{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T10:29:15Z","timestamp":1769077755752,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T00:00:00Z","timestamp":1724630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62401581"],"award-info":[{"award-number":["62401581"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic aperture radar (SAR) automatic target recognition (ATR) technology has seen significant advancements. Despite these advancements, the majority of research still operates under the closed-set assumption, wherein all test samples belong to classes seen during the training phase. In real-world applications, however, it is common to encounter targets not previously seen during training, posing a significant challenge to the existing methods. Ideally, an ATR system should not only accurately identify known target classes but also effectively reject those belonging to unknown classes, giving rise to the concept of open set recognition (OSR). To address this challenge, we propose a novel approach that leverages the unique capabilities of the Capsule Network and the Kullback-Leibler divergence (KLD) to distinguish unknown classes. This method begins by deeply mining the features of SAR targets using the Capsule Network and enhancing the separability between different features through a specially designed loss function. Subsequently, the KLD of features between a testing sample and the center of each known class is calculated. If the testing sample exhibits a significantly larger KLD compared to all known classes, it is classified as an unknown target. The experimental results of the SAR-ACD dataset demonstrate that our method can maintain a correct identification rate of over 95% for known classes while effectively recognizing unknown classes. Compared to existing techniques, our method exhibits significant improvements.<\/jats:p>","DOI":"10.3390\/rs16173141","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T05:16:24Z","timestamp":1724649384000},"page":"3141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Open-Set Recognition Model for SAR Target Based on Capsule Network with the KLD"],"prefix":"10.3390","volume":"16","author":[{"given":"Chunyun","family":"Jiang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Huiqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6799-620X","authenticated-orcid":false,"given":"Ronghui","family":"Zhan","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Wenyu","family":"Shu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"ref_1","unstructured":"Saghri, J., and Guilas, C. (August, January 31). Hausdorff Probabilistic Feature Analysis in SAR Image Recognition. Proceedings of the Applications of Digital Image Processing XXVIII, San Diego, CA, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1080\/2150704X.2017.1327124","article-title":"Refined segmentation of ship target in SAR images based on GVF snake with elliptical constraint","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mishra, A. (2008, January 19\u201321). Validation of PCA and LDA for SAR ATR. Proceedings of the TENCON 2008, Hyderabad, India.","DOI":"10.1109\/TENCON.2008.4766807"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huan, R., Pan, Y., and Mao, K. (2010, January 28\u201331). SAR image target recognition based on NMF feature extraction and Bayesian decision fusion. Proceedings of the 2010 Second IITA International Conference on Geoscience and Remote Sensing, Qingdao, China.","DOI":"10.1109\/IITA-GRS.2010.5602633"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Deng, Z., Jin, J., Su, J., and Yang, X. (2015, January 10\u201311). Sparse representation of natural image based on Contourlet overcomplete dictionary. Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference (IIICEC 2015), Xi\u2019an, China.","DOI":"10.2991\/iiicec-15.2015.290"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6877","DOI":"10.1109\/TGRS.2019.2909121","article-title":"Subdictionary-Based Joint Sparse Representation for SAR Target Recognition Using Multilevel Reconstruction","volume":"57","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3334","DOI":"10.1109\/JSTARS.2017.2671919","article-title":"Target Recognition in Synthetic Aperture Radar Images via Matching of Attributed Scattering Centers","volume":"10","author":"Ding","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4623","DOI":"10.1109\/JSEN.2018.2828307","article-title":"A Region Matching Approach based on 3-D Scattering Center Model with Application to SAR Target Recognition","volume":"18","author":"Ding","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e2934","DOI":"10.1016\/j.na.2009.07.030","article-title":"SVM-based target recognition from synthetic aperture radar images using target region outline descriptors","volume":"71","author":"Anagnostopoulos","year":"2009","journal-title":"Nonlinear Anal. Theory Methods Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1080\/02564602.2015.1019941","article-title":"An SAR ATR Method Based on Scattering Centre Feature and Bipartite Graph Matching","volume":"32","author":"Tian","year":"2015","journal-title":"IETE Tech. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TAES.2012.6237604","article-title":"Multi-View Automatic Target Recognition using Joint Sparse Representation","volume":"48","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.isprsjprs.2023.12.004","article-title":"Physics inspired hybrid attention for SAR target recognition","volume":"207","author":"Huang","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","first-page":"4331","article-title":"Recognition of SAR Target Based on multilayer Auto-Encoder And SNN","volume":"9","author":"Sun","year":"2013","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guo, Y., Chen, S., Zhan, R., Wang, W., and Zhang, J. (2022). LMSD-YOLO: A lightweight YOLO algorithm for multi-scale SAR ship detection. Remote Sens., 14.","DOI":"10.3390\/rs14194801"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/JSTARS.2020.3041783","article-title":"Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation","volume":"14","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, J., and Peng, C. (2023). Weighted residual network for SAR automatic target recognition with data augmentation. Front. Neurorob., 17.","DOI":"10.3389\/fnbot.2023.1298653"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5213016","DOI":"10.1109\/TGRS.2023.3299419","article-title":"Deep Learning Based Polarimetric Data Augmentation: Dual2Full-pol Extension","volume":"61","author":"Aghababaei","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/TGRS.2016.2551720","article-title":"Target