Spectral texture pattern matching: a classifier for digital imagery

Because of the difficulty of specifying general criteria for texture features, automated image analysis in the field of remote sensing has been largely restricted to the spectral domain. An algorithm that integrates spectral and textural information in the classification process is presented. The procedure is capable of classifying a region of arbitrary shape and size and operates effectively near class boundaries. Except for the requirement of user-defined training data, the algorithm can be completely automated. For all accuracy measures tested, the classification accuracy of the spectral texture pattern matching algorithm was higher for most classes than that of the maximum-likelihood classifier. Furthermore, errors with the spectral/textural algorithm are largely confined to omission, which gives a high degree of confidence to the classified pixels