{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T15:53:42Z","timestamp":1782575622364,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,19]],"date-time":"2022-03-19T00:00:00Z","timestamp":1647648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001003"],"award-info":[{"award-number":["62001003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2008085QF284"],"award-info":[{"award-number":["2008085QF284"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020M671851"],"award-info":[{"award-number":["2020M671851"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic-aperture radar (SAR) image target detection is widely used in military, civilian and other fields. However, existing detection methods have low accuracy due to the limitations presented by the strong scattering of SAR image targets, unclear edge contour information, multiple scales, strong sparseness, background interference, and other characteristics. In response, for SAR target detection tasks, this paper combines the global contextual information perception of transformers and the local feature representation capabilities of convolutional neural networks (CNNs) to innovatively propose a visual transformer framework based on contextual joint-representation learning, referred to as CRTransSar. First, this paper introduces the latest Swin Transformer as the basic architecture. Next, it introduces the CNN\u2019s local information capture and presents the design of a backbone, called CRbackbone, based on contextual joint representation learning, to extract richer contextual feature information while strengthening SAR target feature attributes. Furthermore, the design of a new cross-resolution attention-enhancement neck, called CAENeck, is presented to enhance the characterizability of multiscale SAR targets. The mAP of our method on the SSDD dataset attains 97.0% accuracy, reaching state-of-the-art levels. In addition, based on the HISEA-1 commercial SAR satellite, which has been launched into orbit and in whose development our research group participated, we released a larger-scale SAR multiclass target detection dataset, called SMCDD, which verifies the effectiveness of our method.<\/jats:p>","DOI":"10.3390\/rs14061488","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"1488","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":155,"title":["CRTransSar: A Visual Transformer Based on Contextual Joint Representation Learning for SAR Ship Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Runfan","family":"Xia","sequence":"first","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China"},{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China"},{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiyao","family":"Wan","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China"},{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bocai","family":"Wu","sequence":"additional","affiliation":[{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Sun","sequence":"additional","affiliation":[{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230601, China"},{"name":"National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710126, China"},{"name":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi\u2019an 710126, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baidong","family":"Yao","sequence":"additional","affiliation":[{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haibing","family":"Xiang","sequence":"additional","affiliation":[{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengdao","family":"Xing","sequence":"additional","affiliation":[{"name":"National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710126, China"},{"name":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi\u2019an 710126, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1109\/JSEN.2019.2947114","article-title":"Ground moving target imaging based on compressive sensing framework with single-channel SAR","volume":"20","author":"Kang","year":"2019","journal-title":"IEEE Sens. 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