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Proceedings Paper

Optimal band selection for high dimensional remote sensing data using genetic algorithm
Author(s): Xianfeng Zhang; Quan Sun; Jonathan Li
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Paper Abstract

A 'fused' method may not be suitable for reducing the dimensionality of data and a band/feature selection method needs to be used for selecting an optimal subset of original data bands. This study examined the efficiency of GA in band selection for remote sensing classification. A GA-based algorithm for band selection was designed deliberately in which a Bhattacharyya distance index that indicates separability between classes of interest is used as fitness function. A binary string chromosome is designed in which each gene location has a value of 1 representing a feature being included or 0 representing a band being not included. The algorithm was implemented in MATLAB programming environment, and a band selection task for lithologic classification in the Chocolate Mountain area (California) was used to test the proposed algorithm. The proposed feature selection algorithm can be useful in multi-source remote sensing data preprocessing, especially in hyperspectral dimensionality reduction.

Paper Details

Date Published: 9 October 2009
PDF: 7 pages
Proc. SPIE 7471, Second International Conference on Earth Observation for Global Changes, 74711R (9 October 2009); doi: 10.1117/12.847907
Show Author Affiliations
Xianfeng Zhang, Peking Univ. (China)
Quan Sun, Peking Univ. (China)
Jonathan Li, Univ. of Waterloo (Canada)


Published in SPIE Proceedings Vol. 7471:
Second International Conference on Earth Observation for Global Changes
Xianfeng Zhang; Jonathan Li; Guoxiang Liu; Xiaojun Yang, Editor(s)

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