{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T14:35:05Z","timestamp":1783521305694,"version":"3.55.0"},"reference-count":45,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China grant number","award":["2018YFA0701900,2018YFA0701901"],"award-info":[{"award-number":["2018YFA0701900,2018YFA0701901"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Target recognition in synthetic aperture radar (SAR) imagery suffers from speckle noise and geometric distortion brought by the range-based coherent imaging mechanism. A new SAR target recognition system is proposed, using a SAR-to-optical translation network as pre-processing to enhance both automatic and manual target recognition. In the system, SAR images of targets are translated into optical by a modified conditional generative adversarial network (cGAN) whose generator with a symmetric architecture and inhomogeneous convolution kernels is designed to reduce the background clutter and edge blur of the output. After the translation, a typical convolutional neural network (CNN) classifier is exploited to recognize the target types in translated optical images automatically. For training and testing the system, a new multi-view SAR-optical dataset of aircraft targets is created. Evaluations of the translation results based on human vision and image quality assessment (IQA) methods verify the improvement of image interpretability and quality, and translated images obtain higher average accuracy than original SAR data in manual and CNN classification experiments. The good expansibility and robustness of the system shown in extending experiments indicate the promising potential for practical applications of SAR target recognition.<\/jats:p>","DOI":"10.3390\/rs14081793","type":"journal-article","created":{"date-parts":[[2022,4,9]],"date-time":"2022-04-09T05:13:08Z","timestamp":1649481188000},"page":"1793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["SAR Target Recognition Using cGAN-Based SAR-to-Optical Image Translation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8999-5515","authenticated-orcid":false,"given":"Yuchuang","family":"Sun","sequence":"first","affiliation":[{"name":"National Key Lab of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"The School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Jiang","sequence":"additional","affiliation":[{"name":"National Key Lab of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"The School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyao","family":"Yang","sequence":"additional","affiliation":[{"name":"National Key Lab of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"The School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wangzhe","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Lab of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"The School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1364\/JOSA.66.001145","article-title":"Some fundamental properties of speckle","volume":"66","author":"Goodman","year":"1976","journal-title":"JOSA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1109\/LSP.2017.2758203","article-title":"SAR Image Despeckling Using a Convolutional Neural Network","volume":"24","author":"Wang","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chierchia, G., Cozzolino, D., Poggi, G., and Verdoliva, L. (2017, January 23\u201328). SAR image despeckling through convolutional neural networks. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128234"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Hughes, L.H., and Zhu, X.X. (2018). The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion. arXiv.","DOI":"10.5194\/isprs-annals-IV-1-141-2018"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fuentes Reyes, M., Auer, S., Merkle, N., Henry, C., and Schmitt, M. (2019). SAR-to-Optical Image Translation Based on Conditional Generative Adversarial Networks\u2014Optimization, Opportunities and Limits. Remote Sens., 11.","DOI":"10.3390\/rs11172067"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/LGRS.2018.2799232","article-title":"Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN","volume":"15","author":"Hughes","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A survey on object detection in optical remote sensing images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dong, C., Liu, J., and Xu, F. (2018). Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor. Remote Sens., 10.","DOI":"10.3390\/rs10030400"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ren, Y., Zhu, C., and Xiao, S. (2018). Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN. Appl. Sci., 8.","DOI":"10.3390\/app8050813"},{"key":"ref_11","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1109\/LGRS.2019.2894734","article-title":"Synthesis of Multispectral Optical Images From SAR\/Optical Multitemporal Data Using Conditional Generative Adversarial Networks","volume":"16","author":"Bermudez","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2020.3031199","article-title":"Atrous cGAN for SAR to Optical Image Translation","volume":"19","author":"Turnes","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","unstructured":"Darbaghshahi, F.N., Mohammadi, M.R., and Soryani, M. (2020). Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhou, J., Li, M., Zhou, H., and Yu, T. (2020). Quality Assessment of SAR-to-Optical Image Translation. Remote Sens., 12.","DOI":"10.3390\/rs12213472"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"129136","DOI":"10.1109\/ACCESS.2019.2939649","article-title":"SAR-to-Optical Image Translation Using Supervised Cycle-Consistent Adversarial Networks","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","first-page":"228","article-title":"MSTAR extended operating conditions: A tutorial","volume":"Volume 2757","author":"Keydel","year":"1996","journal-title":"Algorithms for Synthetic Aperture Radar Imagery III"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pohl, C., and Van Genderen, J. (2016). Remote Sensing Image Fusion, CRC Press.","DOI":"10.1201\/9781315370101"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/7.745689","article-title":"Automatic target recognition using enhanced resolution SAR data","volume":"35","author":"Novak","year":"1999","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TAES.2007.357120","article-title":"Adaptive boosting for SAR automatic target recognition","volume":"1","author":"Sun","year":"2007","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1080\/01431161.2013.873150","article-title":"Target recognition in SAR imagery based on local gradient ratio pattern","volume":"35","author":"Yuan","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","first-page":"1","article-title":"Target Classification from SAR Imagery Based on the Pixel Grayscale Decline by Graph Convolutional Neural Network","volume":"4","author":"Zhu","year":"2020","journal-title":"IEEE Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mishra, A.K., and Motaung, T. (2015, January 21\u201322). Application of linear and nonlinear PCA to SAR ATR. Proceedings of the 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA), Pardubice, Czech Republic.","DOI":"10.1109\/RADIOELEK.2015.7129065"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/S0262-8856(03)00057-X","article-title":"Genetic algorithm based feature selection for target detection in SAR images","volume":"21","author":"Bhanu","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1018508","DOI":"10.1117\/12.2263218","article-title":"High-performance computing for automatic target recognition in synthetic aperture radar imagery","volume":"Volume 10185","author":"Majumder","year":"2017","journal-title":"Cyber Sensing 2017"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TAES.2016.160061","article-title":"SAR ATR by a combination of convolutional neural network and support vector machines","volume":"52","author":"Wagner","year":"2016","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Qu, Y., Chen, Y., Huang, J., and Xie, Y. (2019, January 15\u201320). Enhanced pix2pix dehazing network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00835"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, X., Yan, H., Huo, C., Yu, J., and Pant, C. (2018, January 20\u201324). Enhancing Pix2Pix for remote sensing image classification. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545870"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"60338","DOI":"10.1109\/ACCESS.2020.2977103","article-title":"A SAR-to-Optical Image Translation Method Based on Conditional Generation Adversarial Network (cGAN)","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Liu, S., Zhang, J., Zhang, Y., Dong, C., and Lin, L. (2018, January 18\u201322). Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00113"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sato, M., Hotta, K., Imanishi, A., Matsuda, M., and Terai, K. (2018, January 19\u201321). Segmentation of Cell Membrane and Nucleus by Improving Pix2pix. Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018), Funchal, Portugal.","DOI":"10.5220\/0006648302160220"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liebelt, J., and Schmid, C. (2010, January 13\u201318). Multi-view object class detection with a 3d geometric model. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539836"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, B., and Saenko, K. (2014, January 1\u20135). From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains. Proceedings of the BMVC 2014, Nottingham, UK.","DOI":"10.5244\/C.28.82"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Peng, X., Sun, B., Ali, K., and Saenko, K. (2015, January 7\u201313). Learning deep object detectors from 3d models. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.151"},{"key":"ref_39","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_40","first-page":"109870H","article-title":"A SAR dataset for ATR development: The Synthetic and Measured Paired Labeled Experiment (SAMPLE)","volume":"Volume 10987","author":"Lewis","year":"2019","journal-title":"Algorithms for Synthetic Aperture Radar Imagery XXVI"},{"key":"ref_41","first-page":"1064709","article-title":"Generative adversarial networks for SAR image realism","volume":"Volume 10647","author":"Lewis","year":"2018","journal-title":"Algorithms for Synthetic Aperture Radar Imagery XXV"},{"key":"ref_42","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_43","unstructured":"Loshchilov, I., and Hutter, F. (2017). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv."},{"key":"ref_44","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Boyat, A.K., and Joshi, B.K. (2015). A review paper: Noise models in digital image processing. arXiv.","DOI":"10.5121\/sipij.2015.6206"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1793\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:50:21Z","timestamp":1760136621000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,8]]},"references-count":45,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14081793"],"URL":"https:\/\/doi.org\/10.3390\/rs14081793","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,8]]}}}