Dynamic programming approach for context classification using the Markov random field

A set of multispectral image context classification techniques are discussed 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 classification 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