{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T13:51:08Z","timestamp":1770817868773,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T00:00:00Z","timestamp":1617926400000},"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, 61901090 and 61671117"],"award-info":[{"award-number":["61901091, 61901090 and 61671117"]}],"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>Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.<\/jats:p>","DOI":"10.3390\/rs13081455","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T05:52:00Z","timestamp":1618206720000},"page":"1455","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Multiview Deep Feature Learning Network for SAR Automatic 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":"Department of Electrical Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}]},{"given":"Weibo","family":"Huo","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}]},{"given":"Chenwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}]},{"given":"Yulin","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}]},{"given":"Yin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}]},{"given":"Junjie","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}]},{"given":"Jianyu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,9]]},"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. Encyclopedia of Aerospace Engineering, Wiley.","DOI":"10.1002\/9780470686652.eae281"},{"key":"ref_7","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_8","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_9","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_10","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_11","doi-asserted-by":"crossref","first-page":"71598","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_12","first-page":"25","article-title":"Discriminating targets from clutter","volume":"6","author":"Kreithen","year":"1993","journal-title":"Linc. Lab. J."},{"key":"ref_13","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_14","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_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"6192","DOI":"10.1109\/TAP.2014.2360700","article-title":"A Forward Approach to Establish Parametric Scattering Center Models for Known Complex Radar Targets Applied to SAR ATR","volume":"62","author":"He","year":"2014","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Zhang, H., Nasrabadi, N.M., Huang, T.S., and Zhang, Y. (2011, January 4). Joint sparse representation based automatic target recognition in SAR images. Proceedings of the SPIE 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII, Orlando, FL, USA.","DOI":"10.1117\/12.883665"},{"key":"ref_20","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_21","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. Lett."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"7829","DOI":"10.1109\/TGRS.2020.2984577","article-title":"Fusion of Sparse Model Based on Randomly Erased Image for SAR Occluded Target Recognition","volume":"58","author":"He","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"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":"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_26","first-page":"332","article-title":"ATR performance using enhanced resolution SAR","volume":"Volume 2757","author":"Novak","year":"1996","journal-title":"Algorithms for Synthetic Aperture Radar Imagery III"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"62340U","DOI":"10.1117\/12.664117","article-title":"Bistatic SAR ATR using PCA-based features","volume":"Volume 6234","author":"Mishra","year":"2006","journal-title":"Automatic Target Recognition XVI"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2120","DOI":"10.1109\/LGRS.2014.2321164","article-title":"Sample Discriminant Analysis for SAR ATR","volume":"11","author":"Liu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","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_30","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_31","unstructured":"Ikeuchi, K., Wheeler, M.D., Yamazaki, T., and Shakunaga, T. (1996). Model-based SAR ATR system. Aerospace\/Defense Sensing and Controls, International Society for Optics and Photonics."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/72.554195","article-title":"Face recognition: A convolutional neural-network approach","volume":"8","author":"Lawrence","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_34","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_35","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_36","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_37","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1117\/12.487171","article-title":"Analysis of multiple-view Bayesian classification for SAR ATR","volume":"Volume 5095","author":"Brown","year":"2003","journal-title":"Algorithms for Synthetic Aperture Radar Imagery X"},{"key":"ref_38","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 Navig."},{"key":"ref_39","unstructured":"Vespe, M., Baker, C.J., and Griffiths, H.D. (2006, January 24\u201327). Aspect dependent drivers for multi-perspective target classification. Proceedings of the 2006 IEEE Conference on Radar, Verona, NY, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"267","DOI":"10.2528\/PIER12100304","article-title":"Target recognition for multi-aspect SAR images with fusion strategies","volume":"134","author":"Huan","year":"2013","journal-title":"Prog. Electromagn. Res."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Ding, B., and Wen, G. (2017). Exploiting multi-view SAR images for robust target recognition. Remote Sens., 9.","DOI":"10.3390\/rs9111150"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"9223","DOI":"10.1109\/TGRS.2019.2925636","article-title":"Sequence SAR Image Classification Based on Bidirectional Convolution-Recurrent Network","volume":"57","author":"Bai","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","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_46","first-page":"11","article-title":"Performance of a high-resolution polarimetric SAR automatic target recognition system","volume":"6","author":"Novak","year":"1993","journal-title":"Linc. Lab. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/36.789617","article-title":"Spotlight SAR data processing using the frequency scaling algorithm","volume":"37","author":"Mittermayer","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lin, Y., Hong, W., Tan, W., Wang, Y., and Xiang, M. (2012, January 22\u201327). Airborne circular SAR imaging: Results at P-band. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352051"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1117\/12.321826","article-title":"Pose estimation in SAR using an information theoretic criterion","volume":"Volume 3370","author":"Principe","year":"1998","journal-title":"Algorithms for Synthetic Aperture Radar Imagery V"},{"key":"ref_50","unstructured":"Gonzalez, R.C., Woods, R.E., and Eddins, S.L. (2004). Digital Image Processing Using MATLAB, Pearson Education India."},{"key":"ref_51","unstructured":"Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2017). Deep learning for precipitation nowcasting: A benchmark and a new model. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D Convolutional Neural Networks for Human Action Recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_54","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_55","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_56","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\/8\/1455\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:27:37Z","timestamp":1760365657000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1455"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,9]]},"references-count":56,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081455"],"URL":"https:\/\/doi.org\/10.3390\/rs13081455","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,9]]}}}