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From model-based optimization algorithms to deep learning models for clustering hyperspectral images

Shaoguang Huang (UGent) , Hongyan Zhang, Haijin Zeng (UGent) and Aleksandra Pizurica (UGent)
(2023) REMOTE SENSING. 15(11).
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Abstract
Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishable in visible and multi-spectral images. Clustering of HSIs, which aims to unveil class patterns in an unsupervised way, is highly important in the interpretation of HSI, especially when labelled data are not available. A number of HSI clustering methods have been proposed. Among them, model-based optimization algorithms, which learn the cluster structure of data by solving convex/non-convex optimization problems, have achieved the current state-of-the-art performance. Recent works extend the model-based algorithms to deep versions with deep neural networks, obtaining huge breakthroughs in clustering performance. However, a systematic survey on the topic is absent. This article provides a comprehensive overview of clustering methods of HSI and tracked the latest techniques and breakthroughs in the domain, including the traditional model-based optimization algorithms and the emerging deep learning based clustering methods. With a new taxonomy, we elaborated on the main ideas, technical details, advantages, and disadvantages of different types of clustering methods of HSIs. We provided a systematic performance comparison between different clustering methods by conducting extensive experiments on real HSIs. Unsolved problems and future research trends in the domain are pointed out. Moreover, we provided a toolbox that contains implementations of representative clustering algorithms to help researchers to develop their own models.
Keywords
hyperspectral images, remote sensing, model-based optimization, clustering, deep learning, NONNEGATIVE MATRIX FACTORIZATION, UNSUPERVISED CLASSIFICATION, SPARSE REPRESENTATION, DICTIONARY, FUSION, LOCALIZATION, PERSPECTIVE, RECOGNITION, EXTRACTION, FRAMEWORK

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MLA
Huang, Shaoguang, et al. “From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images.” REMOTE SENSING, vol. 15, no. 11, 2023, doi:10.3390/rs15112832.
APA
Huang, S., Zhang, H., Zeng, H., & Pizurica, A. (2023). From model-based optimization algorithms to deep learning models for clustering hyperspectral images. REMOTE SENSING, 15(11). https://doi.org/10.3390/rs15112832
Chicago author-date
Huang, Shaoguang, Hongyan Zhang, Haijin Zeng, and Aleksandra Pizurica. 2023. “From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images.” REMOTE SENSING 15 (11). https://doi.org/10.3390/rs15112832.
Chicago author-date (all authors)
Huang, Shaoguang, Hongyan Zhang, Haijin Zeng, and Aleksandra Pizurica. 2023. “From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images.” REMOTE SENSING 15 (11). doi:10.3390/rs15112832.
Vancouver
1.
Huang S, Zhang H, Zeng H, Pizurica A. From model-based optimization algorithms to deep learning models for clustering hyperspectral images. REMOTE SENSING. 2023;15(11).
IEEE
[1]
S. Huang, H. Zhang, H. Zeng, and A. Pizurica, “From model-based optimization algorithms to deep learning models for clustering hyperspectral images,” REMOTE SENSING, vol. 15, no. 11, 2023.
@article{01HCFHX4FB98FMWDSS77VY78T1,
  abstract     = {{Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishable in visible and multi-spectral images. Clustering of HSIs, which aims to unveil class patterns in an unsupervised way, is highly important in the interpretation of HSI, especially when labelled data are not available. A number of HSI clustering methods have been proposed. Among them, model-based optimization algorithms, which learn the cluster structure of data by solving convex/non-convex optimization problems, have achieved the current state-of-the-art performance. Recent works extend the model-based algorithms to deep versions with deep neural networks, obtaining huge breakthroughs in clustering performance. However, a systematic survey on the topic is absent. This article provides a comprehensive overview of clustering methods of HSI and tracked the latest techniques and breakthroughs in the domain, including the traditional model-based optimization algorithms and the emerging deep learning based clustering methods. With a new taxonomy, we elaborated on the main ideas, technical details, advantages, and disadvantages of different types of clustering methods of HSIs. We provided a systematic performance comparison between different clustering methods by conducting extensive experiments on real HSIs. Unsolved problems and future research trends in the domain are pointed out. Moreover, we provided a toolbox that contains implementations of representative clustering algorithms to help researchers to develop their own models.}},
  articleno    = {{2832}},
  author       = {{Huang, Shaoguang and  Zhang, Hongyan and Zeng, Haijin and Pizurica, Aleksandra}},
  issn         = {{2072-4292}},
  journal      = {{REMOTE SENSING}},
  keywords     = {{hyperspectral images,remote sensing,model-based optimization,clustering,deep learning,NONNEGATIVE MATRIX FACTORIZATION,UNSUPERVISED CLASSIFICATION,SPARSE REPRESENTATION,DICTIONARY,FUSION,LOCALIZATION,PERSPECTIVE,RECOGNITION,EXTRACTION,FRAMEWORK}},
  language     = {{eng}},
  number       = {{11}},
  pages        = {{43}},
  title        = {{From model-based optimization algorithms to deep learning models for clustering hyperspectral images}},
  url          = {{http://doi.org/10.3390/rs15112832}},
  volume       = {{15}},
  year         = {{2023}},
}
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