A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification

A new dissimilarity-weighted sparse self-representation (DWSSR) method has been presented to select a proper band subset for hyperspectral imagery (HSI) classification. The DWSSR assumes that all the bands can be represented by the selected band subset, and it formulates sparse representation of all the bands into a sparse self-representation (SSR) model with row-sparsity constraint in the coefficient matrix. Furthermore, the DWSSR integrates a dissimilarity-weighted regularization term with the SSR model to avoid the issue of too-close bands encountered in the SSR. The regularization term explains the encoding cost of all bands with the representative bands, and a new composite dissimilarity measure which combines spectral information divergence with intraband correlation is implemented to estimate the encoding weight. The DWSSR program is solved by the alternating direction method of multipliers (ADMM) framework, and the representative bands are finally selected according to the norm rankings of nonzero rows in the estimated coefficient matrix. Five groups of experiments on three popular HSI datasets are designed to test the performance of DWSSR in band selection, and five state-of-the-art methods are utilized to make comparisons. The results show that the DWSSR performs almost best among all the six methods, either in computational time or classification accuracies.