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Hybrid-hypergraph regularized multiview subspace clustering for hyperspectral images

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Abstract
Clustering algorithms play an essential and unique role in classification tasks, especially when annotated data are unavailable or very scarce. Current clustering approaches in remote sensing are mostly designed for a single data source, such as hyperspectral image (HSI), while, nowadays, multisensor data are being routinely acquired. In this article, we propose a multiview subspace clustering model that exploits effectively the rich information from multiple features extracted either from a single data source (HSI) or from multiple sources that we call generically multiviews of the same scene. An important novelty of our approach is that it integrates local and nonlocal spatial information from each view in a unified framework. Our model learns a common intrinsic cluster structure from view-specific subspace representations by a new decomposition-based scheme. In addition, we develop innovative manifold-based spatial regularization as a hybrid hypergraph, which merges local and nonlocal spatial context and improves, thereby, the learning of view-specific structures. We develop an efficient algorithm to solve the resulting optimization problem. Extensive experiments on real data sets demonstrate the superior clustering performance over the state of the art.
Keywords
Electrical and Electronic Engineering, General Earth and Planetary Sciences, Sparse matrices, Feature extraction, Clustering algorithms, Image segmentation, Erbium, Hyperspectral imaging, Clustering methods, Hyperspectral images (HSIs), multiview clustering, remote sensing, subspace clustering

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MLA
Huang, Shaoguang, et al. “Hybrid-Hypergraph Regularized Multiview Subspace Clustering for Hyperspectral Images.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 60, 2022, doi:10.1109/tgrs.2021.3074184.
APA
Huang, S., Zhang, H., & Pizurica, A. (2022). Hybrid-hypergraph regularized multiview subspace clustering for hyperspectral images. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 60. https://doi.org/10.1109/tgrs.2021.3074184
Chicago author-date
Huang, Shaoguang, Hongyan Zhang, and Aleksandra Pizurica. 2022. “Hybrid-Hypergraph Regularized Multiview Subspace Clustering for Hyperspectral Images.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60. https://doi.org/10.1109/tgrs.2021.3074184.
Chicago author-date (all authors)
Huang, Shaoguang, Hongyan Zhang, and Aleksandra Pizurica. 2022. “Hybrid-Hypergraph Regularized Multiview Subspace Clustering for Hyperspectral Images.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60. doi:10.1109/tgrs.2021.3074184.
Vancouver
1.
Huang S, Zhang H, Pizurica A. Hybrid-hypergraph regularized multiview subspace clustering for hyperspectral images. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. 2022;60.
IEEE
[1]
S. Huang, H. Zhang, and A. Pizurica, “Hybrid-hypergraph regularized multiview subspace clustering for hyperspectral images,” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 60, 2022.
@article{8707568,
  abstract     = {{Clustering algorithms play an essential and unique role in classification tasks, especially when annotated data are unavailable or very scarce. Current clustering approaches in remote sensing are mostly designed for a single data source, such as hyperspectral image (HSI), while, nowadays, multisensor data are being routinely acquired. In this article, we propose a multiview subspace clustering model that exploits effectively the rich information from multiple features extracted either from a single data source (HSI) or from multiple sources that we call generically multiviews of the same scene. An important novelty of our approach is that it integrates local and nonlocal spatial information from each view in a unified framework. Our model learns a common intrinsic cluster structure from view-specific subspace representations by a new decomposition-based scheme. In addition, we develop innovative manifold-based spatial regularization as a hybrid hypergraph, which merges local and nonlocal spatial context and improves, thereby, the learning of view-specific structures. We develop an efficient algorithm to solve the resulting optimization problem. Extensive experiments on real data sets demonstrate the superior clustering performance over the state of the art.}},
  articleno    = {{5505816}},
  author       = {{Huang, Shaoguang and Zhang, Hongyan and Pizurica, Aleksandra}},
  issn         = {{0196-2892}},
  journal      = {{IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING}},
  keywords     = {{Electrical and Electronic Engineering,General Earth and Planetary Sciences,Sparse matrices,Feature extraction,Clustering algorithms,Image segmentation,Erbium,Hyperspectral imaging,Clustering methods,Hyperspectral images (HSIs),multiview clustering,remote sensing,subspace clustering}},
  language     = {{eng}},
  pages        = {{16}},
  title        = {{Hybrid-hypergraph regularized multiview subspace clustering for hyperspectral images}},
  url          = {{http://dx.doi.org/10.1109/tgrs.2021.3074184}},
  volume       = {{60}},
  year         = {{2022}},
}

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