Nonlocal-Similarity-Based Sparse Coding for Hyperspectral Imagery Classification

For hyperspectral imagery (HSI) classification, many works have shown the effectiveness of the spectral-spatial method. However, some previous works using neighboring information assumed that all neighboring pixels make an equal contribution to the central pixel, which is unreasonable for heterogeneous pixels, especially near the boundary of a region. In this letter, a nonlocal self-similarity based on the sparse coding method, followed by the use of a support vector machine classifier, is proposed to improve classification performance. Inspired by the success of nonlocal means, a new nonlocal weighted method is developed to determine the relationship between a test pixel and its neighboring ones. The nonlocal weights are determined by using the spectral angle mapper algorithm, which can exploit the spectral information of surface features. The experiments validate the superiority of our proposed method over existing approaches for HSI classification.