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73 lines
3.2 KiB
XML
73 lines
3.2 KiB
XML
<root>
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<key>ImageClassifier</key>
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<exec>otbcli_ImageClassifier</exec>
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<longname>Image Classification</longname>
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<group>Learning</group>
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<description>Performs a classification of the input image according to a model file.</description>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_InputImage">ParameterRaster</parameter_type>
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<key>in</key>
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<name>Input Image</name>
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<description>The input image to classify.</description>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_InputImage">ParameterRaster</parameter_type>
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<key>mask</key>
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<name>Input Mask</name>
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<description>The mask allows restricting classification of the input image to the area where mask pixel values are greater than 0.</description>
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<optional>True</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
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<key>model</key>
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<name>Model file</name>
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<description>A model file (produced by TrainImagesClassifier application, maximal class label = 65535).</description>
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<isFolder />
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
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<key>imstat</key>
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<name>Statistics file</name>
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<description>A XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application).</description>
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<isFolder />
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<optional>True</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_OutputImage">OutputRaster</parameter_type>
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<key>out</key>
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<name>Output Image</name>
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<description>Output image containing class labels</description>
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<hidden />
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_OutputImage">OutputRaster</parameter_type>
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<key>confmap</key>
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<name>Confidence map</name>
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<description>Confidence map of the produced classification. The confidence index depends on the model :
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- LibSVM : difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample)
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- OpenCV
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* Boost : sum of votes
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* DecisionTree : (not supported)
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* GradientBoostedTree : (not supported)
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* KNearestNeighbors : number of neighbors with the same label
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* NeuralNetwork : difference between the two highest responses
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* NormalBayes : (not supported)
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* 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.
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* SVM : distance to margin (only works for 2-class models)
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</description>
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<hidden />
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_RAM">ParameterNumber</parameter_type>
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<key>ram</key>
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<name>Available RAM (Mb)</name>
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<description>Available memory for processing (in MB)</description>
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<minValue />
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<maxValue />
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<default>128</default>
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<optional>True</optional>
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</parameter>
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</root>
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