Band selection of hyperspectral image based on improved genetic algorithm

doi:10.7523/j.issn.2095-6134.2009.6.010

Abstract

Abstract:

Hyperspectral images have been widely used in earth observation. However, there are some problems such as huge amount of data and high correlation between bands. The existing band selection algorithms have been analyzed in this article regarding to the problems mentioned above. An application of genetic algorithm based on image information content and between-class separability criteria was proposed to band selection of hyperspectral image. The correlation coefficient matrix was formed for subspace partition. The fitness functions of genetic algorithm were reconstructed by using joint entropy as criterion of information content and Bhattacharya distance as between-class separability. The selection operator in genetic algorithm was improved. Finally, the improved algorithm was tested with AVIRIS image data. The maximum likelihood classification method was implemented to classify the selected optimal band combinations. The classification results illustrate that the total classification accuracy percentage of the chosen band image is 94.24% and Kappa coefficient 0.94.

Key words: hyperspectral image, band selection, genetic algorithm, joint entropy, Bhattacharyya distance

CLC Number:

TP751.1

ZHAO Dong, ZHAO Guang-Heng. Band selection of hyperspectral image based on improved genetic algorithm[J]. , 2009, 26(6): 795-802.

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