Spectral 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. We propose an evolutionary classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. The proposed PSO-SVM algorithm is performed to select the best discriminant features and appropriate SVM parameters for hyperspectral remote sensing imagery simultaneously.