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<root>
<key>TrainImagesClassifier-ann</key>
<exec>otbcli_TrainImagesClassifier</exec>
<longname>TrainImagesClassifier (ann)</longname>
<group>Learning</group>
<description>Train a classifier from multiple pairs of images and training vector data.</description>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputImageList">ParameterMultipleInput</parameter_type>
<key>io.il</key>
<name>Input Image List</name>
<description>A list of input images.</description>
<datatype />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputVectorDataList">ParameterMultipleInput</parameter_type>
<key>io.vd</key>
<name>Input Vector Data List</name>
<description>A list of vector data to select the training samples.</description>
<datatype />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
<key>io.imstat</key>
<name>Input XML image statistics file</name>
<description>Input XML file containing the mean and the standard deviation of the input images.</description>
<isFolder />
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
<key>io.confmatout</key>
<name>Output confusion matrix</name>
<description>Output file containing the confusion matrix (.csv format).</description>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
<key>io.out</key>
<name>Output model</name>
<description>Output file containing the model estimated (.txt format).</description>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>elev.default</key>
<name>Default elevation</name>
<description>This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value.</description>
<minValue />
<maxValue />
<default>0</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>sample.mt</key>
<name>Maximum training sample size per class</name>
<description>Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio.</description>
<minValue />
<maxValue />
<default>1000</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>sample.mv</key>
<name>Maximum validation sample size per class</name>
<description>Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio.</description>
<minValue />
<maxValue />
<default>1000</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>sample.bm</key>
<name>Bound sample number by minimum</name>
<description>Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1).</description>
<minValue />
<maxValue />
<default>1</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Empty">ParameterBoolean</parameter_type>
<key>sample.edg</key>
<name>On edge pixel inclusion</name>
<description>Takes pixels on polygon edge into consideration when building training and validation samples.</description>
<default>True</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>sample.vtr</key>
<name>Training and validation sample ratio</name>
<description>Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5).</description>
<minValue />
<maxValue />
<default>0.5</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_String">ParameterString</parameter_type>
<key>sample.vfn</key>
<name>Name of the discrimination field</name>
<description>Name of the field used to discriminate class labels in the input vector data files.</description>
<default>Class</default>
<multiline />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier</key>
<name>Classifier to use for the training</name>
<description>Choice of the classifier to use for the training.</description>
<options>
<choices>
<choice>ann</choice>
</choices>
</options>
<default>0</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier.ann.t</key>
<name>Train Method Type</name>
<description>Type of training method for the multilayer perceptron (MLP) neural network.</description>
<options>
<choices>
<choice>reg</choice>
<choice>back</choice>
</choices>
</options>
<default>0</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_StringList">ParameterString</parameter_type>
<key>classifier.ann.sizes</key>
<name>Number of neurons in each intermediate layer</name>
<description>The number of neurons in each intermediate layer (excluding input and output layers).</description>
<default />
<multiline />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier.ann.f</key>
<name>Neuron activation function type</name>
<description>Neuron activation function.</description>
<options>
<choices>
<choice>ident</choice>
<choice>sig</choice>
<choice>gau</choice>
</choices>
</options>
<default>1</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.a</key>
<name>Alpha parameter of the activation function</name>
<description>Alpha parameter of the activation function (used only with sigmoid and gaussian functions).</description>
<minValue />
<maxValue />
<default>1</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.b</key>
<name>Beta parameter of the activation function</name>
<description>Beta parameter of the activation function (used only with sigmoid and gaussian functions).</description>
<minValue />
<maxValue />
<default>1</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.bpdw</key>
<name>Strength of the weight gradient term in the BACKPROP method</name>
<description>Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1.</description>
<minValue />
<maxValue />
<default>0.1</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.bpms</key>
<name>Strength of the momentum term (the difference between weights on the 2 previous iterations)</name>
<description>Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough.</description>
<minValue />
<maxValue />
<default>0.1</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.rdw</key>
<name>Initial value Delta_0 of update-values Delta_{ij} in RPROP method</name>
<description>Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1).</description>
<minValue />
<maxValue />
<default>0.1</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.rdwm</key>
<name>Update-values lower limit Delta_{min} in RPROP method</name>
<description>Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7).</description>
<minValue />
<maxValue />
<default>1e-07</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier.ann.term</key>
<name>Termination criteria</name>
<description>Termination criteria.</description>
<options>
<choices>
<choice>iter</choice>
<choice>eps</choice>
<choice>all</choice>
</choices>
</options>
<default>2</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.eps</key>
<name>Epsilon value used in the Termination criteria</name>
<description>Epsilon value used in the Termination criteria.</description>
<minValue />
<maxValue />
<default>0.01</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>classifier.ann.iter</key>
<name>Maximum number of iterations used in the Termination criteria</name>
<description>Maximum number of iterations used in the Termination criteria.</description>
<minValue />
<maxValue />
<default>1000</default>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>rand</key>
<name>set user defined seed</name>
<description>Set specific seed. with integer value.</description>
<minValue />
<maxValue />
<default>0</default>
</parameter>
</root>
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