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Journal of Applied Remote Sensing

Band selection for hyperspectral image classification with spatial–spectral regularized sparse graph
Author(s): Puhua Chen; Licheng Jiao
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Paper Abstract

Sparsity preserving projection is a well-known dimensionality reduction method that preserves the sparse representation relationship among data in low-dimensional space, which is beneficial for classification. The idea of sparsity preserving is applied to band selection for hyperspectral classification. Considering the spatial distribution characteristic of hyperspectral image (HSI), a spatial–spectral regularized sparse graph (ssRSG), which could utilize the spatial–spectral information in HSI to promote the discriminability of extracted local structure, is proposed. For band selection, the L2,1 norm is applied to restrain the projection matrix and make a few bands with high importance scores, which are computed by the contribution of bands in a projection matrix. According to the importance score, more important bands are selected. Two real hyperspectral images are used to validate the performance of the proposed method.

Paper Details

Date Published: 10 February 2017
PDF: 8 pages
J. Appl. Remote Sens. 11(1) 010501 doi: 10.1117/1.JRS.11.010501
Published in: Journal of Applied Remote Sensing Volume 11, Issue 1
Show Author Affiliations
Puhua Chen, Xidian Univ. (China)
Licheng Jiao, Xidian Univ. (China)


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