{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:35:09Z","timestamp":1780634109235,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M661356"],"award-info":[{"award-number":["2019M661356"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004750","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["ASFC-201918096001"],"award-info":[{"award-number":["ASFC-201918096001"]}],"id":[{"id":"10.13039\/501100004750","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701526, 62001507"],"award-info":[{"award-number":["61701526, 62001507"]}],"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>Even though deep learning (DL) has achieved excellent results on some public data sets for synthetic aperture radar (SAR) automatic target recognition(ATR), several problems exist at present. One is the lack of transparency and interpretability for most of the existing DL networks. Another is the neglect of unknown target classes which are often present in practice. To solve the above problems, a deep generation as well as recognition model is derived based on Conditional Variational Auto-encoder (CVAE) and Generative Adversarial Network (GAN). A feature space for SAR-ATR is built based on the proposed CVAE-GAN model. By using the feature space, clear SAR images can be generated with given class labels and observation angles. Besides, the feature of the SAR image is continuous in the feature space and can represent some attributes of the target. Furthermore, it is possible to classify the known classes and reject the unknown target classes by using the feature space. Experiments on the MSTAR data set validate the advantages of the proposed method.<\/jats:p>","DOI":"10.3390\/rs13183554","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T02:41:07Z","timestamp":1631068867000},"page":"3554","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN"],"prefix":"10.3390","volume":"13","author":[{"given":"Xiaowei","family":"Hu","sequence":"first","affiliation":[{"name":"Key Lab for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China"},{"name":"Early Warning and Detection Department, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4205-538X","authenticated-orcid":false,"given":"Weike","family":"Feng","sequence":"additional","affiliation":[{"name":"Early Warning and Detection Department, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiduo","family":"Guo","sequence":"additional","affiliation":[{"name":"Early Warning and Detection Department, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Experimental Training Base of College of Information and Communication, National University of Defense Technology, Xi\u2019an 710106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tait, P. (2005). Introduction to Radar Target Recognition, The Institution of Engineering and Technology.","DOI":"10.1049\/PBRA018E"},{"key":"ref_2","unstructured":"NovakL., M., Benitz, G.R., Owirka, G.J., and Bessette, L.A. (1996, January 22). ATR performance using enhanced resolution SAR. Proceedings of the SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery III, Orlando, FL, USA."},{"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 IEEE Region 10 Conference, Hyderabad, India.","DOI":"10.1109\/TENCON.2008.4766807"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1109\/JSTARS.2016.2555938","article-title":"SAR Imagery Feature Extraction Using 2DPCA-Based Two-Dimensional Neighborhood Virtual Points Discriminant Embedding","volume":"9","author":"Pei","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3713","DOI":"10.1109\/TGRS.2011.2162526","article-title":"Automatic Target Recognition of SAR Images Based on Global Scattering Center Model","volume":"49","author":"Zhou","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4598","DOI":"10.1109\/JSEN.2019.2901050","article-title":"SAR Automatic Target Recognition Based on Attribute Scattering Center Model and Discriminative Dictionary Learning","volume":"19","author":"Li","year":"2019","journal-title":"IEEE Sensors J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1109\/LGRS.2016.2608578","article-title":"SAR Automatic Target Recognition Based on Dictionary Learning and Joint Dynamic Sparse Representation","volume":"13","author":"Sun","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1109\/TAES.2017.2649160","article-title":"Automatic Target Recognition of Military Vehicles with Krawtchouk Moments","volume":"53","author":"Clemente","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/TAES.2013.120340","article-title":"SAR Automatic Target Recognition Using Discriminative Graphical Models","volume":"50","author":"Srinivas","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/LGRS.2020.2965558","article-title":"Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning","volume":"18","author":"Huang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1109\/LGRS.2020.2983718","article-title":"Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition","volume":"18","author":"Huang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1109\/TGRS.2019.2947634","article-title":"What, Where, and How to Transfer in SAR Target Recognition Based on Deep CNNs","volume":"58","author":"Huang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Huang, Z., Pan, Z., and Lei, B. (2017). Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data. Remote Sens., 9.","DOI":"10.3390\/rs9090907"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1109\/LGRS.2017.2717486","article-title":"Improving SAR Automatic Target Recognition Models with Transfer Learning from Simulated Data","volume":"14","author":"Kusk","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cha, M., Majumdar, A., Kung, H.T., and Barber, J. (2018, January 15\u201320). Improving sar automatic target recognition using simulated images under deep residual refinements. