Unsupervised Band Selection of Hyperspectral Images via Multi-Dictionary Sparse Representation

Band selection is a direct and effective method to reduce the spectral dimension, which is one of popular topics in hyperspectral remote sensing. Recently, a number of methods were proposed to deal with the band selection problem. Motivated by the previous sparse representation methods, we present a novel framework for band selection based on multi-dictionary sparse representation (MDSR). By obtaining the sparse solutions for each band vector and the corresponding dictionary, the contribution of each band to the raw image is derived. In terms of contribution, the appropriate band subset is selected. Although the number of dictionaries is increasing, the efficiency of the algorithm is much higher than the previous due to the reduction of the dictionary self-learning process. Five state-of-the-art band selection methods are compared with the MDSR on three widely used hyperspectral datasets (Salinas-A, Pavia-U, and Indian Pines). Experimental results show that the MDSR achieves marginally better performance in hyperspectral image classification and better performance in average correlation coefficient and computational time.