Supervised segmentation of remote-sensing multitemporal images based on the tree-structured Markov random field model
We deal with the supervised segmentation of multi-temporal remote-sensing images following a statistical Bayesian approach. To take into account prior information on the class of images, like the correlation between neighboring pixels, as well as the available knowledge about the structure of the current image, we model the image as a tree-structured Markov random field. The data collected at two different dates are jointly processed as a single multi-component image, with the classes defined a priori based on ground truth information and grouped in changed and unchanged macro-classes. Experimental results in terms of classification accuracy prove the effectiveness of the proposed technique with respect to non-contextual methods, as well as to a disjoint approach. In addition, the classification tree allows for a direct interpretation of the result