{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:31:41Z","timestamp":1764333101357,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Aeronautical Science Foundation of China","award":["2019200P4001"],"award-info":[{"award-number":["2019200P4001"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701156"],"award-info":[{"award-number":["61701156"]}],"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>A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure optimization and small sample augmentation. For model structure optimization, the first approach is to extract deep features of the target with residual networks instead of with VGGNet. Then, the aspect ratios of the default boxes are redesigned to match the different targets\u2019 sizes. For small sample augmentation, besides the routine image processing methods, such as rotating, translating, and mirroring, enough training samples are obtained based on the saliency map theory in machine vision. Lastly, a simulated SAR image dataset called Geometric Objects (GO) is constructed, which contains dihedral angles, surface plates and cylinders. The experimental results on the GO-simulated image dataset and the MSTAR real image dataset demonstrate that the proposed method has better performance in SAR target detection than other detection methods.<\/jats:p>","DOI":"10.3390\/rs14010180","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:06:15Z","timestamp":1641769575000},"page":"180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["SAR Target Detection Based on Improved SSD with Saliency Map and Residual Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Fang","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Hefei 230009, China"}]},{"given":"Fengjie","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Hefei 230009, China"}]},{"given":"Changchun","family":"Gui","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Hefei 230009, China"}]},{"given":"Zhangyu","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Hefei 230009, China"}]},{"given":"Mengdao","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Hefei 230009, China"},{"name":"Institute of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","article-title":"Real-Time Processing of Spaceborne SAR Data With Nonlinear Trajectory Based on Variable PRF","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. 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