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Multiple Linear RegressionPANCROMA TM has a utility for regressing one independent variable (bands)against a second dependent variable band, i.e. solving the equationY = a + b 1 X 1Where X1 represents an independent multispectral or hyperspectral band file andY represents the dependent band file. In order to compute the slope andintercept terms, select ‘File’ | ‘Open’ and open the dependent (Y) band first, andthen the X 1 band. (You must open the Y band first.) Then select ‘PreProcess’ |‘Multiple Regression’ | ‘One Independent Variable’. When the Data Entry boxbecomes visible, select the sampling interval. (If the sampling interval is one,every DN will be included in the computation and it will take longer. If the intervalis 2, every other DN will be computed, etc.) The linear coefficient terms will bereported to the diagnostic screen on the Main Window. They will alsoautomatically be inserted into the Data Entry Form for subsequent use for lineartransformations. Note that zero values are ignored. This is to prevent theintercept from being skewed as a result of zero value collar pixels.205
PANCROMA TM has a utility for regressing two independent variable (bands)against a third dependent variable band, i.e. solving the equationY = a + b 1 X 1 + b 2 X 2Where X1 and X2 represent the independent multispectral or hyperspectral bandfiles and Y represents the dependent band file. In order to compute the slopeand intercept terms, select ‘File’ | ‘Open’ and open the dependent (Y) band first,and then the X 1 and X 2 bands. (You must open the Y band first.) Then select‘PreProcess’ | ‘Multiple Regression’ | ‘Two Independent Variables’. When theData Entry box becomes visible, select the sampling interval as discussed above.The linear coefficient terms will be reported to the diagnostic screen on the MainWindow. They will also automatically be inserted into the Data Entry Form forsubsequent use for linear transformations. Note that zero values are ignored.This is to prevent the intercept from being skewed as a result of zero value collarpixels.PANCROMA TM also has a utility for regressing three independent variable(bands) against a third dependent variable band, i.e. solving the equationY = a + b 1 X 1 + b 2 X 2 + b 3 X 3Where X1, X2 and X3 represent the independent multispectral or hyperspectralband files and Y represents the dependent band file. In order to compute theslope and intercept terms, select ‘File’ | ‘Open’ and open the dependent (Y) bandfirst, and then the X 1 , X 2 and X 3 bands. (You must open the Y band first.) Thenselect ‘PreProcess’ | ‘Multiple Regression’ | ‘Three Independent Variables’.When the Data Entry box becomes visible, select the sampling interval asdiscussed above. The linear coefficient terms will be reported to the diagnosticscreen on the Main Window. They will also automatically be inserted into theData Entry Form for subsequent use for linear transformations. Note that zerovalues are NOT ignored for the three independent variable case. As a result it isnecessary to subset the band files so that no-data collar pixels do not skew theregression intercept.PANCROMA TM also has a utility for regressing four independent variable (bands)against a third dependent variable band, i.e. solving the equationY = a + b 1 X 1 + b 2 X 2 + b 3 X 3 + b 4 X 4Where X1, X2, X3 and X4 represent the independent multispectral orhyperspectral band files and Y represents the dependent band file. In order tocompute the slope and intercept terms, select ‘File’ | ‘Open’ and open thedependent (Y) band first, and then the X 1 , X 2 , X 3 and , X 4 bands. (You must openthe Y band first.) Then select ‘PreProcess’ | ‘Multiple Regression’ | ‘FourIndependent Variables’. When the Data Entry box becomes visible, select the206
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1. What Is PANCROMA TM ? ..........
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Compute Single Histogram...........
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67. Tutorial - Pan Sharpening Full
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2. What Useful Things Can PANCROMA
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4. PANCROMA TM Features____________
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5. System Requirements_____________
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7. Getting Started_________________
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Each color and NIR band channel con
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determine if one number is differen
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will be shown at the bottom of the
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12. File I/O_______________________
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13. System Variables_______________
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14. Display________________________
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15. The PANCROMA User Interface____
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not find this file , you will be pr
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If you check the ‘Generate Graysc
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19. Image Subsetting_______________
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IMPORTANT NOTE: There is a check bo
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IMPORTANT NOTE: Saving and opening
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When the Input Box becomes visible,
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20. Batch Subsetting_______________
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21. Quick Start - Pan Sharpening an
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During the internal processing of t
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is unlikely that the settings will
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The default method is checked. The
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around 4000 rows by 4000 columns. I
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the ‘Pan Sharpening Pre Processin
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lowerLimit upperLimitThe following
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Another type of unwanted artifact i
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matching algorithm to be applied. T
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25. ENHG Algorithm - Manual Method_
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The next image is the result of pan
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26. ENHG Algorithm - Automatic Meth
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Next, compute the XIONG file. Open
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The XIONG algorithm is one of the m
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29. AJISANE Algorithm______________
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An example of a pan sharpened image
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The spectral match is indicated by
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You must be careful when selecting
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31. ELIN Local Optimization Algorit
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IMPORTANT NOTE: Regions of high con
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Add the green band and the red band
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33. Pan Sharpen Batch File Processi
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34. Working with SPOT® Data_______
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the special image processing utilit
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35. Working with ASTER Data________
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36. Working with Formosat-2 Data___
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100
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ALI data is similar to Landsat with
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38. Working with Digital Globe® Da
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spectral transmission of the telesc
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ow. Make sure you check this before
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40. Working with Landsat Surface Re
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41. Working with Landsat GEOCOVER D
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foreground and background applicati
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GEOCOVER can offer a very attractiv
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After navigation and selecting your
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Although not designed as a data sou
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44. Working with USGS DOQQ and NAPP
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46. Working with MODIS Surface Refl
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47. Working with RapidEye Data_____
