{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:14:14Z","timestamp":1774541654812,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Science Foundation of China","award":["No.61871305"],"award-info":[{"award-number":["No.61871305"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>It is very common to apply convolutional neural networks (CNNs) to synthetic aperture radar (SAR) automatic target recognition (ATR). However, most of the SAR ATR methods using CNN mainly use the image features of SAR images and make little use of the unique electromagnetic scattering characteristics of SAR images. For SAR images, attributed scattering centers (ASCs) reflect the electromagnetic scattering characteristics and the local structures of the target, which are useful for SAR ATR. Therefore, we propose a network to comprehensively use the image features and the features related to ASCs for improving the performance of SAR ATR. There are two branches in the proposed network, one extracts the more discriminative image features from the input SAR image; the other extracts physically meaningful features from the ASC schematic map that reflects the local structure of the target corresponding to each ASC. Finally, the high-level features obtained by the two branches are fused to recognize the target. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove the capability of the SAR ATR method proposed in this letter.<\/jats:p>","DOI":"10.3390\/rs13245121","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T21:32:40Z","timestamp":1639690360000},"page":"5121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Convolutional Neural Network Combined with Attributed Scattering Centers for SAR ATR"],"prefix":"10.3390","volume":"13","author":[{"given":"Yu","family":"Zhou","sequence":"first","affiliation":[{"name":"The National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"The National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Weitong","family":"Xie","sequence":"additional","affiliation":[{"name":"The National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Lu","family":"Li","sequence":"additional","affiliation":[{"name":"The National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"key":"ref_1","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_2","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_3","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Neural Information Processing System (NIPS), Harrahs and Harveys, Lake Tahoe, NV, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1109\/LGRS.2019.2894845","article-title":"Oil Rig Recognition Using Convolutional Neural Network on Sentinel-1 SAR Images","volume":"16","author":"Falqueto","year":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_5","first-page":"667","article-title":"Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition","volume":"99","author":"Huang","year":"2020","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/83.552098","article-title":"Attributed scattering centers for SAR ATR","volume":"6","author":"Potter","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1109\/18.857795","article-title":"Model-based classification of radar images","volume":"46","author":"Chiang","year":"2000","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3805","DOI":"10.1016\/j.patcog.2010.05.033","article-title":"Classifying transformation-variant attributed point patterns","volume":"43","author":"Dungan","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1080\/02564602.2015.1019941","article-title":"An SAR ATR method based on scattering center feature and bipartite graph matching","volume":"32","author":"Tian","year":"2015","journal-title":"IETE Tech. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"25459","DOI":"10.1109\/ACCESS.2019.2900522","article-title":"Data augmentation based on attributed scattering centers to train robust CNN for SAR ATR","volume":"7","author":"Lv","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jiang, C., and Zhou, Y. (2018). Hierarchical Fusion of Convolutional Neural Networks and Attributed Scattering Centers with Application to Robust SAR ATR. Remote. Sens., 10.","DOI":"10.3390\/rs10060819"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1109\/8.785750","article-title":"A parametric model for synthetic aperture radar measurement","volume":"47","author":"Gerry","year":"1999","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4368","DOI":"10.1109\/TSP.2020.3011332","article-title":"Efficient Attributed Scatter Center Extraction Based on Image-domain Sparse Representation","volume":"68","author":"Yang","year":"2020","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Moses, R.L., Potter, L.C., and Gupta, I.J. (2005). Feature Extraction Using Attributed Scattering Center Models for Model-Based Automatic Target Recognition (ATR), The Ohio State University Columbus. Report.","DOI":"10.21236\/ADA444563"},{"key":"ref_15","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1117\/12.321859","article-title":"Standard SAR ATR evaluation experiments using the MSTAR public release data set","volume":"Volume 3370","author":"Ross","year":"1998","journal-title":"Algorithms for Synthetic Aperture Radar Imagery V"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_18","first-page":"769","article-title":"Very deep convolutional networks for large-scale image recognition","volume":"51","author":"Simonyan","year":"2014","journal-title":"Inf. Softw. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1246548","DOI":"10.1155\/2019\/1246548","article-title":"A Novel Convolutional Neural Network Architecture for SAR Target Recognition","volume":"2019","author":"Xie","year":"2019","journal-title":"J. Sens."},{"key":"ref_20","first-page":"2579","article-title":"Visualizing High-Dimensional Data Using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., and Sun, J. (2015, January 7\u201312). Convolutional neural networks at constrained time cost. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"ref_22","unstructured":"Furukawa, H. (2017). Deep learning for target classification from SAR imagery: Data augmentation and translation invariance. arXiv."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1080\/2150704X.2016.1196837","article-title":"SAR ATR based on displacement- and rotation- insensitive CNN","volume":"7","author":"Du","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1016\/S0031-3203(03)00182-1","article-title":"Stochastic models for recognition of occluded targets","volume":"36","author":"Bhanu","year":"2003","journal-title":"Pattern Recognit."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5121\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:50:04Z","timestamp":1760169004000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":25,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245121"],"URL":"https:\/\/doi.org\/10.3390\/rs13245121","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]}}}