAL BACKGROUND 237 10 6 4 BRDF cover index A cont
365. xternal water map always has the first priority ksolflux 0 file with value added channels not calculated ksolflux 1 value added channels are calculated flx file ishadow 0 and fshd empty string no DEM cast shadow map is used ishadow 0 and fshd valid file name pre calculated DEM cast shadow file is used ishadow 1 DEM cast shadow mask is calculated on the fly The pre calculated map avoids repeated on the fly calculations icl shadow 0 no cloud building shadow correction icl shadow gt 0 cloud building shadow correction is performed 1 de shadowed output as DN image corresponding to input DN scene 2 de shadowed output as surface reflectance image 3 de shadowed output as surface reflectance and DN image If a float _ilu bsq file exists in the scene folder then the de shadowing is performed with this file i e the matched filter algorithm is not applied Otherwise this _ilu bsq has to be renamed temporarily if the matched filter method shall be executed line 22 0 0 5 0 5 itriang ratio_red_swir ratio_blu_red itriang 0 average vis index of reference areas is employed for non reference pixels itriang 1 triangular interpolation of visibility index of reference areas ratio_red_swir ratio of surface reflectance of red to 2 2 wm band for the reference pixels If no 2 2 um band exists but a 1 6 um band the ratio holds for the red to 1 6 wm band CHAPTER 9 I
366. y calculated smile results in array smileresult 4 ncols 17 Here the first dimension 4 contains the center wavelength nm smile shift nm atmospheric transmittance and correlation coefficient The second dimension ncols refers to the number of image columns and the last dimension 17 contains results for the 17 feature wavelengths For example the cen ter wavelengths for all across track pixels and the 760 nm feature are stored in smileresult 0 5 because this feature is the third one and IDL arrays start with index 0 eee X ATCOR Inflight Smile Detection Select Input File Name fcubes apex zuerich_2014 MO070_ZRH_L_140411_a011u_calibr_cubegot Select Atmospheric Database Filet Verc_1d1 atcor atcor_4 atn databaso h07000_w20_rura bp7 Select Sensor Spectral Response Yarc_141 atcor atoor_4 sensor APEX_2015 001 r9p Sensor Calibration Pressure src_id atcor atcor_4 sensor APEX_2015 pressure dat Define Output Smile Coefficients Yeubes apex snile_paly_ord dat sho Detection Resolution nm os0000 Search Range nm factor zo Band Range fa to 299 Split Band Visibility km 70 0000 Solar Zenith deg P2 00 Mean Ground Elevation km s42000 Flight Altitude km 7 08000 Feature Wavelengths nn EE ELE F 430 F 486 527 F 586 W 686 F 760 F 820 M 940 M 1130 F 1268 Faso Fas2 FF 2004 M 2055 M 2200 P 2317 F 2420 Spectral Interpolation Type Linear w Spline Extrapolation Type to detector li
367. z unit m w dm w cm Slope degree puto_husens slp Aspect degree puto husens asp Optional Files Sky View Factor 2 Hpwl0_hysens_sky Cast Shadow 0 1 f w Use pre calculated shadow file 2 Shadow map calculated on the fly requires more memory gt Check line2_geo_ilu for possible DEM related artifacts Message Cancel DK Figure 4 8 Panel for DEM files 4 6 is left and the SPECTRA or IMAGE PROCESSING sections are entered all information is written to a processing initialization inn file e g mage inn When reloading the input file this information is read from the inn file so a new specification of all processing parameters is not necessary Therefore this inn file can also be used for a batch processing see chapter 6 4 3 Survey of processing steps Figure 4 9 shows the typical workflow of atmospheric correction A detailed description of the corresponding graphicical user interface for each module is given in chapter 5 First the image is loaded with possibly some additional information DEM files Then the sensor has to be defined the radiometric calibration file and a basic atmosphere aerosol type combination e g a summer atmosphere with a rural aerosol It is recommended to check the validity of the calibration and to estimate the visibility and perhaps the atmospheric water vapor column wv before processing the image cube The SPECTRA modu

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