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Proceedings Paper

Method of selecting the best classification bands from hyperspectral images based on genetic algorithm and rough set
Author(s): Lixin Sun; Wen Gao
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Paper Abstract

Selecting the best classification bands from hyperspectral images for particular remote sensing application is one of the most important problems in utilizing hyperspectral images. In this paper, the best classification bands selection problem is regarded as optimal feature subset selection problem and the bands in original bands set are divided into redundant and irrelevant. In order to eliminate these two type bands, a multi-level optimal classification bands selection model from hyperspectral images based on genetic algorithm and rough set theory is proposed. Through the initial two steps of the multi-level model, the dimension reduction step and the genetic algorithm based filter step, most of redundant and irrelevant bands are deleted from the original images bands set. From the machine learning perspective, the multi-level model can take both advantages of the filter and wrapper models.

Paper Details

Date Published: 17 August 1998
PDF: 6 pages
Proc. SPIE 3502, Hyperspectral Remote Sensing and Application, (17 August 1998); doi: 10.1117/12.317782
Show Author Affiliations
Lixin Sun, Harbin Engineering Institute (China)
Wen Gao, Harbin Institute of Technology (China)


Published in SPIE Proceedings Vol. 3502:
Hyperspectral Remote Sensing and Application
Robert O. Green; Qingxi Tong, Editor(s)

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