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To tackle this particular challenge, we propose a hybrid relation network, H-RNet, by combining three-dimensional (3-D) convolution neural networks (CNN) and two-dimensional (2-D) CNN to extract the spectral\u2013spatial features whilst reducing the complexity of the network. In an end-to-end relation learning module, the sample pairing approach can effectively alleviate the problem of few labeled samples and learn correlations between samples more accurately for more effective classification. Experimental results on three publicly available datasets have fully demonstrated the superior performance of the proposed model in comparison to a few state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs15102497","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T01:57:51Z","timestamp":1683683871000},"page":"2497","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["H-RNet: Hybrid Relation Network for Few-Shot Learning-Based Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaoyong","family":"Liu","sequence":"first","affiliation":[{"name":"School of Data Science and Engineering, Guangdong Polytechnic Normal University, Guangzhou 510630, China"},{"name":"Academy of Heyuan, Guangdong Polytechnic Normal University, Heyuan 517099, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyang","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510630, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huihui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510630, China"},{"name":"Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangdong Polytechnic Normal University, Guangzhou 510665, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6116-3194","authenticated-orcid":false,"given":"Jinchang","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510630, China"},{"name":"National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huimin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510630, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510630, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510630, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhanhao","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510630, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6248","DOI":"10.1080\/01431161.2020.1736732","article-title":"Feature Extraction for Hyperspectral Image Classification: A Review","volume":"41","author":"Kumar","year":"2020","journal-title":"Int. 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