The KappaCoefficient function method returns the kappa coefficient.

The kappa coefficient measures the agreement between classification and truth values. A kappa value of 1 represents perfect agreement, while a value of 0 represents no agreement. The kappa coefficient is computed as follows:

Where :

  • i is the class number
  • N is the total number of classified values compared to truth values
  • mi,i is the number of values belonging to the truth class i that have also been classified as class i (i.e., values found along the diagonal of the confusion matrix)
  • Ci is the total number of predicted values belonging to class i
  • Gi is the total number of truth values belonging to class i

In the example confusion matrix, the kappa coefficient is 0.990839.

Example

The code example below evaluates classifications using a confusion matrix.

PRO EvaluateClassificationUsingConfusionMatrix
    COMPILE_OPT IDL2
 
    ; Start the application
    e = ENVI()
 
    ; Open an input file
    File = Filepath('qb_boulder_msi', Subdir=['data'], $
    Root_Dir=e.Root_Dir)
    Raster = e.OpenRaster(File)
    File2 = Filepath('qb_boulder_roi.xml', Subdir=['data'], $
    Root_Dir=e.Root_Dir)
    Rois = envi.OpenROI(roiFile)
 
    ; Get training statistics
    StatTask = ENVITask('ROIStatistics')
    StatTask.INPUT_RASTER = Raster
    StatTask.INPUT_ROI = Rois
    StatTask.Execute
 
    ; Get the task from the catalog of ENVITasks
    Task = ENVITask('MahalanobisDistanceClassification')
 
    ; Define inputs
    Task.INPUT_RASTER = Raster
    Task.COVARIANCE = StatTask.Covariance
    Task.MEAN = StatTask.Mean
    Task.CLASS_PIXEL_COUNT = StatTask.Roi_Pixel_Count
    Task.CLASS_NAMES = [Rois[0].name, Rois[1].name, Rois[2].name]
    Task.CLASS_COLORS = [[0,0,255], [0,255,0], [255,0,0]]
 
    ; Run the task and display the result
    Task.Execute
    ClassRaster = Task.OUTPUT_RASTER
    View = e.GetView()
    Layer = View.CreateLayer(ClassRaster)
 
    ; Add the output to the Data Manager
    envi.Data.Add, ClassRaster
 
    ; Calculate the confusion matrix
    ConfusionMatrix = ENVICalculateConfusionMatrixFromRaster(ClassRaster, Rois)
 
    ; Print results
    Print, 'Confusion Matrix:'
    Print, ConfusionMatrix.Confusion_Matrix
    Print, 'Errors of commission: '
    Print, Transpose([[ConfusionMatrix.Column_Names+': '], [(ConfusionMatrix.CommissionError()).ToString()]])
    Print, 'Errors of omission: '
    Print, Transpose([[ConfusionMatrix.Column_Names+': '], [(ConfusionMatrix.OmissionError()).ToString()]])
    Print, 'Overall accuracy: ', ConfusionMatrix.Accuracy()
END

Syntax

Result = ENVIConfusionMatrix.KappaCoefficient([, ERROR=variable])

Return Value

This function method returns the kappa coefficient from the confusion matrix.

Arguments

None

Keywords

ERROR (optional)

Set this keyword to a named variable that will contain any error message issued during execution of this routine. If no error occurs, the ERROR variable will be set to a null string (''). If an error occurs and the routine is a function, then the function result will be undefined.

When this keyword is not set and an error occurs, ENVI returns to the caller and execution halts. In this case, the error message is contained within !ERROR_STATE and can be caught using IDL's CATCH routine. See IDL Help for more information on !ERROR_STATE and CATCH.

See Manage Errors for more information on error handling in ENVI programming.

Version History

ENVI 5.4

Introduced

See Also

ENVIConfusionMatrix