Hyperspectral Band Selection via Adaptive Subspace Partition Strategy

Abstract

Band selection is considered as a direct and effective method to reduce redundancy, which is to select some informative and distinctive bands from the original hyperspectral image cube. Recently, many clustering-based band selection methods have been proposed, but most of them only take into account redundancy between bands, neglecting the amount of information in the subset of selected bands. Furthermore, these algorithms never consider the hyperspectral bands as ordered. Based on these two facts, we propose a novel approach for hyperspectral band selection via an adaptive subspace partition strategy (ASPS). The main contributions are as follows: 1) the ASPS is adopted to partition the hyperspectral image cube into multiple subcubes by maximizing the ratio of interclass distance to intraclass distance; 2) unlike previous methods, we estimate the band noise and select the band containing minimum noise (high-quality band) in each subcube to represent the whole subcube; and 3) adaptive subspace partition is viewed as a general framework and thus forms the variant version. Experimental results on three public datasets show that the proposed method achieves satisfactory results in both accuracy and efficiency than some state-of-the-art algorithms.