10 February 2017 Band selection for hyperspectral image classification with spatial–spectral regularized sparse graph
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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 L 2,1 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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Puhua Chen, Puhua Chen, Licheng Jiao, 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 . Submission: Received: 14 June 2016; Accepted: 24 January 2017
Received: 14 June 2016; Accepted: 24 January 2017; Published: 10 February 2017
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