{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T02:35:12Z","timestamp":1778812512876,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T00:00:00Z","timestamp":1604448000000},"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":["61725105"],"award-info":[{"award-number":["61725105"]}],"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":["41701508"],"award-info":[{"award-number":["41701508"]}],"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>Synthetic Aperture Radar (SAR) target classification is an important branch of SAR image interpretation. The deep learning based SAR target classification algorithms have made remarkable achievements. But the acquisition and annotation of SAR target images are time-consuming and laborious, and it is difficult to obtain sufficient training data in many cases. The insufficient training data can make deep learning based models suffering from over-fitting, which will severely limit their wide application in SAR target classification. Motivated by the above problem, this paper employs transfer-learning to transfer the prior knowledge learned from a simulated SAR dataset to a real SAR dataset. To overcome the sample restriction problem caused by the poor feature discriminability for real SAR data. A simple and effective sample spectral regularization method is proposed, which can regularize the singular values of each SAR image feature to improve the feature discriminability. Based on the proposed regularization method, we design a transfer-learning pipeline to leverage the simulated SAR data as well as acquire better feature discriminability. The experimental results indicate that the proposed method is feasible for the sample restriction problem in SAR target classification. Furthermore, the proposed method can improve the classification accuracy when relatively sufficient training data is available, and it can be plugged into any convolutional neural network (CNN) based SAR classification models.<\/jats:p>","DOI":"10.3390\/rs12213628","type":"journal-article","created":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T00:00:37Z","timestamp":1604534437000},"page":"3628","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["SAR Target Classification Based on Sample Spectral Regularization"],"prefix":"10.3390","volume":"12","author":[{"given":"Wei","family":"Liang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6730-9844","authenticated-orcid":false,"given":"Tengfei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhui","family":"Diao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian","family":"Sun","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangjin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Fu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yirong","family":"Wu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,4]]},"reference":[{"key":"ref_1","first-page":"187","article-title":"The automatic target-recognition system in SAIP","volume":"10","author":"Novak","year":"1997","journal-title":"Linc. Lab. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/7.892675","article-title":"Performance of 10- and 20-target MSE classifiers","volume":"36","author":"Novak","year":"2000","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/7.570713","article-title":"Effects of polarization and resolution on SAR ATR","volume":"33","author":"Novak","year":"1997","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1117\/12.242040","article-title":"Model-based SAR ATR system","volume":"2757","author":"Ikeuchi","year":"1996","journal-title":"Proc. SPIE"},{"key":"ref_5","first-page":"662","article-title":"SAR ATR: So what\u2019s the problem? An MSTAR perspective","volume":"Volume 3721","author":"Ross","year":"1999","journal-title":"Algorithms for Synthetic Aperture Radar Imagery VI, Proceedings of the AEROSENSE \u201999, Orlando, FL, USA, 5\u20139 April 1999"},{"key":"ref_6","unstructured":"Hummel, R. (2000, January 12). Model-based ATR using synthetic aperture radar. Proceedings of the IEEE International Radar Conference, Alexandria, VA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1117\/12.321851","article-title":"Moving and stationary target acquisition and recognition (MSTAR) model-based automatic target recognition: Search technology for a robust ATR","volume":"3370","author":"Diemunsch","year":"1998","journal-title":"Proc. SPIE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1109\/7.937475","article-title":"Support vector machines for SAR automatic target recognition","volume":"37","author":"Zhao","year":"2001","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TAES.2007.357120","article-title":"Adaptive boosting for SAR automatic target recognition","volume":"43","author":"Sun","year":"2007","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/7.913670","article-title":"SAR ATR performance using a conditionally Gaussian model","volume":"37","author":"DeVore","year":"2001","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_11","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_12","unstructured":"Wagner, S. (2014, January 7\u201310). Combination of convolutional feature extraction and support vector machines for radar ATR. Proceedings of the 17th International Conference on Information Fusion (FUSION), Salamanca, Spain."