{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T23:50:48Z","timestamp":1770421848154,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,11]],"date-time":"2023-03-11T00:00:00Z","timestamp":1678492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Civil Aviation Project","award":["D010206"],"award-info":[{"award-number":["D010206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Shadow detection is a new method for video synthetic aperture radar moving target indication (ViSAR-GMTI). The shadow formed by the target occlusion will reflect its real position, preventing the defocusing or offset of the moving target from making it difficult to identify the target during imaging. To achieve high-precision shadow detection, this paper proposes a video SAR moving target shadow-detection algorithm based on low-rank sparse decomposition combined with trajectory area extraction. Based on the low-rank sparse decomposition (LRSD) model, the algorithm creates a new decomposition framework combined with total variation (TV) regularization and coherence suppression items to improve the decomposition effect, and a global constraint is constructed to suppress interference using feature operators. In addition, it cooperates with the double threshold trajectory segmentation and error trajectory elimination method to further improve the detection performance. Finally, an experiment was carried out based on the video SAR data released by Sandia National Laboratory (SNL); the results prove the effectiveness of the proposed method, and the detection performance of the method is proved by comparative experiments.<\/jats:p>","DOI":"10.3390\/rs15061542","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:03:57Z","timestamp":1678676637000},"page":"1542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A ViSAR Shadow-Detection Algorithm Based on LRSD Combined Trajectory Region Extraction"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhongzheng","family":"Yin","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Mingjie","family":"Zheng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Yuwei","family":"Ren","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kim, S.H., Fan, R., and Dominski, F. (2018, January 23\u201327). ViSAR: A 235 GHz radar for airborne applications. Proceedings of the 2018 IEEE Radar Conference (RadarConf18), Oklahoma City, OK, USA.","DOI":"10.1109\/RADAR.2018.8378797"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2012.2231082","article-title":"Coherent change detection using passive GNSS-based BSAR: Experimental proof of concept","volume":"51","author":"Liu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5212718","DOI":"10.1109\/TGRS.2021.3115491","article-title":"Joint Tracking of Moving Target in Single-Channel Video SAR","volume":"60","author":"Zhong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1109\/TAES.1971.310292","article-title":"Synthetic aperture imaging radar and moving targets","volume":"AES-7","author":"Raney","year":"1971","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"73","DOI":"10.5589\/m04-057","article-title":"A constant false alarm rate (CFAR) detector for RADARSAT-2 along-track interferometry","volume":"31","author":"Chiu","year":"2005","journal-title":"Can. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1109\/TAES.1973.309792","article-title":"Theory of adaptive radar","volume":"AES-9","author":"Brennan","year":"1973","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_7","first-page":"313","article-title":"Joint detection of moving target in video synthetic aperture radar","volume":"11","author":"Ding","year":"2022","journal-title":"J. Radars"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jahangir, M. (2007, January 15\u201318). Moving target detection for synthetic aperture radar via shadow detection. Proceedings of the IET International Conference on Radar Systems, Edinburgh, UK.","DOI":"10.1049\/cp:20070659"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LGRS.2017.2679755","article-title":"Preliminary research of low-RCS moving target detection based on Ka-band video SAR","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"7194","DOI":"10.1109\/TGRS.2020.2980419","article-title":"Video SAR moving target indication using deep neural network","volume":"58","author":"Ding","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","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\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518431"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Liu, Z. (2021, January 11\u201316). Moving Target Shadow Detection Based on Deep Learning in Video SAR. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553299"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bao, J., Zhang, X., Zhang, T., and Xu, X. (2022). ShadowDeNet: A Moving Target Shadow Detection Network for Video SAR. Remote Sens., 14.","DOI":"10.3390\/rs14020320"},{"key":"ref_15","first-page":"2080","article-title":"Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization","volume":"22","author":"Wright","year":"2009","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","first-page":"1","article-title":"Single-channel circular SAR ground moving target detection based on LRSD and adaptive threshold detector","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","unstructured":"(2022, November 10). Available online: https:\/\/www.sandia.gov\/radar\/pathfinder-radar-isr-and-synthetic-aperture-radar-sar-systems\/video\/."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.jvcir.2018.09.009","article-title":"Moving object detection by low rank approximation and l1-TV regularization on RPCA framework","volume":"56","author":"Shijila","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1109\/TCYB.2015.2419737","article-title":"Total variation regularized RPCA for irregularly moving object detection under dynamic background","volume":"46","author":"Cao","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_20","first-page":"1","article-title":"Target-oriented SAR imaging for SCR improvement via deep MF-ADMM-Net","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","first-page":"291","article-title":"Edge detection of images using Sobel operator","volume":"2","author":"Vairalkar","year":"2012","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"key":"ref_22","unstructured":"Mairal, J., Jenatton, R., Obozinski, G., and Bach, F. (2010). Network flow algorithms for structured sparsity. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1561\/2400000003","article-title":"Proximal algorithms","volume":"1","author":"Parikh","year":"2014","journal-title":"Found. Trends Optim."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/TCI.2019.2891389","article-title":"Panoramic robust PCA for foreground\u2013background separation on noisy, free-motion camera video","volume":"5","author":"Moore","year":"2019","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1137\/080738970","article-title":"A singular value thresholding algorithm for matrix completion","volume":"20","author":"Cai","year":"2010","journal-title":"SIAM J. Optim."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.1109\/TIT.2014.2311661","article-title":"OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage","volume":"60","author":"Nadakuditi","year":"2014","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000016","article-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","volume":"3","author":"Boyd","year":"2011","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_28","first-page":"1","article-title":"Robust principal component analysis?","volume":"58","author":"Li","year":"2011","journal-title":"J. ACM"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Li, X., Wright, J., Candes, E., and Ma, Y. (2010, January 13\u201318). Stable principal component pursuit. Proceedings of the 2010 IEEE International Symposium on Information Theory, Austin, TX, USA.","DOI":"10.1109\/ISIT.2010.5513535"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., and Hu, S. (2016, January 27\u201330). Traffic-sign detection and classification in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.232"},{"key":"ref_31","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1542\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:52:49Z","timestamp":1760122369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1542"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,11]]},"references-count":31,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061542"],"URL":"https:\/\/doi.org\/10.3390\/rs15061542","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,11]]}}}