{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:42:04Z","timestamp":1773330124673,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Xingliao Talents Program of Liaoning Province","award":["XLYC1907134"],"award-info":[{"award-number":["XLYC1907134"]}]},{"name":"the Xingliao Talents Program of Liaoning Province","award":["LJKZ0174"],"award-info":[{"award-number":["LJKZ0174"]}]},{"name":"the Xingliao Talents Program of Liaoning Province","award":["2018B21"],"award-info":[{"award-number":["2018B21"]}]},{"name":"the Scientific Research Project of the Department of Education of Liaoning Province","award":["XLYC1907134"],"award-info":[{"award-number":["XLYC1907134"]}]},{"name":"the Scientific Research Project of the Department of Education of Liaoning Province","award":["LJKZ0174"],"award-info":[{"award-number":["LJKZ0174"]}]},{"name":"the Scientific Research Project of the Department of Education of Liaoning Province","award":["2018B21"],"award-info":[{"award-number":["2018B21"]}]},{"name":"Liaoning BaiQianWan Talents Program","award":["XLYC1907134"],"award-info":[{"award-number":["XLYC1907134"]}]},{"name":"Liaoning BaiQianWan Talents Program","award":["LJKZ0174"],"award-info":[{"award-number":["LJKZ0174"]}]},{"name":"Liaoning BaiQianWan Talents Program","award":["2018B21"],"award-info":[{"award-number":["2018B21"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A neural network-based object detection algorithm has the advantages of high accuracy and end-to-end processing, and it has been widely used in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation of ship targets, the complex background of near-shore scenes, and the dense arrangement of some ships make it difficult to improve detection accuracy. To solve the above problem, in this paper, a spatial cross-scale attention network (SCSA-Net) for SAR image ship detection is proposed, which includes a novel spatial cross-scale attention (SCSA) module for eliminating the interference of land background. The SCSA module uses the features at each scale output from the backbone to calculate where the network needs attention in space and enhances the features of the feature pyramid network (FPN) output to eliminate interference from noise, and land complex backgrounds. In addition, this paper analyzes the reasons for the \u201cscore shift\u201d problem caused by average precision loss (AP loss) and proposes the global average precision loss (GAP loss) to solve the \u201cscore shift\u201d problem. GAP loss enables the network to distinguish positive samples and negative samples faster than focal loss and AP loss, and achieve higher accuracy. Finally, we validate and illustrate the effectiveness of the proposed method by performing it on SAR Ship Detection Dataset (SSDD), SAR-ship-dataset, and High-Resolution SAR Images Dataset (HRSID). The experimental results show that the proposed method can significantly reduce the interference of background noise on the ship detection results, improve the detection accuracy, and achieve superior results to the existing methods.<\/jats:p>","DOI":"10.3390\/rs15020350","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T04:47:08Z","timestamp":1673239628000},"page":"350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Spatial Cross-Scale Attention Network and Global Average Accuracy Loss for SAR Ship Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9287-3612","authenticated-orcid":false,"given":"Lili","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronical and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Yuxuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronical and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Lele","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Electronical and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Jiannan","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Electronical and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Junpeng","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Tsinghua University, Beijing 100084, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1109\/JSTARS.2022.3169339","article-title":"SEFEPNet: Scale Expansion and Feature Enhancement Pyramid Network for SAR Aircraft Detection with Small Sample Dataset","volume":"15","author":"Zhang","year":"2022","journal-title":"IEEE J. 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