SAR Imagery Classification using Multi-class Support Vector Machines

Abstract

In this paper, we present the application to SAR imagery classification of a novel pattern recognition technique named Multi-class Support Vector Machines (M-SVMs). M-SVMs are a n-ary extension of Support Vector Machines (SVM), introduced by Vapnik within the framework of the Statistical Learning Theory. In this article we use the M-SVMs in order to classify an ERS-1 SAR multi-frequency survey of Torre de Hercules coast, Spain (December 13, 1992). The main objective of this work is to evaluate the classification performances of M-SVMs in comparison with the most frequently employed Neural Networks and Fuzzy classifiers. M-SVMs provided interesting results with respect to Neural Networks and Fuzzy classifiers, having a reliability factor around to 94%.