An Improved Unsupervised Band Selection of Hyperspectral Images Based on Sparse Representation

Abstract

Hyperspectral images have far more spectral bands than ordinary multispectral images. Rich band information provides more favorable conditions for the tremendous applications as well as many problems such as the curse of dimensionality. Band selection is an effective method to reduce the spectral dimension which is one of popular topics in hyperspectral remote sensing. Motivated by previous sparse representation method, 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. Five state-of-art band selection methods are compared with the MDSR on three widely used hyperspectral datasets. Experimental results show that MDSR achieves marginally better performance in hyperspectral image classification, and better performance in average correlation coefficient and computational time.