Contextual remote-sensing image classification through support vector machines, Markov random fields and graph cuts

The problem of remote-sensing image classification is addressed in this paper by proposing a novel contextual classification method that integrates support vector machines (SVMs), Markov random fields (MRFs), and graph cuts. The proposed approach is methodologically explained by the aim to combine the robustness to dimensionality issues and the generalization capability of SVMs, the effectiveness of Markov models in characterizing the spatial contextual information associated with an image, and the capability of graph cut techniques in tackling complex problems of global minimization in computationally acceptable times. In the proposed method, the MRF minimum-energy problem is formalized in terms of an appropriate SVM kernel expansion and addressed through graph cuts. Parameter estimation is automated through two specific algorithms, based on the Ho-Kashyap and Powell numerical procedures. Experiments are carried out with two data sets consisting of multichannel SAR and multispectral high-resolution images.