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In order to deal with the problem of small samples in hyperspectral image classification, a novel small sample classification method based on rotation-invariant uniform local binary pattern (RULBP) features and a graph-based masked autoencoder is proposed in this paper. Firstly, the RULBP features of hyperspectral images are extracted, and then the k-nearest neighbor method is utilized to construct the graph. Furthermore, self-supervised learning is conducted on the constructed graph so that the model can learn to extract features more suitable for small sample classification. Since the self-supervised training mainly adopts the masked autoencoder method, only unlabeled samples are needed to complete the training. After training, only a small number of samples are used to fine-tune the graph convolutional network, so as to complete the classification of all nodes in the graph. A large number of classification experiments on three commonly used hyperspectral image datasets show that the proposed method could achieve higher classification accuracy with fewer labeled samples.<\/jats:p>","DOI":"10.3390\/rs15071869","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T08:27:27Z","timestamp":1680251247000},"page":"1869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Masked Graph Convolutional Network for Small Sample Classification of Hyperspectral Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Wenkai","family":"Liu","sequence":"first","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0848-8453","authenticated-orcid":false,"given":"Bing","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peipei","family":"He","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingfeng","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2145-199X","authenticated-orcid":false,"given":"Kuiliang","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5923","DOI":"10.1109\/TGRS.2013.2274875","article-title":"Joint Collaborative Representation With Multitask Learning for Hyperspectral Image Classification","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. 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