{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:16:47Z","timestamp":1772727407870,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T00:00:00Z","timestamp":1615507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61973285, 62076226, 61873249, 61773355"],"award-info":[{"award-number":["61973285, 62076226, 61873249, 61773355"]}]},{"name":"Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing","award":["KLIGIP-2019A04"],"award-info":[{"award-number":["KLIGIP-2019A04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI) classification. However, the most successful CNN architectures are handcrafted, which need professional knowledge and consume a very significant amount of time. To automatically design cell-based CNN architectures for HSI classification, we propose an efficient continuous evolutionary method, named CPSO-Net, which can dramatically accelerate optimal architecture generation by the optimization of weight-sharing parameters. First, a SuperNet with all candidate operations is maintained to share the parameters for all individuals and optimized by collecting the gradients of all individuals in the population. Second, a novel direct encoding strategy is devised to encode architectures into particles, which inherit the parameters from the SuperNet. Then, particle swarm optimization is used to search for the optimal deep architecture from the particle swarm. Furthermore, experiments with limited training samples based on four widely used biased and unbiased hyperspectral datasets showed that our proposed method achieves good performance comparable to the state-of-the-art HSI classification methods.<\/jats:p>","DOI":"10.3390\/rs13061082","type":"journal-article","created":{"date-parts":[[2021,3,14]],"date-time":"2021-03-14T23:52:06Z","timestamp":1615765926000},"page":"1082","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Continuous Particle Swarm Optimization-Based Deep Learning Architecture Search for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8298-7715","authenticated-orcid":false,"given":"Xiaobo","family":"Liu","sequence":"first","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences (Wuhan), Wuhan 430078, China"}]},{"given":"Chaochao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0020-6503","authenticated-orcid":false,"given":"Zhihua","family":"Cai","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences (Wuhan), Wuhan 430078, China"},{"name":"School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Beibu Gulf Big Data Resources Utilisation Laboratory, Beibu Gulf University, Qinzhou 535011, China"}]},{"given":"Jianfeng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"given":"Zhilang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"given":"Xin","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences (Wuhan), Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MSP.2013.2279179","article-title":"Advances in hyperspectral image classification: Earth monitoring with statistical learning methods","volume":"31","author":"Tuia","year":"2014","journal-title":"IEEE Signal Process. 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