Classification of multisource remote sensing imagery using a genetic algorithm and Markov random fields

The use of contextual information for modeling the prior probability mass function has found applications in the classification of remotely sensed data. With the increasing availability of multisource remotely sensed data sets, random field models, especially Markov random fields (MRF), have been found to provide a theoretically robust yet mathematical tractable way of coding multisource information and of modeling contextual behavior. It is well known that the performance of a model is dependent both on its functional form (in this case, the classification algorithm) and on the accuracy of the estimates of model parameters. In dealing with multisource data, the determination of source weighting and MRF model parameters is a difficult issue. The authors extend the methodology proposed by A. H. Schistad et al. (1996), by demonstrating that the use of an effective search procedure, the genetic algorithm, leads to improved parameter estimation and hence higher classification accuracies.