Spectral and Wavelet-based Feature Selection with Particle Swarm Optimization for Hyperspectral Classification

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 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. Second, for making use of wavelet signal feature of pixels of hyperspectral image,we investigate the performance of the selected wavelet features based on wavelet approximate coefficients at the third level．The PSO algorithm is performed to optimize spectral feature and wavelet-based approximate coefficients to select the best discriminant features for hyperspectral remote imagery．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 PSO-SVM classification system.