Hyperspectral image band selection method combining the variation degree of salient features

Abstract

ABSTRACTBand selection (BS) work for hyperspectral remote sensing images (HRSIs) has attracted increasing attention from researchers. In the HRSI, the salient features of each band always include the more important discriminative features. However, most existing BS methods always have difficulty in simultaneously performing local-global consistency analysis on salient features in bands of HRSIs. In addition, these methods also ignore the consideration of the diversity of band information to some extent when selecting a band subset. To tackle these problems, a novel unsupervised hyperspectral BS method combining the variation degree of salient features (UBSF) is proposed in this article. UBSF first promotes the singular value decomposition method to analyse the salient features in bands and develops a new low-dimensional local-global salient feature representation (NLR) method for discriminative feature representations of the bands. The NLR method combines the variation degree of the features of the significant representative patches of each band in their corresponding significant feature subspaces to achieve a local-global consistency analysis of salient features of each band of HRSIs. Next, UBSF proposes a new two-stage strategy (TSS) to preserve the diversity of BS results as much as possible. In TSS, we propose a novel similarity criterion, which can make the highly similar band subsets with near information amount in the overall bands adaptively compressed according to the obtained similarity results of bands further. Then, the band subset with the most information is selected as the final BS results in the obtained reduced-dimensional HRSI. Finally, the validity of the UBSF method is verified based on three measured HRSI datasets. The experimental results show that the proposed UBSF is superior to other state-of-the-art BS methods.