{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T01:30:44Z","timestamp":1780104644952,"version":"3.54.0"},"reference-count":57,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,20]],"date-time":"2020-09-20T00:00:00Z","timestamp":1600560000000},"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":["61671113,61501098,61571099"],"award-info":[{"award-number":["61671113,61501098,61571099"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China under Grant","award":["2017YFB0502700"],"award-info":[{"award-number":["2017YFB0502700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target\u2019s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.<\/jats:p>","DOI":"10.3390\/rs12183083","type":"journal-article","created":{"date-parts":[[2020,9,20]],"date-time":"2020-09-20T21:20:28Z","timestamp":1600636828000},"page":"3083","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR"],"prefix":"10.3390","volume":"12","author":[{"given":"Xiaqing","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunjun","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1007\/s11432-012-4633-4","article-title":"Sparse microwave imaging: Principles and applications","volume":"55","author":"Zhang","year":"2012","journal-title":"Sci. China Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.culher.2015.05.003","article-title":"An overview of satellite synthetic aperture radar remote sensing in archaeology: From site detection to monitoring","volume":"23","author":"Chen","year":"2017","journal-title":"J. Cult. Herit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/LGRS.2008.2008825","article-title":"Unambiguous reconstruction and high-resolution imaging for multiple-channel SAR and airborne experiment results","volume":"6","author":"Jing","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ji, P., Xing, S., Dai, D., and Pang, B. (2020). Deceptive Targets Generation Simulation Against Multichannel SAR. Electronics, 9.","DOI":"10.3390\/electronics9040597"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kim, S., Yu, J., Jeon, S.Y., Dewantari, A., and Ka, M.H. (2017). Signal processing for a multiple-input, multiple-output (MIMO) video synthetic aperture radar (SAR) with beat frequency division frequency-modulated continuous wave (FMCW). Remote Sens., 9.","DOI":"10.3390\/rs9050491"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"179842","DOI":"10.1109\/ACCESS.2019.2955296","article-title":"3D SAR Image Background Separation Based on Seeded Region Growing","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Garcia-Fernandez, M., Alvarez-Lopez, Y., and Las Heras, F. (2019). Autonomous airborne 3d sar imaging system for subsurface sensing: Uwb-gpr on board a uav for landmine and ied detection. Remote Sens., 11.","DOI":"10.3390\/rs11202357"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5399","DOI":"10.1080\/01431161.2020.1731932","article-title":"Three-dimensional ISAR image reconstruction technique based on radar network","volume":"41","author":"Liu","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"178710","DOI":"10.1109\/ACCESS.2019.2959128","article-title":"A Fast Sparse Recovery Algorithm via Resolution Approximation for LASAR 3D Imaging","volume":"7","author":"Tian","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pu, W., Wang, X., Wu, J., Huang, Y., and Yang, J. (2020). Video SAR Imaging Based on Low-Rank Tensor Recovery. IEEE Trans. Neural Networks Learn. Syst.","DOI":"10.1109\/TNNLS.2020.2978017"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Esposito, C., Natale, A., Palmese, G., Berardino, P., Lanari, R., and Perna, S. (2020). On the Capabilities of the Italian Airborne FMCW AXIS InSAR System. Remote Sens., 12.","DOI":"10.3390\/rs12030539"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Filippo, B. (2019). COSMO-SkyMed staring spotlight SAR data for micro-motion and inclination angle estimation of ships by pixel tracking and convex optimization. Remote Sens., 11.","DOI":"10.3390\/rs11070766"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bao, J., Zhang, X., Tang, X., Wei, S., and Shi, J. (August, January 28). Moving Target Detection and Motion Parameter Estimation VIA Dual-Beam Interferometric SAR. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900508"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1109\/LGRS.2016.2584083","article-title":"Moving Target Detection via Efficient ATI-GoDec Approach for Multichannel SAR System","volume":"13","author":"Li","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bollian, T., Osmanoglu, B., Rincon, R., Lee, S.K., and Fatoyinbo, T. (2019). Adaptive antenna pattern notching of interference in synthetic aperture radar data using digital beamforming. Remote Sens., 11.","DOI":"10.3390\/rs11111346"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, L., Yu, H., and Lan, Y. (2020). Deep Convolutional Neural Network-Based Robust Phase Gradient Estimation for Two-Dimensional Phase Unwrapping Using SAR Interferograms. