Classification of Hyperspectral Remote Sensing Images with Support Vector Machines and Particle Swarm Optimization

Spectral band selection is a fundamental problem in hyperspectral classification. This paper addresses the problem of band selection for hyperspectral remote sensing image and SVM parameter optimization. First, we present a thorough experimental study to show the superiority of the generalization capability of the support vector machine (SVM) approach in the hyperspectral classification of remote sensing image. Second, we propose an evolutionary classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments are conducted on the basis of AVIRIS 92AV3C dataset. The obtained results clearly confirm the superiority of the SVM approach as compared to traditional classifiers, and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSOSVM classification system.