User Manual - pancroma

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When superimposed one over the other the features match very closely. Thedetails to the below show the boundary area between the two image edges, onefully opaque and one semi-transparent. (The image edges do not matchperfectly because PANCROMA TM matches the collared area corners, not theexposed image within). As you can see, the road and dry watercourse featuresalign almost perfectly along the western edge of the image.139
At the present time, PANCROMA TM can only match three-file data sets using theautomatic utility, and data must be in GeoTiff format. Subsetting is to MaximumCommon Extents only. I will probably expand the utility to handle BMP andJPEG format and also four-file data sets for pan sharpening. However I believethat three-file sets represent the majority of maximum.140
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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
Page 89 and 90: Add the green band and the red band
Page 91 and 92: 33. Pan Sharpen Batch File Processi
Page 93 and 94: 34. Working with SPOT® Data_______
Page 95 and 96: the special image processing utilit
Page 97 and 98: 35. Working with ASTER Data________
Page 99 and 100: 36. Working with Formosat-2 Data___
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Page 103 and 104: ALI data is similar to Landsat with
Page 105 and 106: 38. Working with Digital Globe® Da
Page 107 and 108: spectral transmission of the telesc
Page 109 and 110: ow. Make sure you check this before
Page 111 and 112: 40. Working with Landsat Surface Re
Page 113 and 114: 41. Working with Landsat GEOCOVER D
Page 115 and 116: foreground and background applicati
Page 117 and 118: GEOCOVER can offer a very attractiv
Page 119 and 120: After navigation and selecting your
Page 121 and 122: Although not designed as a data sou
Page 123 and 124: 44. Working with USGS DOQQ and NAPP
Page 125 and 126: 46. Working with MODIS Surface Refl
Page 127 and 128: 47. Working with RapidEye Data_____
Page 129 and 130: The Transfer method processes the f
Page 131 and 132: using the PANCROMA TM ‘Preprocess
Page 133 and 134: and band 5 (MIR) also be input. Thi
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Page 137 and 138: 50. Automatic Image Registration of
Page 139: The next two images are the registe
Page 143 and 144: Since only a portion of EMR reachin
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Page 147 and 148: ‘File’ | ‘Open’. After your
Page 149 and 150: Haze reduction may be useful in con
Page 151 and 152: Save your image using GeoTiff file
Page 153 and 154: The correction is made according to
Page 155 and 156: 54. Image Processing Utilities - Gr
Page 157 and 158: Input Channel Adjust Group Utility
Page 159 and 160: 55. Image Processing Utilities - SP
Page 161 and 162: 56. Image Processing Utilities - AS
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
Page 175 and 176: high pixel brightness in one image
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
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Q cal = Quantized and calibrated st
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A section of a SPOT metadata file s
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Temperature (measured in degrees Ke
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Calibration requires multiplication
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NIR band, band 2 (green) and band 3
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and 1 (Brightness, a measure of soi
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X-Y Band Scatter PlotsA scatter plo
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presented. Check the ‘Compute Lin
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PANCROMA TM has a utility for regre
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60. Image Post Processing__________
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You can adjust weighting factors fo
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Unsupervised ClassificationUnsuperv
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214
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Grayscale and Color Image TilingIt
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GeoreferencingPANCROMA TM has a uti
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61. Spectral Analysis______________
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When you click ‘OK’, the select
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The plot shows all of the computed
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PANCROMA TM computed that the cover
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Landsat Reflectance Point Spectrum
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SPOT ® Point Spectrum Generator TM
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will become disabled and the Color
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When the ‘Zoom Disabled’ radio
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Digital spectra are recorded for mi
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The next step is to overlay the Lan
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http://www.science.aster.ersdac.or.
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Although the run was not successful
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The pan sharpened image was created
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Now select ‘Spectral Analysis’
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RapidEye ® Spectral Analyzer TMSpe
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Click ‘OK’. The TOA Reflectance
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The second plot is shown below, dis
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have to rationalize the file sizes.
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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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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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