Skip to Main Content
In the framework of remote-sensing image classification support vector machines (SVMs) have recently been receiving a very strong attention, thanks to their accurate results in many applications and good analytical properties. However, SVM classifiers are intrinsically noncontextual, which represents a severe limitation in image classification. In this paper, a novel method is proposed to integrate support vector classification with Markov random field models for the spatial context, and is validated with multichannel SAR and multispectral high-resolution images. The integration relies on an analytical reformulation of the Markovian minimum-energy rule in terms of a suitable SVM-like kernel expansion. Parameter-optimization and hierarchical clustering algorithms are also integrated in the method to automatically tune its input parameters and to minimize the execution time with large images and training sets, respectively.