Optimal band selection for high dimensional remote sensing data using genetic algorithm

Xianfeng Zhang; Quan Sun; Jonathan Li

doi:10.1117/12.847907

9 October 2009 Optimal band selection for high dimensional remote sensing data using genetic algorithm

Xianfeng Zhang, Quan Sun, Jonathan Li

Proceedings Volume 7471, Second International Conference on Earth Observation for Global Changes; 74711R (2009) https://doi.org/10.1117/12.847907
Event: Second International Conference on Earth Observation for Global Changes, 2009, Chengdu, China

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.

Citation Download Citation

Xianfeng Zhang, Quan Sun, and Jonathan Li "Optimal band selection for high dimensional remote sensing data using genetic algorithm", Proc. SPIE 7471, Second International Conference on Earth Observation for Global Changes, 74711R (9 October 2009); https://doi.org/10.1117/12.847907

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