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<root>
<key>KMeansClassification</key>
<exec>otbcli_KMeansClassification</exec>
<longname>Unsupervised KMeans image classification</longname>
<group>Learning</group>
<description>Unsupervised KMeans image classification</description>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputImage">ParameterRaster</parameter_type>
<key>in</key>
<name>Input Image</name>
<description>Input image to classify.</description>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputImage">OutputRaster</parameter_type>
<key>out</key>
<name>Output Image</name>
<description>Output image containing the class indexes.</description>
<hidden />
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_RAM">ParameterNumber</parameter_type>
<key>ram</key>
<name>Available RAM (Mb)</name>
<description>Available memory for processing (in MB)</description>
<minValue />
<maxValue />
<default>128</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputImage">ParameterRaster</parameter_type>
<key>vm</key>
<name>Validity Mask</name>
<description>Validity mask. Only non-zero pixels will be used to estimate KMeans modes.</description>
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>ts</key>
<name>Training set size</name>
<description>Size of the training set (in pixels).</description>
<minValue />
<maxValue />
<default>100</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>nc</key>
<name>Number of classes</name>
<description>Number of modes, which will be used to generate class membership.</description>
<minValue />
<maxValue />
<default>5</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>maxit</key>
<name>Maximum number of iterations</name>
<description>Maximum number of iterations for the learning step.</description>
<minValue />
<maxValue />
<default>1000</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>ct</key>
<name>Convergence threshold</name>
<description>Convergence threshold for class centroid (L2 distance, by default 0.0001).</description>
<minValue />
<maxValue />
<default>0.0001</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
<key>outmeans</key>
<name>Centroid filename</name>
<description>Output text file containing centroid positions</description>
</parameter>
</root>
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