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Semi-supervised and self-supervised algorithms have advantages in coping with this phenomenon. This paper primarily concentrates on applying self-supervised strategies to make strides in semi-supervised HSI classification. Notably, we design an effective and a unified self-supervised assisted semi-supervised residual network (SSRNet) framework for HSI classification. The SSRNet contains two branches, i.e., a semi-supervised and a self-supervised branch. The semi-supervised branch improves performance by introducing HSI data perturbation via a spectral feature shift. The self-supervised branch characterizes two auxiliary tasks, including masked bands reconstruction and spectral order forecast, to memorize the discriminative features of HSI. SSRNet can better explore unlabeled HSI samples and improve classification performance. Extensive experiments on four benchmarks datasets, including Indian Pines, Pavia University, Salinas, and Houston2013, yield an average overall classification accuracy of 81.65%, 89.38%, 93.47% and 83.93%, which sufficiently demonstrate that SSRNet can exceed expectations compared to state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs14132997","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:43:00Z","timestamp":1656024180000},"page":"2997","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Self-Supervised Assisted Semi-Supervised Residual Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2187-727X","authenticated-orcid":false,"given":"Liangliang","family":"Song","sequence":"first","affiliation":[{"name":"School of Artifical Intelligentce, Xidian University, Xi\u2019an 710071, China"},{"name":"Guangzhou Institute of Technology, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7372-9180","authenticated-orcid":false,"given":"Zhixi","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Artifical Intelligentce, Xidian University, Xi\u2019an 710071, China"},{"name":"Intelligent Decision and Cognitive Innovation Center, State Administration of Science, Technology and Industry for National Defense, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artifical Intelligentce, Xidian University, Xi\u2019an 710071, China"},{"name":"Intelligent Decision and Cognitive Innovation Center, State Administration of Science, Technology and Industry for National Defense, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5011-5768","authenticated-orcid":false,"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artifical Intelligentce, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"School of Artifical Intelligentce, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Z., Huang, L., and He, J. 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