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The main contributions and novelties of the method are as follows: (1) the 3D-RAN can directly process the 3D HSI data and effectively preserve the data structure of HSI; (2) the constructed residual module can supplement the loss of some important information during the process of information transmission, so that it makes the extracted features contain richer information; (3) the embedded attention module can strengthen the important features and suppress the unimportant features by adjusting weights, which effectively improves the classification performance. Moreover, weight sharing makes the attention module require fewer parameters. The experiments on three public HSI data sets demonstrate that the proposed 3D-RAN outperforms several state-of-the-art methods. <\/jats:p>","DOI":"10.1142\/s0219691323500042","type":"journal-article","created":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T03:21:40Z","timestamp":1672975300000},"source":"Crossref","is-referenced-by-count":2,"title":["3D residual attention network for hyperspectral image classification"],"prefix":"10.1142","volume":"21","author":[{"given":"Huizhen","family":"Li","sequence":"first","affiliation":[{"name":"Research Center for Applied Mathematics and Interdisciplinary Sciences, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kanghui","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, P. R. 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