Hyperspectral Band Selection Using Pair-Wise Constraint and Band-Wise Correlation

Abstract

In this paper, a novel supervised band selection (BS) method based on pair-wise constraint and band-wise correlation information is proposed for the dimension reduction of hyperspectral images. On the one hand, the band-wise correlation information, is used for selecting band-subset with lower redundancy and higher representation. This process is achieved by first partitioning all spectral bands into continuous groups and then calculate a band-wise correlation matrix within each group, which is used later for selecting bands of more representation and lower redundancy. On the other hand, pair-wise supervised information (i.e., whether a pair of labeled samples are from the same class) is exploited for selecting band-subsets to better discriminate different classes. That is, a few bands are adaptively chosen for each pair of labeled samples according to spectral-similarity, to ensure that the distance between samples from different classes is far and keep sample-pair from same class close. By the joint use of both pair-wise constraint information and band-wise correlation information, the proposed BS method can lead to select optimal band-subsets with low-redundancy, high-representation and high-discrimination. Experimental results demonstrate the effectiveness of the proposed BS method.