User Manual - pancroma

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Notice that the areas of the most dense cloud coverage are colored red,indicating the most pronounced change from the base image. Note the smallblue hole in the cloud coverage denoted by the lower arrow. This coincides withone of the few clouds in the base image. This has been correctly identified as aregion of lesser change.This example, although depicting a trivial change, illustrates how the ChangeDistance TM utility can be used to identify much more subtle changes in Landsatscenes with great sensitivity.Multispectral Band Image ClassificationPANCROMA TM provides utilities for image classification using multispectralimage vectors. The procedure for conducting such classifications is similar tothat described in the Post Processing section, except that Landsat 7, LandsatReflectance, Landsat 8, SPOT, ASTER, EO-1 ALI and NAIO images can beclassified using various numbers of bands. For example, you can classify usingsix Landsat 7 DN bands by selecting ‘File’ | ‘Open’ and opening your sixmultispectral band files. Then select ‘Spectral Analysis’ | ‘Landsat UnsupervisedClassifier’ | ‘Six Band DN’. The Classification Input Box will become visible.279
The parameters are as follows:Computation Method: Defines whether the arithmetic mean or the median is usedto calculate the cluster center.Distance Algorithm: Defines which distance measure is used, as given by thetable:• Euclidean distance• City-block distance• Correlation• Absolute value of the correlation• Uncentered correlation• Absolute uncentered correlationNumber of Clusters: i.e. the number of classes computedNumber of Passes: The number of times clustering is performed. Clustering isperformed the selected number of times, each time starting from a different(random) initial assignment of bands to clusters. The clustering solution with thelowest inside-cluster sum of distances is chosen.IMPORTANT NOTE: Classification requires large internal fp32 data structures.Maximum input band sizes will be limited to less than full scenes. Maximuminput band size 8MB-16MB depending on the number of input bands.Select your run parameters and select ‘OK’. Your specified number of best-fitclasses will be statistically computed and displayed as an RGB image. In somecases the classified RGB image may look very similar to the input RGB image.However the classified image will be composed of one RGB color combinationper class. You can highlight one or more of the classes by selecting the dropdown menu on the RGB image and clicking on a target class. Clicking OK on the280
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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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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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54. Image Processing Utilities - Gr
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Input Channel Adjust Group Utility
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55. Image Processing Utilities - SP
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56. Image Processing Utilities - AS
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57. Image Processing Utilities - Pa
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Channel Level AdjustPANCROMA TM pro
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ENHG Scale FactorThe ENHG slider ba
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59. Image Preprocessing____________
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select ‘File’ | ‘Open’ and
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Analyzer. Doubling is done by first
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high pixel brightness in one image
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There are a few rules of thumb rega
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You will need three control points
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• image resampling• geometric m
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ROWS groupsFirst row of dataSecond
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Where QCAL are the calibrated image
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When you select ‘OK’ the surfac
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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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Page 243 and 244: Although the run was not successful
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Page 251 and 252: Click ‘OK’. The TOA Reflectance
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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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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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