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2020.2965918"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gao, Y., Zhang, S., Li, T., Chen, Q., Zhang, X., and Li, S. (2019). Refined two-stage programming approach of phase unwrapping for multi-baseline SAR interferograms using the unscented Kalman filter. Remote Sens., 11.","DOI":"10.3390\/rs11020199"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/7.745691","article-title":"SAR imaging of moving targets","volume":"35","author":"Perry","year":"1999","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_19","first-page":"18","article-title":"A keystone transform without interpolation for SAR ground moving-target imaging","volume":"4","author":"Zhu","year":"2007","journal-title":"J. Appl. Remote Sens."},{"key":"ref_20","unstructured":"Wells, L., Sorensen, K., Doerry, A., and Remund, B. (2003, January 8\u201315). Developments in SAR and IFSAR systems and technologies at Sandia National Laboratories. Proceedings of the 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652), Big Sky, MT, USA."},{"key":"ref_21","first-page":"90771B","article-title":"Stationary and moving target shadow characteristics in synthetic aperture radar","volume":"9077","author":"Raynal","year":"2014","journal-title":"Radar Sens. Technol. XVIII"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Miller, J., Bishop, E., Doerry, A., and Raynal, A.M. (2015, January 23). Impact of ground mover motion and windowing on stationary and moving shadows in synthetic aperture radar imagery. Proceedings of the SPIE 2015 Defense & Security Symposium, Algorithms for Synthetic Aperture Radar Imagery XXII, Baltimore, MD, USA.","DOI":"10.1117\/12.2179173"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1109\/TGRS.2017.2754098","article-title":"An extended moving target detection approach for high-resolution multichannel SAR-GMTI systems based on enhanced shadow-aided decision","volume":"56","author":"Xu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Mao, X., Yan, H., Zhu, D., and Hu, X. (2017, January 23\u201328). A novel approach to moving targets shadow detection in VideoSAR imagery sequence. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127026"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"42418","DOI":"10.1109\/ACCESS.2019.2907146","article-title":"Moving Target Shadow Detection and Global Background Reconstruction for VideoSAR Based on Single-Frame Imagery","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3014","DOI":"10.1109\/JSTARS.2019.2919382","article-title":"Geospatial Object Detection via Deconvolutional Region Proposal Network","volume":"12","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wei, S., Su, H., Ming, J., Wang, C., Yan, M., Kumar, D., Shi, J., and Zhang, X. (2020). Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet. Remote Sens., 12.","DOI":"10.3390\/rs12010167"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Su, H., Wei, S., Liu, S., Liang, J., Wang, C., Shi, J., and Zhang, X. (2020). HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12060989"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Chen, T., Tian, J., Zhou, Z., Wang, C., Yang, X., and Shi, J. (2019, January 26\u201329). Complex Background SAR Target Recognition Based on Convolution Neural Network. Proceedings of the 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Xiamen, China.","DOI":"10.1109\/APSAR46974.2019.9048279"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7177","DOI":"10.1109\/TGRS.2017.2743222","article-title":"Complex-valued convolutional neural network and its application in polarimetric SAR image classification","volume":"55","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, C., Shi, J., Zhou, Y., Yang, X., Zhou, Z., Wei, S., and Zhang, X. (2020). Semisupervised Learning-Based SAR ATR via Self-Consistent Augmentation. IEEE Trans. Geosci. Remote Sens., 1\u201312.","DOI":"10.1109\/TGRS.2020.2993804"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhou, Y., Wang, C., and Shi, J. (August, January 28). SAR Images Enhancement Via Deep Multi-Scale Encoder-Decoder Neural Network. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898690"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Shi, J., Yang, X., Wang, C., Kumar, D., Wei, S., and Zhang, X. (2019). Deep multi-scale recurrent network for synthetic aperture radar images despeckling. Remote Sens., 11.","DOI":"10.3390\/rs11212462"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, S., Li, H., and Xu, Z. (2018, January 22\u201327). Shadow Tracking of Moving Target Based on CNN for Video SAR System. