Pp. Raghu et B. Yegnanarayana, MULTISPECTRAL IMAGE CLASSIFICATION USING GABOR FILTERS AND STOCHASTICRELAXATION NEURAL-NETWORK, Neural networks, 10(3), 1997, pp. 561-572
In this article, we propose a supervised classification scheme for mul
tispectral image data based on the spectral as well as textural featur
es. A filter. bank consisting of Gabor wavelets is used to extract the
features from the multispectral imagery. The classification model con
sists of three three random processes, namely, feature formation, part
ition and label competition. The feature formation process models the
multispectral texture features from the Gabor filter bank as a multiva
riate Gaussian distribution. The partition process and the label compe
tition process represent a set of label constraints. These constraints
are represented on a Hopfield neural network model, and a stochastic
relaxation strategy is used to evolve a global minimum energy state of
the network, corresponding to the maximum a posteriori (MAP) probabil
ity. The performance of the scheme is demonstrated on a variety of mul
tispectral multipolar images obtained from SIR-C/X-SAR. (C) 1997 Elsev
ier Science Ltd.