A spatial-temporal contextual classification approach based on Markov random fields using multi-temporal imagery

Abstract

The traditional classification method based on the spectral information in pixel level faces the problem of spectrum confusion. Pixels on multi-temporal image show dependencies in both the spatial and temporal domains besides spectral information. When spectral information has limited discriminative power, spatial-temporal dependencies can help to remove the spectral confusion. Two ETM+ images in different seasons after processing are used and supervised classification algorithm-the maximum likelihood classification (MLC) is used to initialize the algorithm proposed in this article. Then a Markov Random Fields (MRF) model is used to model the spatial-temporal contextual prior probabilities of images. Lastly the likelihood estimates of spectral observation from MLC and conditional spatial-temporal priors from MRF are integrated into posterior estimates by Bayes rule, the optimal classification was achieved when the classification corresponds to maximum a posteriori (MAP). The results show that MRF is an efficient probabilistic model for analysis of spatial and temporal contextual information. A spatial-temporal classification algorithm that explicitly integrates spectral, spatial and temporal information in multi-temporal images can achieve significant improvements over non-contextual classification. Some errors have been avoided because of the integration of space and time information.