Dual Homogeneous Patches-Based Band Selection Methodology for Hyperspectral Classification

Abstract

Homogeneous band- or pixel-based feature selection, which exploits the difference between spectral or spatial regions to select informative and low-redundant bands, has been extensively studied in classifying hyperspectral images (HSIs). Although many models have proven effective, they rarely simultaneously exploit homogeneous spatial and spectral information, which are beneficial to extract potential low-dimensional characteristics even under noise. Moreover, the employed vectorial transformation and unordered assumption destroy the implicit knowledge of HSIs. To solve these issues, a dual homogeneous pixel patches-based methodology termed PHSIMR was created for selecting the most representative, low-redundant, and informative bands, integrating hybrid superpixelwise adjacent band grouping and regional informative mutuality ranking algorithms. Specifically, the adjoining band grouping technique is designed to group adjacent bands into connected clusters with a small homogeneous pixel patch containing several homolabeled adjacent spatial points. Hence, the processing is efficient, and the superpixelwise adjoining band grouping can perceptually and quickly acquire connected band groups. Furthermore, the constructed graph and affiliated group avoid vectorial transformation and unordered assumption, protecting spectral and spatial contextual information. Then, the regional informative mutuality ranking algorithm is employed on another larger pixel patch within each homogeneous band group, acquiring the final representative, low-redundant, and informative band subset. Since the employed dual patches consist of homolabeled spatial pixels, PHSIMR is a supervised methodology. Comparative experiments on three benchmark HSIs were performed to demonstrate the efficiency and effectiveness of the proposed PHSIMR.