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518431"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ding, J., Wen, L., Zhong, C., and Loffeld, O. (2020). Video SAR Moving Target Indication Using Deep Neural Network. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2020.2980419"},{"key":"ref_37","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1109\/JSTARS.2013.2238891","article-title":"Streaming BP for non-linear motion compensation SAR imaging based on GPU","volume":"6","author":"Jun","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tang, X., Zhang, X., Shi, J., Wei, S., and Tian, B. (2019). Ground Moving Target 2-D Velocity Estimation and Refocusing for Multichannel Maneuvering SAR with Fixed Acceleration. Sensors, 19.","DOI":"10.3390\/s19173695"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1109\/TGRS.2018.2870299","article-title":"Background-Free Ground Moving Target Imaging for Multi-PRF Airborne SAR","volume":"57","author":"Jin","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"026516","DOI":"10.1117\/1.JRS.13.026516","article-title":"Ground slowly moving target detection and velocity estimation via high-speed platform dual-beam synthetic aperture radar","volume":"13","author":"Tang","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/TGRS.2010.2053848","article-title":"Ground moving targets imaging algorithm for synthetic aperture radar","volume":"49","author":"Zhu","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1109\/TGRS.2016.2622712","article-title":"Image-based target detection and radial velocity estimation methods for multichannel SAR-GMTI","volume":"55","author":"Suwa","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2860","DOI":"10.1109\/TAES.2011.6034669","article-title":"An autoregressive formulation for SAR backprojection imaging","volume":"47","author":"Moses","year":"2011","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_45","first-page":"180","article-title":"Recursive SAR imaging","volume":"Volume 6970","author":"Zelnio","year":"2008","journal-title":"Algorithms for Synthetic Aperture Radar Imagery XV"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1109\/TAES.2016.150581","article-title":"Processing video-SAR data with the fast backprojection method","volume":"52","author":"Song","year":"2016","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1109\/TTHZ.2018.2872412","article-title":"Unified Coordinate System Algorithm for Terahertz Video-SAR Image Formation","volume":"8","author":"Zuo","year":"2018","journal-title":"IEEE Trans. Terahertz Sci. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M. (2010, January 13\u201318). Visual object tracking using adaptive correlation filters. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-speed tracking with kernelized correlation filters","volume":"37","author":"Henriques","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H. (2016). Fully-convolutional siamese networks for object tracking. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., and Yu, N. (2017, January 22\u201329). Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.518"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhai, M., Chen, L., Mori, G., and Javan Roshtkhari, M. (2018, January 8\u201314). Deep learning of appearance models for online object tracking. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-11018-5_57"},{"key":"ref_53","first-page":"61","article-title":"Simulation of RCS of Ship by Using Feko and Hypermesh","volume":"5","year":"2008","journal-title":"Equip. Environ. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1109\/LRA.2018.2792152","article-title":"Re 3: Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects","volume":"3","author":"Farhadi","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"\u010cehovin, L., Kristan, M., and Leonardis, A. (2014, January 24\u201326). Is my new tracker really better than yours?. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, USA.","DOI":"10.1109\/WACV.2014.6836055"},{"key":"ref_56","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_57","unstructured":"Chen, Z., Hong, Z., and Tao, D. (2015). An experimental survey on correlation filter-based tracking. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3083\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:11:51Z","timestamp":1760177511000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3083"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,20]]},"references-count":57,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12183083"],"URL":"https:\/\/doi.org\/10.3390\/rs12183083","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,20]]}}}