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<root>
<key>ImageClassifier</key>
<exec>otbcli_ImageClassifier</exec>
<longname>Image Classification</longname>
<group>Learning</group>
<description>Performs a classification of the input image according to a model file.</description>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputImage">ParameterRaster</parameter_type>
<key>in</key>
<name>Input Image</name>
<description>The input image to classify.</description>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputImage">ParameterRaster</parameter_type>
<key>mask</key>
<name>Input Mask</name>
<description>The mask allows restricting classification of the input image to the area where mask pixel values are greater than 0.</description>
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
<key>model</key>
<name>Model file</name>
<description>A model file (produced by TrainImagesClassifier application, maximal class label = 65535).</description>
<isFolder />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
<key>imstat</key>
<name>Statistics file</name>
<description>A XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application).</description>
<isFolder />
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputImage">OutputRaster</parameter_type>
<key>out</key>
<name>Output Image</name>
<description>Output image containing class labels</description>
<hidden />
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputImage">OutputRaster</parameter_type>
<key>confmap</key>
<name>Confidence map</name>
<description>Confidence map of the produced classification. The confidence index depends on the model :
- LibSVM : difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample)
- OpenCV
* Boost : sum of votes
* DecisionTree : (not supported)
* GradientBoostedTree : (not supported)
* KNearestNeighbors : number of neighbors with the same label
* NeuralNetwork : difference between the two highest responses
* NormalBayes : (not supported)
* RandomForest : Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now.
* SVM : distance to margin (only works for 2-class models)
</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>
<optional>True</optional>
</parameter>
</root>
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