A new 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 motivate a Bayesian context decision rule, and a Markov-Gibbs model for the original Landsat MSS (multispectral scanner) image is introduced, and then develop a new contextual classification algorithm, in which maximizing the posterior probability (MAP) is based on stochastic relaxation, an annealing optimization method. Finally, experimental results that are based on simulated and real multispectral remote sensing images to Fhow how classification accuracy is greatly improved are presented. The algorithm is highly parallel and exploits the equivalence between Gibbs distribution\ and Markov random fields (MRF).
In this paper, we develop set of multispectral image context classification techniques which are based on a recursive algorithm for optimal estimation of the state of a two-dimensional discrete Markov Random Field.The three recursive algorithms are forms of dynamic programming. Because the estimation equations of the recursive algorithm are quite simple, the computation complexity of the approach is low. It is shown that recursive contextual classifications can improve classification performance, as compared to noncontextual classification. In addition, this algorithm has the advantage over other techniques in that it handles multispectral data naturally and simultaneously.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.