{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T21:59:59Z","timestamp":1776463199902,"version":"3.51.2"},"reference-count":96,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"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":["61673259"],"award-info":[{"award-number":["61673259"]}],"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":["21510760600"],"award-info":[{"award-number":["21510760600"]}],"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":["21010501900"],"award-info":[{"award-number":["21010501900"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai \u201cScience and Technology Innovation Action Plan\u201d Hong Kong, Macao, and Taiwan Science and Technology Cooperation Project","award":["61673259"],"award-info":[{"award-number":["61673259"]}]},{"name":"Shanghai \u201cScience and Technology Innovation Action Plan\u201d Hong Kong, Macao, and Taiwan Science and Technology Cooperation Project","award":["21510760600"],"award-info":[{"award-number":["21510760600"]}]},{"name":"Shanghai \u201cScience and Technology Innovation Action Plan\u201d Hong Kong, Macao, and Taiwan Science and Technology Cooperation Project","award":["21010501900"],"award-info":[{"award-number":["21010501900"]}]},{"name":"Capacity Building Project of Local Colleges and Universities of Shanghai","award":["61673259"],"award-info":[{"award-number":["61673259"]}]},{"name":"Capacity Building Project of Local Colleges and Universities of Shanghai","award":["21510760600"],"award-info":[{"award-number":["21510760600"]}]},{"name":"Capacity Building Project of Local Colleges and Universities of Shanghai","award":["21010501900"],"award-info":[{"award-number":["21010501900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic Aperture Radar (SAR) target detection is a significant research direction in radar information processing. Aiming at the poor robustness and low detection accuracy of traditional detection algorithms, SAR image target detection based on the Convolutional Neural Network (CNN) is reviewed in this paper. Firstly, the traditional SAR image target detection algorithms are briefly discussed, and their limitations are pointed out. Secondly, the CNN\u2019s network principle, basic structure, and development process in computer vision are introduced. Next, the SAR target detection based on CNN is emphatically analyzed, including some common data sets and image processing methods for SAR target detection. The research status of SAR image target detection based on CNN is summarized and compared in detail with traditional algorithms. Afterward, the challenges of SAR image target detection are discussed and future research is proposed. Finally, the whole article is summarized. By summarizing and analyzing prior research work, this paper is helpful for subsequent researchers to quickly recognize the current development status and identify the connections between various detection algorithms. Beyond that, this paper summarizes the problems and challenges confronting researchers in the future, and also points out the specific content of future research, which has certain guiding significance for promoting the progress of SAR image target detection.<\/jats:p>","DOI":"10.3390\/rs14246240","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T06:14:00Z","timestamp":1670566440000},"page":"6240","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["A Survey of SAR Image Target Detection Based on Convolutional Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2637-6765","authenticated-orcid":false,"given":"Ying","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Yisheng","family":"Hao","sequence":"additional","affiliation":[{"name":"Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159262","DOI":"10.1109\/ACCESS.2019.2951030","article-title":"MSARN: A Deep Neural Network Based on An Adaptive Recalibration Mechanism for Multiscale and Arbitrary-oriented SAR Ship Detection","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., and Zhang, H. 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