Fulin Luo
Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image.
Luo, Fulin; Guo, Tan; Lin, Zhiping; Ren, Jinchang; Zhou, Xiaocheng
Authors
Tan Guo
Zhiping Lin
Professor Jinchang Ren j.ren@rgu.ac.uk
Professor of Computing Science
Xiaocheng Zhou
Abstract
Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semisupervised learning methods fail to consider multiple properties of an HSI, which has restricted the discriminant performance of feature representation. In this article, we introduce the hypergraph into semisupervised learning to reveal the complex multistructures of an HSI, and construct a semisupervised discriminant hypergraph learning (SSDHL) method by designing an intraclass hypergraph and an interclass graph with the labeled samples. SSDHL constructs an unsupervised hypergraph with the unlabeled samples. In addition, a total scatter matrix is used to measure the distribution of the labeled and unlabeled samples. Then, a low-dimensional projection function is constructed to compact the properties of the intraclass hypergraph and the unsupervised hypergraph, and simultaneously separate the characteristics of the interclass graph and the total scatter matrix. Finally, according to the objective function, we can obtain the projection matrix and the low-dimensional features. Experiments on three HSI data sets (Botswana, KSC, and PaviaU) show that the proposed method can achieve better classification results compared with a few state-of-the-art methods. The result indicates that SSDHL can simultaneously utilize the labeled and unlabeled samples to represent the homogeneous properties and restrain the heterogeneous characteristics of an HSI.
Citation
LUO, F., GUO, T., LIN, Z., REN, J. and ZHOU, X. 2020. Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image. IEEE journal of selected topics in applied earth observations and remote sensing [online], 13, pages 4242-4256. Available from: https://doi.org/10.1109/jstars.2020.3011431
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 20, 2020 |
Online Publication Date | Jul 23, 2020 |
Publication Date | Dec 31, 2020 |
Deposit Date | May 6, 2022 |
Publicly Available Date | Mar 28, 2024 |
Journal | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Print ISSN | 1939-1404 |
Electronic ISSN | 2151-1535 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Volume | 13 |
Pages | 4242-4256 |
DOI | https://doi.org/10.1109/JSTARS.2020.3011431 |
Keywords | Dimensionality reduction (DR); graph learning; hyperspectral image (HSI) classification; Locality-constrained linear coding; Neighborhood margin |
Public URL | https://rgu-repository.worktribe.com/output/1085389 |
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LOU 2020 Semisupervised hypergraph (VOR)
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 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.
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