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462109"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, L., Pan, Z., Qiu, X., and Peng, L. (2018). SAR target classification with CycleGAN transferred simulated samples. IEEE Int. Geosci. Remote Sens. Symp., 4411\u20134414.","DOI":"10.1109\/IGARSS.2018.8517866"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1109\/LGRS.2019.2958379","article-title":"SAR Target Recognition with Limited Training Data Based on Angular Rotation Generative Network","volume":"17","author":"Sun","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Song, Q., Xu, F., and Jin, Y.Q. (August, January 28). SAR Image Representation Learning with Adversarial Autoencoder Networks. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898922"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Toizumi, T., Sagi, K., and Senda, Y. (2018, January 22\u201327). Automatic association between SAR and optical images based on zero-shot learning. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517299"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/LGRS.2019.2936897","article-title":"EM simulation-aided zero-shot learning for SAR automatic target recognition","volume":"17","author":"Song","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1109\/LGRS.2017.2758900","article-title":"Zero-shot learning of SAR target feature space with deep generative neural networks","volume":"14","author":"Song","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wei, Q.R., He, H., Zhao, Y., and Li, J.-A. (2021). Learn to Recognize Unknown SAR Targets From Reflection Similarity. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2020.3023086"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Scherreik, M., and Rigling, B. (2016, January 18\u201319). Multi-class open set recognition for SAR imagery. Proceedings of the Automatic Target Recognition XXVI, Baltimore, MD, USA.","DOI":"10.1117\/12.2224384"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4445","DOI":"10.1109\/TGRS.2019.2891266","article-title":"Open set incremental learning for automatic target recognition","volume":"57","author":"Dang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","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 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Xiamen, China.","DOI":"10.1109\/APSAR46974.2019.9048316"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ma, X., Ji, K., Zhang, L., Feng, S., Xiong, B., and Kuang, G. (2021). An Open Set Recognition Method for SAR Targets Based on Multitask Learning. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2021.3079418"},{"key":"ref_29","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_30","unstructured":"Rezende, D.J., Mohamed, S., and Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. arXiv."},{"key":"ref_31","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. arXiv, 2672\u20132680."},{"key":"ref_32","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_33","first-page":"1558","article-title":"Autoencoding beyond pixels using a learned similarity metric","volume":"48","author":"Larsen","year":"2016","journal-title":"Int. Conf. Int. Conf. Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bao, J., Chen, D., Wen, F., Li, H., and Hua, G. (2017). CVAE-GAN: Fine-grained image generation through asymmetric training. arXiv, 2745\u20132754.","DOI":"10.1109\/ICCV.2017.299"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhang, K., Li, Z., and Qiao, Y. (2016, January 8\u201316). A Discriminative Feature Learning Approach for Deep Face Recognition. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, F., Xiang, X., Cheng, J., and Yuille, A.L. (2017, January 23\u201327). NormFace: L2 hypersphere embedding for face verification. Proceedings of the ACM Multimedia Conference, Mountain View, CA, USA.","DOI":"10.1145\/3123266.3123359"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., and Song, L. (2017). SphereFace: Deep Hypersphere Embedding for Face Recognition. arXiv, 6738\u20136746.","DOI":"10.1109\/CVPR.2017.713"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., and Liu, W. (2018). Cosface: Large margin cosine loss for deep face recognition. arXiv, 5265\u20135274.","DOI":"10.1109\/CVPR.2018.00552"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1109\/LSP.2018.2822810","article-title":"Additive margin softmax for face verification","volume":"25","author":"Wang","year":"2018","journal-title":"IEEE Sig. Proc. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., and Zafeiriou, S. (2018). ArcFace: Additive angular margin loss for deep face recognition. arXiv.","DOI":"10.1109\/CVPR.2019.00482"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_42","first-page":"2579","article-title":"Visualizing high-dimensional data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_44","unstructured":"Odena, A., Olah, C., and Shlens, J. (2017, January 6\u201311). Conditional image synthesis with auxiliary classifier GANs. Proceedings of the 34th International Conference on Machine Learning, Sydney, NSW, Australia."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3554\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:58:04Z","timestamp":1760165884000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,7]]},"references-count":44,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183554"],"URL":"https:\/\/doi.org\/10.3390\/rs13183554","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,7]]}}}