The classification algorithm is based on a model fit to the AIRSAR data which provides estimates of the contributions of surface, double-bounce, and volume scatter to the observed polarimetric radar signatures for each of the three wavelengths measured by the AIRSAR. After the model fit has been applied to the data, simple rules (established by training on different ground cover signatures in just three AIRSAR images) are used to classify the results into one of several different ground cover types. The algorithm used is capable of distinguishing between areas with no vegetation cover, areas with low- and medium-level vegetation cover, forested areas, and urban areas. The three vegetated classes of ground cover are further sub-divided into areas where double-bounce scatter is significant and areas where it is not.

The output from MAPVEG consists of a byte image with each class assigned to a different numerical value plus a header which provides information on the image dimensions, a key to the numerical values, and a listing of the percentage of the image assigned to each class. The image is typically much smaller than the original AIRSAR data (e.g. a 330K image from 39Mb of typical AIRSAR data) and can be easily displayed on a PC or Macintosh using standard image processing software tools.