Particle Swarms for Feature Extraction of Hyperspectral Data

• MONTEIRO Sildomar Takahashi

  Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

• KOSUGI Yukio

  Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

抄録

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.

収録刊行物

• IEICE transactions on information and systems

  IEICE transactions on information and systems 90 (7), 1038-1046, 2007-07-01

  一般社団法人電子情報通信学会

キーワード

• feature extraction
• particle swarm optimization
• hyperspectral data
• neural networks
• principal components analysis

詳細情報

• CRID: 1572824502428271616
• NII論文ID: 110007538624
• NII書誌ID: AA10826272
• ISSN: 09168532
• 本文言語コード: en
• データソース種別:
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