{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T13:31:08Z","timestamp":1773840668371,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901198"],"award-info":[{"award-number":["61901198"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071379"],"award-info":[{"award-number":["62071379"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071378"],"award-info":[{"award-number":["62071378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Program Funded by Shaanxi Provincial Education Department","award":["20JK0904"],"award-info":[{"award-number":["20JK0904"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2021JM-461"],"award-info":[{"award-number":["2021JM-461"]}]},{"name":"Program of Qingjiang Excellent Young Talents, Jiangxi University of Science and Technology","award":["JXUSTQJYX2020019"],"award-info":[{"award-number":["JXUSTQJYX2020019"]}]},{"name":"New Star Team of Xi'an University of Posts &amp; Telecommunications","award":["xyt2016-01"],"award-info":[{"award-number":["xyt2016-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks (CNNs) have achieved great results in hyperspectral image (HSI) classification in recent years. However, convolution kernels are reused among different spatial locations, known as spatial-agnostic or weight-sharing kernels. Furthermore, the preference of spatial compactness in convolution (typically, 3\u00d73 kernel size) constrains the receptive field and the ability to capture long-range spatial interactions. To mitigate the above two issues, in this article, we combine a novel operation called involution with residual learning and develop a new deep residual involution network (DRIN) for HSI classification. The proposed DRIN could model long-range spatial interactions well by adopting enlarged involution kernels and realize feature learning in a fairly lightweight manner. Moreover, the vast and dynamic involution kernels are distinct over different spatial positions, which could prioritize the informative visual patterns in the spatial domain according to the spectral information of the target pixel. The proposed DRIN achieves better classification results when compared with both traditional machine learning-based and convolution-based methods on four HSI datasets. Especially in comparison with the convolutional baseline model, i.e., deep residual network (DRN), our involution-powered DRIN model increases the overall classification accuracy by 0.5%, 1.3%, 0.4%, and 2.3% on the University of Pavia, the University of Houston, the Salinas Valley, and the recently released HyRANK HSI benchmark datasets, respectively, demonstrating the potential of involution for HSI classification.<\/jats:p>","DOI":"10.3390\/rs13163055","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T08:47:52Z","timestamp":1628066872000},"page":"3055","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Deep Residual Involution Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2364-2749","authenticated-orcid":false,"given":"Zhe","family":"Meng","sequence":"first","affiliation":[{"name":"School of Telecommunication and Information Engineering (School of Artificial Intelligence), Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0323-9573","authenticated-orcid":false,"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Telecommunication and Information Engineering (School of Artificial Intelligence), Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4289-7114","authenticated-orcid":false,"given":"Miaomiao","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8111-8195","authenticated-orcid":false,"given":"Wen","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Telecommunication and Information Engineering (School of Artificial Intelligence), Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kirsch, M., Lorenz, S., Zimmermann, R., Tusa, L., M\u00f6ckel, R., H\u00f6dl, P., Booysen, R., Khodadadzadeh, M., and Gloaguen, R. 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