Comparison of two spectral-texture classification algorithms

Two classification algorithms that rely on both spectral and textural information are presented and compared. The first is a standard maximum-likelihood classification procedure with a texture "band" added to the spectral band set. The second is a pattern matching algorithm which integrates the spectral and spatial characteristics of the data in recognizing a user-specified training pattern. The pattern matching algorithm proved to be the most effective procedure of those compared. Classification results from both methods are compared with each other and with a purely spectral classification using a maximum-likelihood classifier.