Hyperspectral Band Selection via Difference Between Intergroups

Various methods are proposed to reduce the dimensions of hyperspectral image by band selection in recent years. Most methods select one band from each group to construct a band subset. However, the redundancy in the selected bands from different groups is neglected. Furthermore, the researchers do not pay enough attention to how many bands are appropriated for selection. To solve these issues, we propose a hyperspectral band selection method via difference between inter-groups (DIG), which includes grouping strategy and ranking strategy. Specifically, the grouping strategy adopts intra-group similarity to reasonably distribute all partitioning point positions. The similarity of bands within the same group is significantly improved. For the ranking strategy, it not only takes into account the knowledge and intra-group similarity of bands, but also evaluates the differences between each band and other inter-group bands. The redundancy in band subset is reduced sufficiently. In order to accurately obtain the optimal number of bands, an evaluation function is designed to measure the information content and redundancy in various band subsets. Experimental results from different aspects show that the proposed model has a large performance advantage on three public datasets.