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Journal of Applied Remote Sensing

Band selection algorithm based on information entropy for hyperspectral image classification
Author(s): Li Xie; Guangyao Li; Lei Peng; Qiaochuan Chen; Yunlan Tan; Mang Xiao
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Paper Abstract

A band selection algorithm based on information entropy is proposed for hyperspectral image classification. First, original spectral features are transformed into discrete features and represented by a discrete space model. Then, the band selection algorithm based on information entropy is adopted to reduce feature dimensionality. The bands with weak class separability are effectively abandoned by the band selection algorithm. Moreover, support vector machine classifiers with composite kernels are employed to incorporate spatial features into spectral features, reducing speckle errors in the classification maps. The proposed methods are applied to three benchmark hyperspectral data sets for classification. The performance of the proposed methods is compared with a band selection algorithm based on mutual information. The experimental results demonstrate that the band selection algorithm based on information entropy can effectively reduce feature dimensionality and improve classification accuracy.

Paper Details

Date Published: 22 May 2017
PDF: 17 pages
J. Appl. Rem. Sens. 11(2) 026018 doi: 10.1117/1.JRS.11.026018
Published in: Journal of Applied Remote Sensing Volume 11, Issue 2
Show Author Affiliations
Li Xie, Tongji Univ. (China)
Guangyao Li, Tongji Univ. (China)
Lei Peng, Tongji Univ. (China)
Qiaochuan Chen, Tongji Univ. (China)
Yunlan Tan, National Administration of Surveying, Mapping and Geoinformation of China (China)
Jinggangshan Univ. (China)
Mang Xiao, Shanghai Institute of Technology (China)


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