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			119 lines
		
	
	
		
			5.3 KiB
		
	
	
	
		
			XML
		
	
	
	
	
	
			
		
		
	
	
			119 lines
		
	
	
		
			5.3 KiB
		
	
	
	
		
			XML
		
	
	
	
	
	
| <root>
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|   <key>TrainImagesClassifier-bayes</key>
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|   <exec>otbcli_TrainImagesClassifier</exec>
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|   <longname>TrainImagesClassifier (bayes)</longname>
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|   <group>Learning</group>
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|   <description>Train a classifier from multiple pairs of images and training vector data.</description>
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|   <parameter>
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|     <parameter_type source_parameter_type="ParameterType_InputImageList">ParameterMultipleInput</parameter_type>
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|     <key>io.il</key>
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|     <name>Input Image List</name>
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|     <description>A list of input images.</description>
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|     <datatype />
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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_InputVectorDataList">ParameterMultipleInput</parameter_type>
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|     <key>io.vd</key>
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|     <name>Input Vector Data List</name>
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|     <description>A list of vector data to select the training samples.</description>
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|     <datatype />
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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>io.imstat</key>
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|     <name>Input XML image statistics file</name>
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|     <description>Input XML file containing the mean and the standard deviation of the input images.</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_OutputFilename">OutputFile</parameter_type>
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|     <key>io.confmatout</key>
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|     <name>Output confusion matrix</name>
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|     <description>Output file containing the confusion matrix (.csv format).</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_OutputFilename">OutputFile</parameter_type>
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|     <key>io.out</key>
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|     <name>Output model</name>
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|     <description>Output file containing the model estimated (.txt format).</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_Float">ParameterNumber</parameter_type>
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|     <key>elev.default</key>
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|     <name>Default elevation</name>
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|     <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>
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|     <minValue />
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|     <maxValue />
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|     <default>0</default>
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|   </parameter>
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|   <parameter>
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|     <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
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|     <key>sample.mt</key>
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|     <name>Maximum training sample size per class</name>
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|     <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>
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|     <minValue />
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|     <maxValue />
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|     <default>1000</default>
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|   </parameter>
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|   <parameter>
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|     <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
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|     <key>sample.mv</key>
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|     <name>Maximum validation sample size per class</name>
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|     <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>
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|     <minValue />
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|     <maxValue />
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|     <default>1000</default>
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|   </parameter>
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|   <parameter>
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|     <parameter_type source_parameter_type="ParameterType_Empty">ParameterBoolean</parameter_type>
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|     <key>sample.edg</key>
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|     <name>On edge pixel inclusion</name>
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|     <description>Takes pixels on polygon edge into consideration when building training and validation samples.</description>
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|     <default>True</default>
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|   </parameter>
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|   <parameter>
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|     <parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
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|     <key>sample.vtr</key>
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|     <name>Training and validation sample ratio</name>
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|     <description>Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5).</description>
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|     <minValue />
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|     <maxValue />
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|     <default>0.5</default>
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|   </parameter>
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|   <parameter>
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|     <parameter_type source_parameter_type="ParameterType_String">ParameterString</parameter_type>
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|     <key>sample.vfn</key>
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|     <name>Name of the discrimination field</name>
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|     <description>Name of the field used to discriminate class labels in the input vector data files.</description>
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|     <default>Class</default>
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|     <multiline />
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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_Choice">ParameterSelection</parameter_type>
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|     <key>classifier</key>
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|     <name>Classifier to use for the training</name>
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|     <description>Choice of the classifier to use for the training.</description>
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|     <options>
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|       <choices>
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|         <choice>bayes</choice>
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|         </choices>
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|     </options>
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|     <default>0</default>
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|   </parameter>
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|   <parameter>
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|     <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
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|     <key>rand</key>
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|     <name>set user defined seed</name>
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|     <description>Set specific seed. with integer value.</description>
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|     <minValue />
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|     <maxValue />
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|     <default>0</default>
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|   </parameter>
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| </root>
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