Satellite image classification using a modified Metropolis dynamics

Abstract

A pseudo-stochastic variation of the Metropolis dynamics for combinatorial optimization in image classification using Markov random fields is presented. At high temperature, the behavior of the algorithm is similar to the stochastic ones. However, if the temperature is less than a certain threshold, it becomes deterministic. The length of the pseudo-stochastic phase is controlled by a constant threshold used in the modified dynamics. The algorithm yields an approximate but usually good solution to the optimization problem. The algorithm runs on a connection machine. It is applied to the standard pixel classification problem; objective and subjective comparisons with other algorithms have been made.