Xp. Jia et Ja. Richards, EFFICIENT MAXIMUM-LIKELIHOOD CLASSIFICATION FOR IMAGING SPECTROMETER DATA SETS, IEEE transactions on geoscience and remote sensing, 32(2), 1994, pp. 274-281
A simplified maximum likelihood classification technique for handling
remotely sensed image data is proposed which reduces, significantly, t
he processing time associated with traditional maximum likelihood clas
sification when applied to imaging spectrometer data, and copes with t
he training of geographically small classes. Several wavelength subgro
ups are formed from the complete set of spectral bands in the data, ba
sed on properties of the global correlation among the bands. Discrimin
ant values are computed for each subgroup separately and the sum of di
scriminants is used for pixel labeling. Several subgrouping methods ar
e investigated and the results show that a compromise among classifica
tion accuracy, processing time, and available training pixels can be a
chieved by using appropriate subgroup sizes.