Band selection for hyperspectral image classification with spatial–spectral regularized sparse graph

Puhua Chen; Licheng Jiao

doi:10.1117/1.JRS.11.010501

10 February 2017 Band selection for hyperspectral image classification with spatial–spectral regularized sparse graph

Puhua Chen, Licheng Jiao

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 010501 (February 2017). https://doi.org/10.1117/1.JRS.11.010501

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.

Citation Download Citation

Puhua Chen and Licheng Jiao "Band selection for hyperspectral image classification with spatial–spectral regularized sparse graph," Journal of Applied Remote Sensing 11(1), 010501 (10 February 2017). https://doi.org/10.1117/1.JRS.11.010501

Received: 14 June 2016; Accepted: 24 January 2017; Published: 10 February 2017

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