A discriminatory groups-based supervised band selection technique for hyperspectral image classification

Abstract

ABSTRACT Dimensionality reduction is a prevalent preprocessing step when analysing hyperspectral images (HSIs). It uses band selection (BS) and feature extraction (FE) techniques, but the former can retain the physical meaning and interpretability of the HSI datasets. Hence, these techniques are preferred to select a proper subset of bands without transformation. This letter introduces a straightforward supervised BS technique using discriminatory groups (DGs) that does not require complex computations. In this technique, total DGs are achieved equal to the spectral dimension of the HSI dataset, and each holds bands similar to the number of classes. The constituent bands of the DGs may present more than once, but the total number of bands in the group remains unchanged. The obtained DGs are ranked using high- to low-discrimination fractions, which indicate the uniqueness of the respective DG. The highest value that a DG can attain represents the group’s ability to distinguish among all classes (the maximum number of classes). Hence, the proposed technique has been termed ‘maximum class discriminatory group (MCDG)’. The discrimination fraction works as the control parameter for the selection of bands. Subsequently, the unique bands are selected from the ranked DGs and used for HSI classification. The proposed MCDG technique has an articulate and quick implementation without requiring any initialization of bands and outperforms various state-of-the-art methods while experimenting using real HSI datasets.