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The Transfer method processes the f
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using the PANCROMA TM ‘Preprocess
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and band 5 (MIR) also be input. Thi
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134
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50. Automatic Image Registration of
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The next two images are the registe
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At the present time, PANCROMA TM ca
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Since only a portion of EMR reachin
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144
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‘File’ | ‘Open’. After your
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Haze reduction may be useful in con
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Save your image using GeoTiff file
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The correction is made according to
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Page 157 and 158: Input Channel Adjust Group Utility
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Page 163 and 164: 57. Image Processing Utilities - Pa
Page 165 and 166: Channel Level AdjustPANCROMA TM pro
Page 167 and 168: ENHG Scale FactorThe ENHG slider ba
Page 169 and 170: 59. Image Preprocessing____________
Page 171 and 172: select ‘File’ | ‘Open’ and
Page 173 and 174: Analyzer. Doubling is done by first
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Page 177 and 178: There are a few rules of thumb rega
Page 179 and 180: You will need three control points
Page 181 and 182: • image resampling• geometric m
Page 183 and 184: ROWS groupsFirst row of dataSecond
Page 185 and 186: Where QCAL are the calibrated image
Page 187 and 188: When you select ‘OK’ the surfac
Page 189 and 190: The rendering below shows a color t
Page 191 and 192: Q cal = Quantized and calibrated st
Page 193 and 194: A section of a SPOT metadata file s
Page 195 and 196: Temperature (measured in degrees Ke
Page 197 and 198: Calibration requires multiplication
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Page 203 and 204: X-Y Band Scatter PlotsA scatter plo
Page 205: presented. Check the ‘Compute Lin
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Page 211 and 212: You can adjust weighting factors fo
Page 213 and 214: Unsupervised ClassificationUnsuperv
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Page 217 and 218: Grayscale and Color Image TilingIt
Page 219 and 220: GeoreferencingPANCROMA TM has a uti
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Page 223 and 224: When you click ‘OK’, the select
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Page 239 and 240: The next step is to overlay the Lan
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Page 243 and 244: Although the run was not successful
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Page 247 and 248: Now select ‘Spectral Analysis’
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Page 251 and 252: Click ‘OK’. The TOA Reflectance
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Euclidean Distance Fraction TM Anal
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The idea is to adjust the included
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In addition to Landsat 7, Spectral
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Low numbers mean high discriminatio
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Copyright USGSAlthough this data is
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Note that the band 6 (TIR) track ba
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higher (whiter) the grayscale pixel
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The table with the manually input r
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this does not correspond to physica
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L71005068_06820030919_B10.TIFL71005
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L71005068_06820040804_B50.TIFL71005
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278
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The parameters are as follows:Compu
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Object Oriented ClassificationPANCR
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to the other. For example a filter
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carefully. PANCROMA TM is able to p
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Landsat imageImage magnitude spectr
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Considerable smoothing has occurred
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IMPORTANT NOTE: In reality, the con
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The following is an example of an i
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In order to deconvolve using fixed
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63. Writing an Index File for an SR
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It will be saved as a .dat file. A
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64. Processing Huge Files and File
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My area of interest for this image
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306
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Now we will start the pan sharpenin
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66. Tutorial - Pan Sharpened Image
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The next step will be to create an
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The panchromatic image is the one i
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And my result looked like this:As y
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67. Tutorial - Pan Sharpening Full
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68. Tutorial - Gap Filling using Tr
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You can use the 'Max Cloud' drop do
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'Enter' and the Adjust file will be
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PANCROMA TM does the rest after tha
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70. Tutorial - Gap Filling using Ha
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Another reason to keep the Search E
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Gap Filled Grayscale ImageGap Fille
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When you do so a batch processing d
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The images below show subsets taken
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340
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It is also apparent that the graysc
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73. Tutorial - Six File TERAS Batch
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common areas of both scenes. See th
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348
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Use the checked image processing ut
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IssuesAlthough the gap filling proc
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76. Tutorial - Pan Sharpening Lands
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77. Tutorial - Image Processing Wor
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From Next PageSection 13Subset One
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I again selected ‘Close Graphic I
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I then selected ‘Pan Sharpening
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the vegetation tones are still norm
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Reduction’ and then ‘Apply Cont
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acquired during different seasons o
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This image is better than the Trans
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I also prepared an image using the
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Full sized Landsat scenes can be pr
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78. Tutorial - Improving Image Qual
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The following image is the RGB colo
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Press ‘OK’. The transformed gra
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However it is close enough to illus
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The second image is a straightforwa
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It is also possible to combine the
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80. Pan Sharpened Image: Landsat Mo
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82. Pan Sharpened Image: Landsat De
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88. Pan Sharpened Image: EO-1 Mount
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Multiple Linear Regression, 205Mult
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