<root>
  <key>FusionOfClassifications-dempstershafer</key>
  <exec>otbcli_FusionOfClassifications</exec>
  <longname>FusionOfClassifications (dempstershafer)</longname>
  <group>Learning</group>
  <description>Fuses several classifications maps of the same image on the basis of class labels.</description>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputImageList">ParameterMultipleInput</parameter_type>
    <key>il</key>
    <name>Input classifications</name>
    <description>List of input classification maps to fuse. Labels in each classification image must represent the same class.</description>
    <datatype />
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
    <key>method</key>
    <name>Fusion method</name>
    <description>Selection of the fusion method and its parameters.</description>
    <options>
      <choices>
        <choice>dempstershafer</choice>
      </choices>
    </options>
    <default>0</default>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputFilenameList">ParameterMultipleInput</parameter_type>
    <key>method.dempstershafer.cmfl</key>
    <name>Confusion Matrices</name>
    <description>A list of confusion matrix files (*.CSV format) to define the masses of belief and the class labels. Each file should be formatted the following way: the first line, beginning with a '#' symbol, should be a list of the class labels present in the corresponding input classification image, organized in the same order as the confusion matrix rows/columns.</description>
    <datatype />
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
    <key>method.dempstershafer.mob</key>
    <name>Mass of belief measurement</name>
    <description>Type of confusion matrix measurement used to compute the masses of belief of each classifier.</description>
    <options>
      <choices>
        <choice>precision</choice>
        <choice>recall</choice>
        <choice>accuracy</choice>
        <choice>kappa</choice>
      </choices>
    </options>
    <default>0</default>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>nodatalabel</key>
    <name>Label for the NoData class</name>
    <description>Label for the NoData class. Such input pixels keep their NoData label in the output image and are not handled in the fusion process. By default, 'nodatalabel = 0'.</description>
    <minValue />
    <maxValue />
    <default>0</default>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>undecidedlabel</key>
    <name>Label for the Undecided class</name>
    <description>Label for the Undecided class. Pixels with more than 1 fused class are marked as Undecided. Please note that the Undecided value must be different from existing labels in the input classifications. By default, 'undecidedlabel = 0'.</description>
    <minValue />
    <maxValue />
    <default>0</default>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_OutputImage">OutputRaster</parameter_type>
    <key>out</key>
    <name>The output classification image</name>
    <description>The output classification image resulting from the fusion of the input classification images.</description>
    <hidden />
  </parameter>
</root>
