{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:16:58Z","timestamp":1740154618039,"version":"3.37.3"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,21]],"date-time":"2024-01-21T00:00:00Z","timestamp":1705795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62001003"]},{"name":"Natural Science Foundation of Anhui Province","award":["2008085QF284"]},{"name":"China Postdoctoral Science Foundation","award":["2020M671851"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Due to the complex imaging mechanism of SAR images and the lack of multi-angle and multi-parameter real scene SAR target data, the generalization performance of existing deep-learning-based synthetic aperture radar (SAR) image target detection methods are extremely limited. In this paper, we propose an unsupervised domain-adaptive SAR ship detection method based on cross-domain feature interaction and data contribution balance. First, we designed a new cross-domain image generation module called CycleGAN-SCA to narrow the gap between the source domain and the target domain. Second, to alleviate the influence of complex backgrounds on ship detection, a new backbone using a self-attention mechanism to tap the potential of feature representation was designed. Furthermore, aiming at the problems of low resolution, few features and easy information loss of small ships, a new lightweight feature fusion and feature enhancement neck was designed. Finally, to balance the influence of different quality samples on the model, a simple and efficient E12IoU Loss was constructed. Experimental results based on a self-built large-scale optical-SAR cross-domain target detection dataset show that compared with existing cross-domain methods, our method achieved optimal performance, with the mAP reaching 68.54%. Furthermore, our method achieved a 6.27% improvement compared to the baseline, even with only 5% of the target domain labeled data.<\/jats:p>","DOI":"10.3390\/rs16020420","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T11:49:31Z","timestamp":1705924171000},"page":"420","source":"Crossref","is-referenced-by-count":5,"title":["Unsupervised Domain-Adaptive SAR Ship Detection Based on Cross-Domain Feature Interaction and Data Contribution Balance"],"prefix":"10.3390","volume":"16","author":[{"given":"Yanrui","family":"Yang","sequence":"first","affiliation":[{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China"},{"name":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electronics and Information Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China"},{"name":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electronics and Information Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Long","family":"Sun","sequence":"additional","affiliation":[{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3688-9633","authenticated-orcid":false,"given":"Zheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8023-9075","authenticated-orcid":false,"given":"Zhixiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electronics and Information Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Bocai","family":"Wu","sequence":"additional","affiliation":[{"name":"38th Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3616","DOI":"10.1109\/JSTARS.2017.2692820","article-title":"A novel ship detector based on the generalized-likelihood ratio test for SAR imagery","volume":"10","author":"Iervolino","year":"2017","journal-title":"IEEE J. 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