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

摘要

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 β 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.