{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T06:10:30Z","timestamp":1762409430559,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,17]],"date-time":"2018-05-17T00:00:00Z","timestamp":1526515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, a new Region-based Convolutional Neural Networks (RCNN) method is proposed for target recognition in large scene synthetic aperture radar (SAR) images. To locate and recognize the targets in SAR images, there are three steps in the traditional procedure: detection, discrimination, classification and recognition. Each step is supposed to provide optimal processing results for the next step, but this is difficult to implement in real-life applications because of speckle noise and inefficient connection among these procedures. To solve this problem, the RCNN is applied to large scene SAR target recognition, which can detect the objects while recognizing their classes based on its regression method and the sharing network structure. However, size of the input images to RCNN is limited so that the classification could be accomplished, which leads to a problem that RCNN is not able to handle the large scene SAR images directly. Thus, before the RCNN, a fast sliding method is proposed to segment the scene image into sub-images with suitable size and avoid dividing targets into different sub-images. After the RCNN, candidate regions on different slices are predicted. To locate targets on large scene SAR images from these candidate regions on small slices, the Non-maximum Suppression between Regions (NMSR) is proposed, which could find the most proper candidate region among all the overlapped regions. Experiments on    1476 \u00d7 1784    simulated MSTAR images of simple scenes and complex scenes show that the proposed method can recognize all targets with the best accuracy and fastest speed, and outperform the other methods, such as constant false alarm rate (CFAR) detector + support vector machine (SVM), Visual Attention+SVM, and Sliding-RCNN.<\/jats:p>","DOI":"10.3390\/rs10050776","type":"journal-article","created":{"date-parts":[[2018,5,17]],"date-time":"2018-05-17T11:47:45Z","timestamp":1526557665000},"page":"776","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["SAR Target Recognition in Large Scene Images via Region-Based Convolutional Neural Networks"],"prefix":"10.3390","volume":"10","author":[{"given":"Zongyong","family":"Cui","sequence":"first","affiliation":[{"name":"Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Sihang","family":"Dang","sequence":"additional","affiliation":[{"name":"Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0117-9087","authenticated-orcid":false,"given":"Zongjie","family":"Cao","sequence":"additional","affiliation":[{"name":"Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Sifei","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2827-3321","authenticated-orcid":false,"given":"Nengyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/LGRS.2015.2478256","article-title":"High-resolution SAR image classification via deep convolutional autoencoders","volume":"12","author":"Geng","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1109\/JSTARS.2016.2618891","article-title":"Polarimetric SAR Feature Extraction With Neighborhood Preservation-Based Deep Learning","volume":"10","author":"Liu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1117\/1.602495","article-title":"Synthetic aperture radar automatic target recognition with three strategies of learning and representation","volume":"39","author":"Zhao","year":"2000","journal-title":"Opt. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Song, S., Xu, B., and Yang, J. (2016). SAR target recognition via supervised discriminative dictionary learning and sparse representation of the SAR-HOG feature. Remote Sens., 8.","DOI":"10.3390\/rs8080683"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1364\/OL.28.000890","article-title":"MSTAR: A submicrometer absolute metrology system","volume":"28","author":"Lay","year":"2003","journal-title":"Opt. Lett."},{"key":"ref_7","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_8","unstructured":"English, R.A., Rawlinson, S.J., and Sandirasegaram, N.M. (2003, January 12). ATR workbench for automating image analysis. Proceedings of the Algorithms for Synthetic Aperture Radar Imagery X, Orlando, FL, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wei, G., Qi, Q., Jiang, L., and Ping, Z. (2008, January 7\u201311). A New Method of SAR Image Target Recognition based on AdaBoost Algorithm. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779570"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tison, C., Pourthie, N., and Souyris, J.C. (2007, January 23\u201328). Target recognition in SAR images with Support Vector Machines (SVM). Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4422829"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, Y., Han, P., Lu, X., Wu, R., and Huang, J. (2006, January 16\u201319). The Performance Comparison of Adaboost and SVM Applied to SAR ATR. Proceedings of the International Conference on Radar, Shanghai, China.","DOI":"10.1109\/ICR.2006.343515"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Science, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., and Erhan, D. (2016, January 8\u201316). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Morgan, D.A.E. (2015, January 13). Deep convolutional neural networks for ATR from SAR imagery. Proceedings of the Algorithms for Synthetic Aperture Radar Imagery XXII, Baltimore, MA, USA.","DOI":"10.1117\/12.2176558"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Malmgren-Hansen, D., and Nobel-J, M. (2015, January 7\u201310). Convolutional neural networks for SAR image segmentation. Proceedings of the 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ISSPIT.2015.7394333"},{"key":"ref_19","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_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":"227","DOI":"10.1007\/978-3-642-76153-9_28","article-title":"Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition","volume":"8","author":"Bridle","year":"1990","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/TAES.2012.6178042","article-title":"Classification via the Shadow Region in SAR Imagery","volume":"48","author":"Papson","year":"2012","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (arXiv, 2014). Visualizing and Understanding Convolutional Networks, arXiv.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., and Sermanet, P. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lecun, Y., Kavukcuoglu, K., and Farabet, C. (June, January 30). Convolutional networks and applications in vision. Proceedings of the 2010 IEEE International Symposium on Circuits and Systems, Paris, France.","DOI":"10.1109\/ISCAS.2010.5537907"},{"key":"ref_27","unstructured":"Arbib, M.A. (1998). The Handbook of Brain Theory and Neural Networks, MIT Press."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/72.655045","article-title":"Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions","volume":"9","author":"Huang","year":"1998","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by superpositions of a sigmoidal function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math. Control Signals Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hara, K., Saito, D., and Shouno, H. (2015, January 12\u201317). Analysis of function of rectified linear unit used in deep learning. Proceedings of the 2015 International Joint Conference on International Joint Conference on Neural Networks, Killarney, Ireland.","DOI":"10.1109\/IJCNN.2015.7280578"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dong, L., Wei, F., Tan, C., and Tang, D. (2014, January 23\u201325). Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers), Baltimore, MD, USA.","DOI":"10.3115\/v1\/P14-2009"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kang, M., Ji, K., and Leng, X. (2017). Contextual region-based convolutional neural network with multilayer fusion for SAR ship detection. Remote Sens., 9.","DOI":"10.3390\/rs9080860"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"4585","DOI":"10.1109\/TGRS.2013.2282820","article-title":"An improved iterative censoring scheme for CFAR ship detection with SAR imagery","volume":"52","author":"An","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","first-page":"125","article-title":"SAR image target detection in complex environments based on improved visual attention algorithm","volume":"1","author":"Shuo","year":"2014","journal-title":"EURASIP J. Wirel. Commun. Netw."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/776\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:04:42Z","timestamp":1760195082000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/776"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,17]]},"references-count":35,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["rs10050776"],"URL":"https:\/\/doi.org\/10.3390\/rs10050776","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,5,17]]}}}