PAPER Particle Swarms for Feature Extraction of Hyperspectral Data

SUMMARY This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Parti-cle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extrac-tion. A formulation utilizing two swarms of particles was developed to opti-mize simultaneously a desired performance criterion and the number of se-lected features. Candidate feature sets were evaluated on a regression prob-lem. Artificial neural networks were trained to construct linear and nonlin-ear models of chemical concentration of glucose in soybean crops. Ex-perimental results utilizing real-world hyperspectral datasets demonstrate the viability of the method. The particle swarms-based approach presented superior performance in comparison with conventional feature extraction methods, on both linear and nonlinear models. key words: feature extraction, particle swarm optimization, hyperspectral data, neural networks, principal components analysis 1