ISODATA unsupervised classification calculates class means evenly distributed in the data space then iteratively clusters the remaining pixels using minimum distance techniques. Each iteration recalculates means and reclassifies pixels with respect to the new means. Iterative class splitting, merging, and deleting is done based on input threshold parameters. All pixels are classified to the nearest class unless a standard deviation or distance threshold is specified, in which case some pixels may be unclassified if they do not meet the selected criteria. This process continues until the number of pixels in each class changes by less than the selected pixel change threshold or the maximum number of iterations is reached.

You can also write a script to perform ISODATA classification using the ISODATAClassification task.

Reference: Tou, J. T., and R. C. Gonzalez. Pattern Recognition Principles, Addison-Wesley Publishing Company, Reading, Massachusetts (1974).

  1. From the Toolbox, select Classification > Unsupervised Classification > IsoData Classification. ISODATA Classification dialog appears.
  2. Select an Input Raster and perform optional spatial and spectral subsetting.
  3. Optional: Enter the Number Of Classes to define. The default value is 5.
  4. Optional: Enter a Change Threshold Percent, which will determine when to complete the classification. When the percentage of pixels that change classes during an iteration is less than the threshold value, the classification completes. The default value is 2 percent.
  5. Optional: Enter the maximum number of Iterations. If the change threshold percent is not met before the maximum number of iterations is reached, the classification completes. The default value is 10.
  6. Specify a filename and location for the Output Raster.
  7. Enable the Preview check box to preview the settings before processing 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. To preview a different area in your image, pan and zoom to the area of interest and re-enable the Preview option. Depending on the algorithm being used by the tool, the preview result might be different from the final result of processing on the full extent, full resolution of the input image in the following scenarios: 1) If you zoomed out of the input raster in the view by 50%, or a percentage less than 50%, ENVI uses a downsampled image at the closest resolution level to calculate the preview, or 2) If the entire image is not visible in the view, ENVI uses the subset in the viewable area of the input image to calculate the preview.

  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. To reuse these task settings in future ENVI sessions, save them to a file. Click the down arrow next to the OK button , select Save Parameter Values, then specify the path 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 saved your settings.

  10. You can run the process in the background on your computer or in the background on an ENVI Server (if available). Click the down arrow next to the OK button and select one of the following:

    • Run Task in the Background: Runs the process on your computer. This method does not use an ENVI Server. The job progress will show in the ENVI Job Console's Background Jobs tab.
    • Run Task on envi_server_name. Runs the process on the selected ENVI Server. The job progress will show in the ENVI Job Console's envi_server_name tab.

    See ENVI Job Console for details on monitoring ENVI Server jobs and viewing results. See ENVI Servers for details on configuring and using an ENVI Server.

  11. Click OK.