Multispectral image context classification using stochastic relaxation

A multispectral image context classification which is based on a stochastic relaxation algorithm and Markov-Gibbs random field is presented. The implementation of the relaxation algorithm is related to a form of optimization programming using annealing. The authors discuss the motivation for a Bayesian context-decision rule, and then use a Markov-Gibbs model to develop a contextual classification algorithm in which maximizing the posterior probability is based on stochastic relaxation. Experimental results that are based on simulated and real multispectral remote sensing images are presented to show how classification accuracy is greatly improved. The algorithm is highly parallel and exploits the equivalence between Gibbs distributions and Markov random fields