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Novel hyperbolic clustering-based band hierarchy (HCBH) for effective unsupervised band selection of hyperspectral images.

Sun, He; Zhang, Lei; Ren, Jinchang; Huang, Hua

Authors

He Sun

Lei Zhang

Jinchang Ren

Hua Huang



Abstract

For dimensionality reduction of HSI, many clustering-based unsupervised band selection (UBS) methods have been proposed due to their superiority of reducing the high redundancy between selected bands. However, most of these methods fail to reflect the data structure of HSI, leading to inconsistent results of band selection. To tackle this particular issue, we have proposed a novel hyperbolic clustering-based band hierarchy (HCBH) to fully represent the underlying spectral structure and obtain a more consistent band selection. With the proposed adaptive hyperbolic clustering, the performance can be effectively improved with the aid of geometrical information. By introducing a cluster-centre based ranking metric, the desired band subset can be naturally obtained during the clustering process. Experimental results on three popularly used datasets have validated the superior performance of the proposed approach, which outperforms a few state-of-the-art (SOTA) UBS approaches.

Citation

SUN, H., ZHANG, L., REN, J. and HUANG, H. 2022. Novel hyperbolic clustering-based band hierarchy (HCBH) for effective unsupervised band selection of hyperspectral images. Pattern recognition [online], 130, article number 108788. Available from: https://doi.org/10.1016/j.patcog.2022.108788

Journal Article Type Article
Acceptance Date May 11, 2022
Online Publication Date May 13, 2022
Publication Date Oct 31, 2022
Deposit Date Jun 30, 2022
Publicly Available Date May 14, 2023
Journal Pattern Recognition
Print ISSN 0031-3203
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 130
Article Number 108788
DOI https://doi.org/10.1016/j.patcog.2022.108788
Keywords Hyperspectral images; Unsupervised band selection; Hyperbolic space clustering; Hierarchical clustering
Public URL https://rgu-repository.worktribe.com/output/1670295