Classification Using the Deep Convolutional Networks for SAR Images","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4611","DOI":"10.1109\/JSTARS.2024.3357171","article-title":"MGSFA-Net: Multiscale Global Scattering Feature Association Network for SAR Ship Target Recognition","volume":"17","author":"Zhang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_20","unstructured":"Matthew, S., and Brian, R. (2016, January 18\u201319). Multi-class open set recognition for SAR imagery. Proceedings of the Automatic Target Recognition XXVI, Baltimore, MD, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dang, S., Cao, Z., Cui, Z., and Pi, Y. (2019, January 26\u201329). Open Set SAR Target Recognition Using Class Boundary Extracting. Proceedings of the 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Xiamen, China.","DOI":"10.1109\/APSAR46974.2019.9048316"},{"key":"ref_22","first-page":"4005105","article-title":"An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR","volume":"20","author":"Wang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3500805","DOI":"10.1109\/LGRS.2023.3342904","article-title":"SAR Target Open-Set Recognition Based on Joint Training of Class-Specific Sub-Dictionary Learning","volume":"21","author":"Ma","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","unstructured":"Oveis, A.H., Giusti, E., Ghio, S., and Martorella, M. (2022, January 25\u201327). Open Set Recognition in SAR Images Using the Openmax Approach: Challenges and Extension to Boost the Accuracy and Robustness. Proceedings of the 14th European Conference on Synthetic Aperture Radar (EUSAR 2022), Leipzig, Germany."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Giusti, E., Ghio, S., Oveis, A.H., and Martorella, M. (2022, January 21\u201325). Open Set Recognition in Synthetic Aperture Radar Using the Openmax Classifier. Proceedings of the 2022 IEEE Radar Conference (RadarConf22), New York, NY, USA.","DOI":"10.1109\/RadarConf2248738.2022.9763898"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Giusti, E., Ghio, S., and Martorella, A.H.O.A. (2022). Proportional Similarity-Based Openmax Classifier for Open Set Recognition in SAR Images. Remote Sens., 14.","DOI":"10.3390\/rs14184665"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Thomson, B., and Scherreik, M. (2023, January 1\u20135). Deep Open World SAR Target Recognition with Regular Polytope Networks. Proceedings of the 2023 IEEE Radar Conference (RadarConf23), San Antonio, TX, USA.","DOI":"10.1109\/RadarConf2351548.2023.10149765"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3954","DOI":"10.1109\/JSTARS.2021.3068944","article-title":"Training SAR-ATR Models for Reliable Operation in Open-World Environments","volume":"14","author":"Inkawhich","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4014005","DOI":"10.1109\/LGRS.2021.3079418","article-title":"An Open Set Recognition Method for SAR Targets Based on Multitask Learning","volume":"19","author":"Ma","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhang, Y., Liu, D., and Wei, Q. (2023). SAR Target Recognition with Limited Training Samples in Open Set Conditions. Sensors, 23.","DOI":"10.3390\/s23031668"},{"key":"ref_31","unstructured":"Cui, Y., Kuang, G., Tang, T., and Zhou, X. (2022, January 25\u201327). SAR Open Set Recognition Based on Counterfactual Framework. Proceedings of the 2022 Photonics and Electromagnetics Research Symposium, Hangzhou, China."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1109\/JSTARS.2022.3225882","article-title":"SAR Target Recognition via Random Sampling Combination in Open-World Environments","volume":"16","author":"Geng","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Neal, L., Olson, M., Fern, X., Wong, W., and Li, F. (2018, January 8\u201314). Open Set Learning with Counterfactual Images. Proceedings of the Computer vision\u2014ECCV 2018: 15th European Conference, Munich, Germany.","DOI":"10.1007\/978-3-030-01231-1_38"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jo, I., Kim, J., Kang, H., Kim, Y., and Choi, S. (2018, January 15\u201320). Open Set Recognition by Regularising Classifier with Fake Data Generated by Generative Adversarial Networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461700"},{"key":"ref_35","unstructured":"Sabour, S., Frosst, N., and Hinton, G. (2017, January 4\u20139). Dynamic routing between capsules. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917), Long Beach, CA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5226517","DOI":"10.1109\/TGRS.2022.3166174","article-title":"SCAN: Scattering Characteristics Analysis Network for Few-Shot Aircraft Classification in High-Resolution SAR Images","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1109\/TPAMI.2020.2981604","article-title":"Recent advances in open set recognition: A survey","volume":"43","author":"Geng","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.ins.2023.01.062","article-title":"Learning multiple gaussian prototypes for open-set recognition","volume":"626","author":"Liu","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bendale, A., and Boult, T.E. (2016, January 27\u201330). Towards open set deep networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yoshihashi, R., Shao, W., Kawakami, R., You, S., Iida, M., and Naemura, T. (2019, January 15\u201320). Classification-Reconstruction Learning for Open-Set Recognition. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00414"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Guo, Y., Camporese, G., Yang, W., Sperduti, A., and Ballan, L. (2021, January 10\u201317). Conditional Variational Capsule Network for Open Set Recognition. Proceedings of the 18th IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00017"},{"key":"ref_42","unstructured":"Dimity, M., Niko, S., Michael, M., and Feras, D. (2021, January 3\u20138). Class Anchor Clustering: A Loss for Distance-based Open Set Recognition. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cheng, X., Huo, Y., Lin, S., Dong, Y., Zhao, S., Zhang, M., and Wang, H. (2024). Deep Feature Aggregation Network for Hyperspectral Anomaly Detection. IEEE Trans. Instrum. Meas., early access.","DOI":"10.1109\/TIM.2024.3403211"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3141\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:42:48Z","timestamp":1760110968000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,26]]},"references-count":43,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173141"],"URL":"https:\/\/doi.org\/10.3390\/rs16173141","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,26]]}}}