Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection
Sun, He and Ren, Jinchang and Zhao, Huimin and Sun, Genyun and Liao, Wenzhi and Fang, Zhenyu and Zabalza, Jaime (2020) Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection. IEEE Transactions on Cybernetics. ISSN 2168-2275 (https://doi.org/10.1109/TCYB.2020.2977750)
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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.
ORCID iDs
Sun, He, Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194, Zhao, Huimin, Sun, Genyun, Liao, Wenzhi, Fang, Zhenyu and Zabalza, Jaime ORCID: https://orcid.org/0000-0002-0634-1725;-
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Item type: Article ID code: 71924 Dates: DateEvent24 March 2020Published24 March 2020Published Online25 February 2020AcceptedNotes: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 30 Mar 2020 09:42 Last modified: 22 Nov 2024 15:20 URI: https://strathprints.strath.ac.uk/id/eprint/71924