Band Selection for Specific Target Detection of Hyperspectral Imagery

Abstract

Band selection (BS) is considered as an effective method for dimensionality reduction of hyperspectral data. As an important application for hyperspectral remote sensing, target detection is widely concerned. Therefore, how to select more representational band subset for specific target to improve performance of detection is worth discussing. This letter proposed a BS method for specific target detection, called constrained target band selection under adaptive subspace partitioning (CTASPBS). Firstly, all bands are partitioned into multiple weakly correlated subsets via adaptive subspace partition strategy (ASPS). Then, according to a target-constrained band prioritization (BP) criterion, the band with the highest priority in each subset is selected to form the optimal band subset. Due to the application of different BP criterion, two BS methods ASPS_MinV and ASPS_MaxV are proposed. Finally, experimental results on real hyperspectral data show that CTASPBS is an effective BS method for specific target detection.