Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection.

Sun, He; Ren, Jinchang; Zhao, Huimin; Sun, Genyun; Liao, Wenzhi; Fang, Zhenyu; Zabalza, Jaime

Abstract

Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still lacking. The lack of proper understanding of the HSI data structure has resulted in the inconsistency in the outcome of UBS. Besides, most of the UBS methods are either relying on complicated measurements or rather noise sensitive, which hinder the efficiency of the determined band subset. In this article, an adaptive distance-based band hierarchy (ADBH) clustering framework is proposed for UBS in HSI, which can help to avoid the noisy bands while reflecting the hierarchical data structure of HSI. With a tree hierarchy-based framework, we can acquire any number of band subset. By introducing a novel adaptive distance into the hierarchy, the similarity between bands and band groups can be computed straightforward while reducing the effect of noisy bands. Experiments on four datasets acquired from two HSI systems have fully validated the superiority of the proposed framework.

Citation

SUN, H., REN, J., ZHAO, H., SUN, G., LIAO, W., FANG, Z. and ZABALZA, J. 2022. Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection. IEEE transactions on cybernetics [online], 52(1), pages 215-227. Available from: https://doi.org/10.1109/TCYB.2020.2977750

• Journal Article Type: Article
• Acceptance Date: Mar 24, 2020
• Online Publication Date: Mar 24, 2020
• Publication Date: Jan 31, 2022
• Deposit Date: Mar 31, 2022
• Publicly Available Date: Mar 31, 2022
• Journal: IEEE Transactions on Cybernetics
• Print ISSN: 2168-2267
• Electronic ISSN: 2168-2275
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 52
• Issue: 1
• Pages: 215-227
• DOI: https://doi.org/10.1109/TCYB.2020.2977750
• Keywords: Hyperspectral band selection; Unsupervised learning; Hierarchy clustering; Adaptive distance
• Public URL: https://rgu-repository.worktribe.com/output/1085437