},{"key":"ref_13","first-page":"94750F","article-title":"Deep convolutional neural networks for ATR from SAR imagery","volume":"Volume 9475","author":"Morgan","year":"2015","journal-title":"Algorithms for Synthetic Aperture Radar Imagery XXII, Proceedings of the SPIE DEFENSE + SECURITY, Baltimore, MD, USA, 20\u201324 April 2015"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1117\/12.242059","article-title":"MSTAR extended operating conditions: A tutorial","volume":"2757","author":"Keydel","year":"1996","journal-title":"Proc. SPIE"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shijie, J., Ping, W., Peiyi, J., and Siping, H. (2017, January 20\u201322). Research on data augmentation for image classification based on convolution neural networks. Proceedings of the 2017 Chinese Automation Congress (CAC), Jinan, China.","DOI":"10.1109\/CAC.2017.8243510"},{"key":"ref_17","unstructured":"Alexander, R., Henry R, E., Zeshan, H., Jared, D., and Christopher, R. (2017, January 4\u20139). Learning to Compose Domain-Specific Transformations for Data Augmentation. Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Man\u00e9, D., Vasudevan, V., and Le, Q.V. (2019, January 15\u201320). AutoAugment: Learning Augmentation Strategies From Data. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref_19","unstructured":"Auer, S. (2011). 3D Synthetic Aperture Radar Simulation for Interpreting Complex Urban Reflection Scenarios, Verlag der Bayerischen Akademie der Wissenschaften. Deutsche Geod\u00e4tische Kommission."},{"key":"ref_20","unstructured":"Hammer, H., and Schulz, K. (September, January 31). Coherent simulation of SAR images. Proceedings of the SPIE Image Signal Process. Remote Sens. XV, Berlin, Germany."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.1109\/TGRS.2009.2022326","article-title":"Hybrid GPU-Based Single- and Double-Bounce SAR Simulation","volume":"47","author":"Balz","year":"2009","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, L., Pan, Z., Qiu, X., and Peng, L. (2018, January 22\u201327). SAR Target Classification with CycleGAN Transferred Simulated Samples. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517866"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"42255","DOI":"10.1109\/ACCESS.2019.2907728","article-title":"Image Data Augmentation for SAR Sensor via Generative Adversarial Nets","volume":"7","author":"Cui","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.1109\/TGRS.2019.2957453","article-title":"LDGAN: A Synthetic Aperture Radar Image Generation Method for Automatic Target Recognition","volume":"58","author":"Cao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1109\/LGRS.2018.2884898","article-title":"Synthetic Aperture Radar Image Generation With Deep Generative Models","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1109\/LGRS.2018.2876378","article-title":"SAR Target Image Classification Based on Transfer Learning and Model Compression","volume":"16","author":"Zhong","year":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019). Deep Transfer Learning for Few-Shot SAR Image Classification. Remote Sens., 11.","DOI":"10.20944\/preprints201905.0030.v1"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019, January 16\u201317). SAR Image Classification Using Few-Shot Cross-Domain Transfer Learning. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00120"},{"key":"ref_30","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_31","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_32","doi-asserted-by":"crossref","first-page":"153391","DOI":"10.1109\/ACCESS.2019.2948618","article-title":"SAR Target Recognition Based on Cross-Domain and Cross-Task Transfer Learning","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","unstructured":"Wang, K., Zhang, G., Xu, Y., and Leung, H. (2020). SAR Target Recognition Based on Probabilistic Meta-Learning. IEEE Geosci. Remote. Sens. Lett., 1\u20135."},{"key":"ref_34","unstructured":"Chen, X., Wang, S., Long, M., and Wang, J. (2019, January 9\u201315). Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_35","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning. Advances in Neural Information Processing Systems 32, Curran Associates, Inc."},{"key":"ref_36","unstructured":"Kusk, A., Abulaitijiang, A., and Dall, J. (2016, January 6\u20139). Synthetic SAR Image Generation using Sensor, Terrain and Target Models. Proceedings of the EUSAR 2016: 11th European Conference on Synthetic Aperture Radar, Hamburg, Germany."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Qiao, S., Liu, C., Shen, W., and Yuille, A. (2018, January 18\u201323). Few-Shot Image Recognition by Predicting Parameters from Activations. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00755"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1109\/JSTARS.2015.2513481","article-title":"SAR Target Recognition Via Sparse Representation of Monogenic Signal on Grassmann Manifolds","volume":"9","author":"Dong","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.1109\/JSTARS.2015.2436694","article-title":"SAR Target Recognition via Joint Sparse Representation of Monogenic Signal","volume":"8","author":"Dong","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.1109\/JSTARS.2018.2830103","article-title":"Configuration Recognition via Class-Dependent Structure Preserving Projections With Application to Targets in SAR Images","volume":"11","author":"Liu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1049\/iet-rsn.2018.5132","article-title":"Target recognition in SAR image based on robust locality discriminant projection","volume":"12","author":"Yu","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"42190","DOI":"10.1109\/ACCESS.2019.2906564","article-title":"A Gradually Distilled CNN for SAR Target Recognition","volume":"7","author":"Min","year":"2019","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/LGRS.2018.2865608","article-title":"Multiple Feature Aggregation Using Convolutional Neural Networks for SAR Image-Based Automatic Target Recognition","volume":"15","author":"Cho","year":"2018","journal-title":"IEEE Geosci. Remote. Sens. 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