Variable precision rough set based unsupervised band selection technique for hyperspectral image classification

Unsupervised band selection is still a relevant research topic for mitigating certain challenges of hyperspectral image classification. In this paper, a greedy unsupervised hyperspectral band selection technique is proposed based on variable precision rough set (VPRS). The proposed technique defined a novel dependency measure by exploiting VPRS. Furthermore, the dependency measure is defined in such a way that it became less sensitive to the degree of misclassification parameter beta in VPRS. Our technique first computed the similarity between every pair of bands using the proposed dependency measure and selected a band from the pair that produced maximum similarity value. After that a novel criterion is proposed to select the informative bands one-by-one by adopting first order incremental search. The effectiveness of the proposed band selection technique is assessed by comparing it with five state-of-the-art techniques using three hyperspectral data sets. (c) 2019 Elsevier B.V. All rights reserved.