Mercurial > repos > ecology > srs_pca

annotate pca_raster.xml @ 1:9300a492b634 draft default tip

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planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit 0f331043178bbfbe5cda0e31d887971464b3071c
author ecology
date Fri, 07 Apr 2023 15:58:46 +0000
parents 79bcb8c6dff0
children
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79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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1 <tool id="srs_pca" name="Compute a PCA raster" version="@VERSION@" profile = "20.01">
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2 <description>from remote sensing data</description>
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3 <macros>
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4 <import>macro.xml</import>
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5 </macros>
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6 <expand macro="SRS_requirements"/>
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7 <command detect_errors="exit_code"><![CDATA[
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8 #import re
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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9 #if $method.origin == 'envi_bil':
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10 #set input_raster = $method.input_raster
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11 #set input_raster_identifier = re.sub('[^\s\w\-]', '_', str($input_raster.element_identifier))
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12 #set input_header = $method.input_header
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13 #set input_header_identifier = re.sub('[^\s\w\-]+[^.hdr]', '_', str($input_header.element_identifier))
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14 cp '${input_raster}' '${input_raster_identifier}' &&
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15 cp '${input_header}' '${input_header_identifier}' &&
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16 #end if
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17 Rscript
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18 '$__tool_directory__/pca_raster.r'
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19 #if $method.origin == 'envi_bil':
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20 '$input_raster_identifier'
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21 '$input_header_identifier'
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22 ''
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23 #else:
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24 ''
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25 ''
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26 '$method.input'
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27 #end if
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28 '$type'
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29 '$__tool_directory__/functions.r'
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30 '$output_raster'
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31 '$output_pca'
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32 '$plots'
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33 ]]>
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34 </command>
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35 <inputs>
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36 <conditional name="method">
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37 <param name="origin" type="select" label="In which format are your data ?">
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38 <option value="zipper">The data you are using are in a zip folder Reflectance</option>
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39 <option value="envi_bil">Your already have the files ENVI BIL</option>
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40 </param>
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41 <when value="zipper">
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42 <param name="input" type="data" format="zip" multiple="true" label="Input data"/>
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43 </when>
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44 <when value="envi_bil">
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45 <param name="input_raster" type="data" format="bil" label="Input raster"/>
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46 <param name="input_header" type="data" format="hdr" label="Input raster header"/>
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47 </when>
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48 </conditional>
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49 <param name="type" type="select" label="Do you want to do a PCA or a SPCA ?" display="radio" help="If you choose PCA there is no rescaling of the data as oppposed as if you choose SPCA">
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50 <option value="SPCA">SPCA</option>
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51 <option value="PCA">PCA</option>
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52 </param>
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53 </inputs>
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54 <outputs>
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55 <collection type="list" name="output_raster" label="PCA raster">
1
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56 <discover_datasets pattern="(?P&lt;designation&gt;.+)" directory="RESULTS/" visible="false" recurse="true" format="bil"/>
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57 <discover_datasets pattern="(?P&lt;designation&gt;.+)" directory="RESULTS/" visible="false" recurse="true" format="hdr"/>
0
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58 </collection>
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59 <data name="output_pca" from_work_dir="PCA.tabular" format="tabular" label="PCA tabular"/>
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60 <collection type="list" name="plots" label="PCA plot">
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61 <discover_datasets pattern="(?P&lt;designation&gt;.+)\.png" visible="false" format="png"/>
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62 </collection>
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63 </outputs>
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64 <tests>
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65 <test>
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66 <param name="origin" value="envi_bil"/>
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67 <param name="input_raster" value="S2A_Subset"/>
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68 <param name="input_header" value="S2A_Subset.hdr"/>
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69 <param name="type" value="PCA"/>
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70 <output_collection name="output_raster" type="list" count="5"/>
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71 <output name="output_pca">
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72 <assert_contents>
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73 <has_n_lines n="960435"/>
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74 </assert_contents>
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75 </output>
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76 <output_collection name="plots" type="list" count="1"/>
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77 </test>
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78 </tests>
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79 <help><![CDATA[
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80 ========================================================================
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81 Process satellite remote sensing data to produce biodiversity indicators
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82 ========================================================================
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83
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84
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85 **What it does**
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86
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87 Féret and Asner (2014) developed a method for **tropical forest** diversity mapping based on very high spatial resolution airborne imaging spectroscopy.
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88
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89 The goal of this tool using the package biodivMapR is to produce a Principal Components Analysis (PCA) based on (optical) images. Principal component analysis prepares for feature extraction in order to reduce noise in the image. It is performed on a random subset of the image to ensure computational efficiency for large images.
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90
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91 **Input description**
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92
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93 It expects an image file as input, with a specific data format. ENVI HDR image with BIL interleave required.
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94 The image is an ENVI raster including :
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95
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96 - A binary file (which has no extension here).
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97
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98 - A header file (with .hdr extension).
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99
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100 The header file is a text file including all necessary metadata which can be read with a text editor. It includes image dimensions, projection, and the name and central wavelength for each spectral band.
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101
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102 In order to get such input we advise to use the tool preprocessing sentinel 2 data.
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103
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104 +--------------+----------+
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105 | BIL | ENVI HDR |
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106 +==============+==========+
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107 | raster stack | Metadata |
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108 +--------------+----------+
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109 | ... | ... |
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110 +--------------+----------+
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111
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112 **Output**
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113
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114 - One tabular with 3 columns : longitude, latitude and pca.
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115
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116 - One png graph.
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117
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118 - One ENVI BIL files with the one file for the bil raster and one file for the hdr header.
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119 ]]> </help>
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120 <expand macro="SRS_BDMRref"/>
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121 </tool>