EFFICIENT MAXIMUM-LIKELIHOOD CLASSIFICATION FOR IMAGING SPECTROMETER DATA SETS

A simplified maximum likelihood classification technique for handling remotely sensed image data is proposed which reduces, significantly, the processing time associated with traditional maximum likelihood classification when applied to imaging spectrometer data, and copes with the training of geographically small classes. Several wavelength subgroups are formed from the complete set of spectral bands in the data, based on properties of the global correlation among the bands. Discriminant values are computed for each subgroup separately and the sum of discriminants is used for pixel labeling. Several subgrouping methods are investigated and the results show that a compromise among classification accuracy, processing time, and available training pixels can be achieved by using appropriate subgroup sizes.