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Particle Swarms for Feature Extraction of Hyperspectral Data Sildomar Takahashi MONTEIRO Yukio KOSUGI Publication IEICE TRANSACTIONS on Information and Systems Vol.E90-D No.7 pp.1038-1046 Publication Date: 2007/07/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e90-d.7.1038 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Pattern Recognition Keyword: feature extraction, particle swarm optimization, hyperspectral data, neural networks, principal components analysis, Full Text: PDF(1.3MB)>>
Summary: This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Particle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extraction. A formulation utilizing two swarms of particles was developed to optimize simultaneously a desired performance criterion and the number of selected features. Candidate feature sets were evaluated on a regression problem. Artificial neural networks were trained to construct linear and nonlinear models of chemical concentration of glucose in soybean crops. Experimental 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. | ||||||||||
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