Hyperspectral Band Selection via Optimal Combination Strategy

Abstract

Band selection is one of the main methods of reducing the number of dimensions in a hyperspectral image. Recently, various methods have been proposed to address this issue. However, these methods usually obtain the band subset in the perspective of a locally optimal solution. To achieve an optimal solution with a global perspective, this paper developed a novel method for hyperspectral band selection via optimal combination strategy (OCS). The main contributions are as follows: (1) a subspace partitioning approach is proposed which can accurately obtain the partitioning points of the subspace. This ensures that similar bands can be divided into the same subspace; (2) two candidate representative bands with a large amount of information and high similarity are chosen from each subspace, which can fully represent all bands in the subspace; and (3) an optimal combination strategy is designed to acquire the optimal band subset, which achieves an optimal solution with a global perspective. The results on four public datasets illustrate that the proposed method achieves satisfactory performance against other methods.