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Contextual remote-sensing image classification by support vector machines and Markov random fields

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2 Author(s)
Moser, G. ; Dept. of Biophys. & Electron. Eng. (DIBE), Univ. of Genoa, Opera, Italy ; Serpico, S.B.

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.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International

Date of Conference:

25-30 July 2010

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