Hyperspectral Band Selection via Structural Correlation and Information Measures

Abstract

Band selection is an effective dimensionality reduction technique for hyperspectral image (HSI). In recent years, many unsupervised band selection methods have been proposed, but most of them measure the similarity between bands only from the spectral dimension, ignoring their high spatial correlation. In addition, these methods provide a less than ideal quantitative description of spectral information and neglect the redundancy within the selected bands. To address these issues, we propose a hyperspectral band selection via structural correlation and information measures (SCIMs), claiming the following contributions: 1) through introducing the structural similarity (SSIM) index to assess the correlation between bands, both spectral and spatial information are considered; 2) the HSI cube is partitioned into several groups by considering both intragroup and intergroup similarity measured by SSIM; and 3) in order to obtain a high-quality band subset, a representative band is selected in each group from the point of view of information as well as redundancy. The experimental results on three HSI datasets show that the proposed method has significant advantages compared with competitors.