{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:26:53Z","timestamp":1774538813775,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T00:00:00Z","timestamp":1725321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62371022"],"award-info":[{"award-number":["62371022"]}]},{"name":"National Natural Science Foundation of China","award":["EP\/T021063\/1"],"award-info":[{"award-number":["EP\/T021063\/1"]}]},{"name":"National Natural Science Foundation of China","award":["EP\/T024917\/1"],"award-info":[{"award-number":["EP\/T024917\/1"]}]},{"name":"UK Engineering and Physical Sciences Research Council (EPSRC)","award":["62371022"],"award-info":[{"award-number":["62371022"]}]},{"name":"UK Engineering and Physical Sciences Research Council (EPSRC)","award":["EP\/T021063\/1"],"award-info":[{"award-number":["EP\/T021063\/1"]}]},{"name":"UK Engineering and Physical Sciences Research Council (EPSRC)","award":["EP\/T024917\/1"],"award-info":[{"award-number":["EP\/T024917\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenarios, they may encounter target classes absent from the training set, thereby necessitating an open set recognition (OSR) challenge for SAR-ATR. The crux of OSR lies in establishing distinct decision boundaries between known and unknown classes to mitigate confusion among different classes. To address this issue, we introduce a novel framework termed reinforced class separability for SAR target open set recognition (RCS-OSR), which focuses on optimizing prototype distribution and enhancing the discriminability of features. First, to capture discriminative features, a cross-modal causal features enhancement module (CMCFE) is proposed to strengthen the expression of causal regions. Subsequently, regularized intra-class compactness loss (RIC-Loss) and intra-class relationship aware consistency loss (IRC-Loss) are devised to optimize the embedding space. In conjunction with joint supervised training using cross-entropy loss, RCS-OSR can effectively reduce empirical classification risk and open space risk simultaneously. Moreover, a class-aware OSR classifier with adaptive thresholding is designed to leverage the differences between different classes. Consequently, our method can construct distinct decision boundaries between known and unknown classes to simultaneously classify known classes and identify unknown classes in open scenarios. Extensive experiments conducted on the MSTAR dataset demonstrate the effectiveness and superiority of our method in various OSR tasks.<\/jats:p>","DOI":"10.3390\/rs16173277","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T12:22:33Z","timestamp":1725366153000},"page":"3277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1489-0812","authenticated-orcid":false,"given":"Fei","family":"Gao","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Xin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Rongling","family":"Lang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7184-5057","authenticated-orcid":false,"given":"Jinping","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8080-082X","authenticated-orcid":false,"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[{"name":"Cyber and Big Data Research Laboratory, Edinburgh Napier University, Edinburgh EH11 4BN, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6014","DOI":"10.1109\/ACCESS.2016.2611492","article-title":"Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review","volume":"4","author":"Gill","year":"2016","journal-title":"IEEE Access"},{"key":"ref_2","first-page":"5202813","article-title":"Sar Target Incremental Recognition Based on Features with Strong Separability","volume":"62","author":"Gao","year":"2024","journal-title":"IEEE Trans. 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