Support vector machines for classification of hyperspectral remote-sensing images

Abstract

In this paper, we address the problem of classification of hyperspectral remote-sensing images (in the original hyperdimensional feature space) by Support Vector Machines (SVMs). In particular, we investigate the effectiveness of SVMs in terms of classification accuracy, computational time and stability to parameter setting. Experiments, carried out on a standard AVIRIS hyperspectral data set, include a comparison with two other widely used nonparametric approaches, i.e., the K-nn and the Radial Basis Function (RBF) neural networks classifiers. The obtained results point out interesting properties of SVMs in hyperdimensional feature spaces and suggest them as a promising tool to classify hyperspectral remote-sensing images.