A Particle Swarm Optimization-based Approach for Hyperspectral Band Selection

In this paper, a feature selection algorithm based on particle swarm optimization for processing remotely acquired hyperspectral data is presented. Since particle swarm optimization was originally developed to search only continuous spaces, it could not deal with the problem of spectral band selection directly. We propose a method utilizing two swarms of particles in order to optimize simultaneously a desired performance criterion and the number of selected features. The candidate feature sets were evaluated on a regression problem using artificial neural networks to construct nonlinear models of chemical concentration of glucose in soybean crops. Experimental results attesting the viability of the method utilizing realworld hyperspectral data are presented. The particle swarm optimization-based approach presented superior performance in comparison with a conventional feature extraction method.