{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T18:49:11Z","timestamp":1775674151136,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundations of China","award":["61901369"],"award-info":[{"award-number":["61901369"]}]},{"name":"National Natural Science Foundations of China","award":["62101454"],"award-info":[{"award-number":["62101454"]}]},{"name":"National Natural Science Foundations of China","award":["CXJJYL2022040"],"award-info":[{"award-number":["CXJJYL2022040"]}]},{"name":"Xi\u2019an University of Posts and Telecommunications Graduate Innovation Foundation","award":["61901369"],"award-info":[{"award-number":["61901369"]}]},{"name":"Xi\u2019an University of Posts and Telecommunications Graduate Innovation Foundation","award":["62101454"],"award-info":[{"award-number":["62101454"]}]},{"name":"Xi\u2019an University of Posts and Telecommunications Graduate Innovation Foundation","award":["CXJJYL2022040"],"award-info":[{"award-number":["CXJJYL2022040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In a hyperspectral image classification (HSIC) task, manually labeling samples requires a lot of manpower and material resources. Therefore, it is of great significance to use small samples to achieve the HSIC task. Recently, convolutional neural networks (CNNs) have shown remarkable performance in HSIC, but they still have some areas for improvement. (1) Convolutional kernel weights are determined through initialization and cannot be adaptively adjusted based on the input data. Therefore, it is difficult to adaptively learn the structural features of the input data. (2) The convolutional kernel size is single per layer, which leads to the loss of local information for a large convolutional kernel or global information for a small convolutional kernel. In order to solve the above problems, we propose a plug-and-play method called dynamic convolution based on structural re-parameterization (DCSRP). The contributions of this method are as follows. Firstly, compared with traditional convolution, dynamic convolution is a non-linear function, so it has more representation power. In addition, it can adaptively capture the contextual information of input data. Secondly, the large convolutional kernel and the small convolutional kernel are integrated into a new large convolutional kernel. The large convolutional kernel shares the advantages of the two convolution kernels, which can capture global information and local information at the same time. The results in three publicly available HSIC datasets show the effectiveness of the DCSRP.<\/jats:p>","DOI":"10.3390\/rs15235561","type":"journal-article","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T12:01:00Z","timestamp":1701259260000},"page":"5561","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Hyperspectral Image Classification Promotion Using Dynamic Convolution Based on Structural Re-Parameterization"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8101-5738","authenticated-orcid":false,"given":"Chen","family":"Ding","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710129, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}]},{"given":"Xu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710129, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}]},{"given":"Jingyi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710129, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}]},{"given":"Yaoyang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710129, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9574-4069","authenticated-orcid":false,"given":"Mengmeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710129, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech and Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi\u2019an 710129, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Flores, H., Lorenz, S., Jackisch, R., Tusa, L., Contreras, I.C., Zimmermann, R., and Gloaguen, R. 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