{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:48:58Z","timestamp":1773787738788,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62071204"],"award-info":[{"award-number":["62071204"]}]},{"name":"National Natural Science Foundation of China","award":["BK20201338"],"award-info":[{"award-number":["BK20201338"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["62071204"],"award-info":[{"award-number":["62071204"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20201338"],"award-info":[{"award-number":["BK20201338"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The main challenges of remote sensing image scene classification are extracting discriminative features and making full use of the training data. The current mainstream deep learning methods usually only use the hard labels of the samples, ignoring the potential soft labels and natural labels. Self-supervised learning can take full advantage of natural labels. However, it is difficult to train a self-supervised network due to the limitations of the dataset and computing resources. We propose a self-supervised knowledge distillation network (SSKDNet) to solve the aforementioned challenges. Specifically, the feature maps of the backbone are used as supervision signals, and the branch learns to restore the low-level feature maps after background masking and shuffling. The \u201cdark knowledge\u201d of the branch is transferred to the backbone through knowledge distillation (KD). The backbone and branch are optimized together in the KD process without independent pre-training. Moreover, we propose a feature fusion module to fuse feature maps dynamically. In general, SSKDNet can make full use of soft labels and has excellent discriminative feature extraction capabilities. Experimental results conducted on three datasets demonstrate the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.3390\/rs14194813","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T03:30:37Z","timestamp":1664335837000},"page":"4813","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Remote Sensing Image Scene Classification via Self-Supervised Learning and Knowledge Distillation"],"prefix":"10.3390","volume":"14","author":[{"given":"Yibo","family":"Zhao","sequence":"first","affiliation":[{"name":"Jiangsu Provincial Engineering Laboratory for Pattern Recognition and Computational Intelligence, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0778-9094","authenticated-orcid":false,"given":"Jianjun","family":"Liu","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Engineering Laboratory for Pattern Recognition and Computational Intelligence, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"}]},{"given":"Jinlong","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Engineering Laboratory for Pattern Recognition and Computational Intelligence, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"}]},{"given":"Zebin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. 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