{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:12:39Z","timestamp":1773778359559,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:00:00Z","timestamp":1630540800000},"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":["61901091"],"award-info":[{"award-number":["61901091"]}],"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":["61901090"],"award-info":[{"award-number":["61901090"]}],"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) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.<\/jats:p>","DOI":"10.3390\/rs13173493","type":"journal-article","created":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T23:05:12Z","timestamp":1630623912000},"page":"3493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["FEF-Net: A Deep Learning Approach to Multiview SAR Image Target Recognition"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4616-6642","authenticated-orcid":false,"given":"Jifang","family":"Pei","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Zhiyong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Xueping","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Weibo","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Yin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Yulin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Junjie","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Jianyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TAES.1967.5408745","article-title":"Synthetic Aperture Radar","volume":"AES-3","author":"Brown","year":"1967","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1364\/OPN.15.11.000028","article-title":"Synthetic aperture radar","volume":"15","author":"Doerry","year":"2004","journal-title":"Opt. Photonics News"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Blacknell, D., and Griffiths, H. (2013). Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR), The Institution of Engineering and Technology.","DOI":"10.1049\/PBRA033E"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1109\/TAES.1986.310772","article-title":"Automatic Target Recognition: State of the Art Survey","volume":"AES-22","author":"Bhanu","year":"1986","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mishra, A.K., and Mulgrew, B. (2010). Automatic target recognition. Encycl. Aerosp. Eng., 1\u20138.","DOI":"10.1002\/9780470686652.eae281"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/MAES.2021.3049857","article-title":"Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey","volume":"36","author":"Aouf","year":"2021","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"071598","DOI":"10.1117\/1.JRS.7.071598","article-title":"Target detection in synthetic aperture radar imagery: A state-of-the-art survey","volume":"7","author":"McGuire","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_10","first-page":"25","article-title":"Discriminating targets from clutter","volume":"6","author":"Kreithen","year":"1993","journal-title":"Lincoln Lab. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/TGRS.2010.2052623","article-title":"An Improved Scheme for Target Discrimination in High-Resolution SAR Images","volume":"49","author":"Gao","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","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_13","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_14","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_15","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_16","first-page":"805112","article-title":"Joint sparse representation based automatic target recognition in SAR images","volume":"8051","author":"Zhang","year":"2011","journal-title":"SPIE Defense Secur. Sens. Int. Soc. Opt. Photonics"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_19","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","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_21","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_22","first-page":"364","article-title":"Convolutional Neural Network With Data Augmentation for SAR Target Recognition","volume":"13","author":"Ding","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7282","DOI":"10.1109\/TGRS.2018.2849967","article-title":"SAR ATR of Ground Vehicles Based on LM-BN-CNN","volume":"56","author":"Zhou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Brendel, G.F., and Horowitz, L.L. (2000). Benefits of aspect diversity for SAR ATR: Fundamental and experimental results. AeroSense 2000, International Society for Optics and Photonics.","DOI":"10.1117\/12.396367"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.3390\/rs9111150","article-title":"Exploiting multi-view SAR images for robust target recognition","volume":"9","author":"Ding","year":"2017","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"26880","DOI":"10.1109\/ACCESS.2017.2773363","article-title":"Multi-Aspect-Aware Bidirectional LSTM Networks for Synthetic Aperture Radar Target Recognition","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TGRS.2017.2776357","article-title":"SAR Automatic Target Recognition Based on Multiview Deep Learning Framework","volume":"56","author":"Pei","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1049\/iet-rsn.2014.0296","article-title":"Pseudo-Zernike-based multi-pass automatic target recognition from multi-channel synthetic aperture radar","volume":"9","author":"Clemente","year":"2015","journal-title":"IET Radar Sonar Navigat."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable convolutional networks. Proceedings of the IEEE International Conference On Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference On Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_31","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_32","unstructured":"Trevor Hastie, R.T., and Friedman, J. (2009). The Elements of Statistical Learning; Data Mining, Inference and Prediction, Springer."},{"key":"ref_33","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, MIT Press."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ross, T.D., Worrell, S.W., Velten, V.J., Mossing, J.C., and Bryant, M.L. (1998). Standard SAR ATR evaluation experiments using the MSTAR public release data set. Aerospace\/Defense Sensing and Controls, International Society for Optics and Photonics.","DOI":"10.1117\/12.321859"},{"key":"ref_35","unstructured":"Gonzalez, R.C., Woods, R.E., and Eddins, S.L. (2004). Digital Image Processing Using MATLAB, Pearson Education India."},{"key":"ref_36","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3493\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:55:20Z","timestamp":1760165720000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,2]]},"references-count":36,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13173493"],"URL":"https:\/\/doi.org\/10.3390\/rs13173493","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,2]]}}}