{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:13:14Z","timestamp":1773346394711,"version":"3.50.1"},"reference-count":18,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,3]],"date-time":"2022-09-03T00:00:00Z","timestamp":1662163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2021YFA1000100"],"award-info":[{"award-number":["2021YFA1000100"]}]},{"name":"National Key R&amp;D Program of China","award":["2021YFA1000102"],"award-info":[{"award-number":["2021YFA1000102"]}]},{"name":"National Key R&amp;D Program of China","award":["61873279"],"award-info":[{"award-number":["61873279"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFA1000100"],"award-info":[{"award-number":["2021YFA1000100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFA1000102"],"award-info":[{"award-number":["2021YFA1000102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873279"],"award-info":[{"award-number":["61873279"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the cost of acquiring and labeling remote sensing images, only a limited number of images with the target objects are obtained and labeled in some practical applications, which severely limits the generalization capability of typical deep learning networks. Self-supervised learning can learn the inherent feature representations of unlabeled instances and is a promising technique for marine ship detection. In this work, we design a more-way CutPaste self-supervised task to train a feature representation network using clean marine surface images with no ships, based on which a two-stage object detection model using Mask R-CNN is improved to detect marine ships. Experimental results show that with a limited number of labeled remote sensing images, the designed model achieves better detection performance than supervised baseline methods in terms of mAP. Particularly, the detection accuracy for small-sized marine ships is evidently improved.<\/jats:p>","DOI":"10.3390\/rs14174383","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4383","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["SS R-CNN: Self-Supervised Learning Improving Mask R-CNN for Ship Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9385-5977","authenticated-orcid":false,"given":"Ling","family":"Jian","sequence":"first","affiliation":[{"name":"School of Economics and Management, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Zhiqi","family":"Pu","sequence":"additional","affiliation":[{"name":"School of Economics and Management, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Lili","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Science, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Tiancan","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Economics and Management, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Xijun","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Science, China University of Petroleum, Qingdao 266580, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. 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Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4383\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:22:51Z","timestamp":1760142171000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,3]]},"references-count":18,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174383"],"URL":"https:\/\/doi.org\/10.3390\/rs14174383","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,3]]}}}