Mahalanobis Distance Classification

Mahalanobis distance classification is a direction-sensitive distance classifier that uses statistics for each class. It is similar to Maximum Likelihood classification but assumes all class covariances are equal and therefore is a faster method. All pixels are classified to the closest ROI class unless you specify a distance threshold, in which case some pixels may be unclassified if they do not meet the threshold.

Reference: Richards, J.A. Remote Sensing Digital Image Analysis Berlin: Springer-Verlag (1999), 240 pp.

You can also write a script to perform Mahalanobis Distance classificationusing the MahalanobisDistanceClassification task.

  1. From the Toolbox, select Classification > Supervised Classification > Mahalanobis Distance Classification. The Mahalanobis Distance Classification dialog appears.
  2. Select an Input Raster and perform optional spatial and spectral subsetting, and/or masking.
  3. Select the Input ROIs file that represents the classes. Statistics from the ROIs are used as input to the Mahalanobis Distance calculation.
  4. In the Threshold Maximum Distance field, specify a pixel value between 0 and 10000000 that applies to all classes. The number of array elements must equal the number of classes. Mahalanobis Distance accounts for possible non-spherical probability distributions. This value represents the distance within which a class must fall from the center or mean of the distribution for a class. The smaller the distance threshold, the more pixels that are unclassified.
  5. Specify a filename and location for the Output Rule Raster. A rule raster is a greyscale image that shows intermediate classification results, where each band represents a rule raster for each class. With Mahalanobis Distance classification, pixel values represent the distance from the class mean.

  6. Enable the Display result check box to display the output in the view when processing is complete. Otherwise, if the check box is disabled, the result can be loaded from the Data Manager.

  7. Specify a filename and location for the Output Raster (the classification raster).

  8. Enable the Display result check box to display the output in the view when processing is complete. Otherwise, if the check box is disabled, the result can be loaded from the Data Manager.

  9. Enable the Preview check box to see a preview of the settings before you click OK to process the data. The preview is calculated only on the area in the view and uses the resolution level at which you are viewing the image. See Preview for details on the results. To preview a different area in your image, pan and zoom to the area of interest and re-enable the Preview option.

  10. To see a model-based version of this tool that shows how the tool is constructed from individual tasks, click Open in Modeler.

  11. To reuse these task settings in future ENVI sessions, save them to a file. Click the down arrow and select Save Parameter Values, then specify the location and filename to save to. Note that some parameter types, such as rasters, vectors, and ROIs, will not be saved with the file. To apply the saved task settings, click the down arrow and select Restore Parameter Values, then select the file where you previously stored your settings.

  12. To run the process in the background, click the down arrow and select Run Task in the Background. If an ENVI Server has been set up on the network, the Run Task on remote ENVI Server name is also available. The ENVI Server Job Console will show the progress of the job and will provide a link to display the result when processing is complete. See ENVI Servers for more information.

  13. Click OK.