Parallelepiped classification uses a simple decision rule to classify multispectral data. The decision boundaries form an n-dimensional parallelepiped classification in the image data space. The dimensions of the parallelepiped classification are defined based upon a standard deviation threshold from the mean of each selected class. If a pixel value lies above the low threshold and below the high threshold for all n bands being classified, it is assigned to that class. If the pixel value falls in multiple classes, ENVI assigns the pixel to the first class matched. Areas that do not fall within any of the parallelepiped classes are designated as unclassified.

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

You can also write a script to perform Parallelepiped classification using the ParallelepipedClassification task.

1. From the Toolbox, select Classification > Supervised Classification > Parallelepiped Classification. The Parallelepiped 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 Parallelepiped calculation.
4. Enter a filename and location for the Output Raster.
5. 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.
6. Optional: Enter a filename and location for the Output Rule Raster.
7. 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.
8. 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.

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

10. 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.

11. 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.

12. Click OK.

The pixel values of the resulting rule images range from 0 to n (where n is the number of bands) and represent the number of bands that satisfied the parallelepiped criteria. There is one rule image for each selected class. Areas that match all bands for a particular class are carried over as classified areas into the classified image. If more than one match occurs, the first class to evaluate (the first ROI from the selected list) carries over into the classified image.