Band Selection Using Improved Sparse Subspace Clustering for Hyperspectral Imagery Classification

Abstract

An improved sparse subspace clustering (ISSC) method is proposed to select an appropriate band subset for hyperspectral imagery (HSI) classification. The ISSC assumes that band vectors are sampled from a union of low-dimensional orthogonal subspaces and each band can be sparsely represented as a linear or affine combination of other bands within its subspace. First, the ISSC represents band vectors with sparse coefficient vectors by solving the L2-norm optimization problem using the least square regression (LSR) algorithm. The sparse and block diagonal structure of the coefficient matrix from LSR leads to correct segmentation of band vectors. Second, the angular similarity measurement is presented and utilized to construct the similarity matrix. Third, the distribution compactness (DC) plot algorithm is used to estimate an appropriate size of the band subset. Finally, spectral clustering is implemented to segment the similarity matrix and the desired ISSC band subset is found. Four groups of experiments on three widely used HSI datasets are performed to test the performance of ISSC for selecting bands in classification. In addition, the following six state-of-the-art band selection methods are used to make comparisons: linear constrained minimum variance-based band correlation constraint (LCMV-BCC), affinity propagation (AP), spectral information divergence (SID), maximum-variance principal component analysis (MVPCA), sparse representation-based band selection (SpaBS), and sparse nonnegative matrix factorization (SNMF). Experimental results show that the ISSC has the second shortest computational time and also outperforms the other six methods in classification accuracy when using an appropriate band number obtained by the DC plot algorithm.