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

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.