Variable-number variable-band selection for feature characterization in hyperspectral signatures

This paper presents a novel band selection-based feature characterization technique for a hyperspectral signature, which is referred to as variable-number variable-band selection (VNVBS). Since a hyperspectral signature can be uniquely characterized by its spectral profile, its feature characterization can be achieved by selecting appropriate bands from the original set of spectral bands, and the number of bands to be selected is totally determined by its original spectral shape. As a result, two hyperspectral signatures may require different sets of bands for spectral feature characterization. Therefore, the proposed VNVBS allows one to select a different number of variable bands in accordance with the hyperspectral signature to be processed. In order for the VNVBS to select an appropriate subset of bands for a hyperspectral signature, a new band prioritization criterion (BPC), which is referred to as orthogonal subspace projector based BPC, is derived. It assigns a different priority score to each spectral band of a hyperspectral signature such that various features can be captured by the VNVBS. Accordingly, the VNVBS can be interpreted as a spectral feature extraction technique for hyperspectral signature characterization. Finally, experiments using two data sets are conducted to demonstrate that the VNVBS can improve the performance of the hyperspectral signature characterization.