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University of Geosciences (Wuhan)","award":["42071322"],"award-info":[{"award-number":["42071322"]}]},{"name":"\u201cCUG Scholar\u201d Scientific Research Funds at China University of Geosciences (Wuhan)","award":["2022164"],"award-info":[{"award-number":["2022164"]}]},{"name":"\u201cCUG Scholar\u201d Scientific Research Funds at China University of Geosciences (Wuhan)","award":["174B09119"],"award-info":[{"award-number":["174B09119"]}]},{"name":"\u201cCUG Scholar\u201d Scientific Research Funds at China University of Geosciences (Wuhan)","award":["BOF.24Y.2021.0049.01"],"award-info":[{"award-number":["BOF.24Y.2021.0049.01"]}]},{"name":"the Flanders AI Research Programme","award":["2022YFB3903605"],"award-info":[{"award-number":["2022YFB3903605"]}]},{"name":"the Flanders AI Research Programme","award":["42071322"],"award-info":[{"award-number":["42071322"]}]},{"name":"the Flanders AI Research Programme","award":["2022164"],"award-info":[{"award-number":["2022164"]}]},{"name":"the Flanders AI Research Programme","award":["174B09119"],"award-info":[{"award-number":["174B09119"]}]},{"name":"the Flanders AI Research Programme","award":["BOF.24Y.2021.0049.01"],"award-info":[{"award-number":["BOF.24Y.2021.0049.01"]}]},{"name":"the Bijzonder Onderzoeksfonds (BOF)","award":["2022YFB3903605"],"award-info":[{"award-number":["2022YFB3903605"]}]},{"name":"the Bijzonder Onderzoeksfonds (BOF)","award":["42071322"],"award-info":[{"award-number":["42071322"]}]},{"name":"the Bijzonder Onderzoeksfonds (BOF)","award":["2022164"],"award-info":[{"award-number":["2022164"]}]},{"name":"the Bijzonder Onderzoeksfonds (BOF)","award":["174B09119"],"award-info":[{"award-number":["174B09119"]}]},{"name":"the Bijzonder Onderzoeksfonds (BOF)","award":["BOF.24Y.2021.0049.01"],"award-info":[{"award-number":["BOF.24Y.2021.0049.01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs15112832","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T02:04:21Z","timestamp":1685412261000},"page":"2832","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Shaoguang","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Department of Telecommunications and Information Processing, Ghent University, 9000 Ghent, Belgium"}]},{"given":"Hongyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Haijin","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Telecommunications and Information Processing, Ghent University, 9000 Ghent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9322-4999","authenticated-orcid":false,"given":"Aleksandra","family":"Pi\u017eurica","sequence":"additional","affiliation":[{"name":"Department of Telecommunications and Information Processing, Ghent University, 9000 Ghent, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/JSTARS.2018.2800701","article-title":"Hyperspectral image denoising using local low-rank matrix recovery and global spatial\u2013spectral total variation","volume":"11","author":"He","year":"2018","journal-title":"IEEE J. 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