Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2026  > Proofreading [2nd]
GAO Senhao, YANG Xiaqing, SHI Jun, et al. Semi-supervised video synthetic aperture radar shadow tracking based on physics-aware spectral spatial attention and kinematic constraints[J]. Journal of Radars, 2026, 15(2): 1–24. doi: 10.12000/JR25265
Citation: GAO Senhao, YANG Xiaqing, SHI Jun, et al. Semi-supervised video synthetic aperture radar shadow tracking based on physics-aware spectral spatial attention and kinematic constraints[J]. Journal of Radars, 2026, 15(2): 1–24. doi: 10.12000/JR25265
shu

Semi-supervised Video Synthetic Aperture Radar Shadow Tracking Based on Physics-aware Spectral Spatial Attention and Kinematic Constraints

DOI: 10.12000/JR25265 CSTR: 32380.14.JR25265
  • 1.

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

  • 2.

    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (62301118, 62371104), Natural Science Foundation of Sichuan Province (2025ZNSFSC0466), Starting Foundation of the University of Electronic Science and Technology of China (Y030232059002019)
More Information
  • Corresponding author: YANG Xiaqing, yangxiaqing@uestc.edu.cn
  • Received Date: 2025-12-08
  • Rev Recd Date: 2026-03-05
    • Available Online: 2026-03-10
    Abstract
  • SAR faces significant challenges, including strong speckle noise interference, substantial nonrigid target deformation, and a scarcity of supervised training samples. To address these issues, wepropose a semi-supervised tracking method that integrates physics-aware spectral spatial attention and kinematic constraints. For the detection stage, we construct a semi-supervised feature enhancement network based on an improved UniMatchV2 architecture. Specifically, to account for the spectral spatial characteristics of SAR images, a physics-aware hybrid spectral spatial attention module is designed and embedded into the high-level feature space of the decoder. This module utilizes its spectral branch to globally suppress wideband speckle noise and its spatial branch to locally anchor the geometric structure. A dynamic weight generator is introduced to adaptively fuse the dual-domain features, thereby generating high-quality prediction masks under extremely low annotation ratios. For the tracking stage, we propose a spatiotemporal association framework tailored for semi-supervised uncertainty. The framework includes a kinematic prior gate based on a linear Gaussian state-space model to smooth and correct jittery detection edges. Subsequently, a multidimensional cost matrix integrating kinematic residuals and geometric consistency is built to resolve association ambiguities caused by target maneuverability and deformation. Experimental results on measured data from Sandia National Laboratories demonstrate that the proposed method achieves a Multiple Object Tracking Accuracy of 64.19% using only 1/32 of the labeled data, outperforming baseline methods by 6.73%. This effectively addresses the challenge of robustly tracking weak and small shadows in heavily cluttered backgrounds.

     

    • FullText(HTML)
  • loading
    • References(33)
  • [1]
    DING Jinshan, WEN Liwu, ZHONG Chao, et al. Video SAR moving target indication using deep neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(10): 7194–7204. doi: 10.1109/TGRS.2020.2980419.
    [2]
    丁金闪, 仲超, 温利武, 等. 视频合成孔径雷达双域联合运动目标检测方法[J]. 雷达学报, 2022, 11(3): 313–323. doi: 10.12000/JR22036.

    DING Jinshan, ZHONG Chao, WEN Liwu, et al. Joint detection of moving target in video synthetic aperture radar[J]. Journal of Radars, 2022, 11(3): 313–323. doi: 10.12000/JR22036.
    [3]
    RAYNAL A M, BICKEL D L, and DOERRY A W. Stationary and moving target shadow characteristics in synthetic aperture radar[C]. Radar Sensor Technology XVIII, Baltimore, USA, 2014: 90771B. doi: 10.1117/12.2049729.
    [4]
    ENDER J H G, GIERULL C H, and CERUTTI-MAORI D. Improved space-based moving target indication via alternate transmission and receiver switching[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(12): 3960–3974. doi: 10.1109/TGRS.2008.2002266.
    [5]
    TIAN Xiaoqing, LIU Jing, MALLICK M, et al. Simultaneous detection and tracking of moving-target shadows in ViSAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(2): 1182–1199. doi: 10.1109/TGRS.2020.2998782.
    [6]
    ARGENTI F, LAPINI A, BIANCHI T, et al. A tutorial on speckle reduction in synthetic aperture radar images[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(3): 6–35. doi: 10.1109/MGRS.2013.2277512.
    [7]
    SOHN K, ZHANG Zizhao, LI Chunliang, et al. A simple semi-supervised learning framework for object detection[OL]. arXiv preprint arXiv: 2005.04757, 2020. doi: 10.48550/arXiv.2005.04757.
    [8]
    LIU Yencheng, MA C Y, HE Zijian, et al. Unbiased teacher for semi-supervised object detection[C]. 9th International Conference on Learning Representations (ICLR), Virtual, Austria, May 2021. doi: 10.48550/arXiv.2102.09480.
    [9]
    WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[C]. 15th European Conference on Computer Vision – ECCV 2018, Munich, Germany, 2018: 3–19. doi: 10.1007/978-3-030-01234-2_1.
    [10]
    QIN Zequn, ZHANG Pengyi, WU Fei, et al. FcaNet: Frequency channel attention networks[C]. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, Canada, 2021: 763–772. doi: 10.1109/ICCV48922.2021.00082.
    [11]
    ZHANG Peng, CHEN Lifu, LI Zhenhong, et al. Automatic extraction of water and shadow from SAR images based on a multi-resolution dense encoder and decoder network[J]. Sensors, 2019, 19(16): 3576. doi: 10.3390/s19163576.
    [12]
    LI Qiupeng and KONG Yingying. An improved SAR image semantic segmentation Deeplabv3+ network based on the feature post-processing module[J]. Remote Sensing, 2023, 15(8): 2153. doi: 10.3390/rs15082153.
    [13]
    WANG Wei, ZHOU Yuanyuan, XIE Zhikun, et al. Moving target shadow detection using transformer in video sar[C]. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022: 2614–2617. doi: 10.1109/IGARSS46834.2022.9884510.
    [14]
    BAO Jinyu, ZHANG Xiaoling, ZHANG Tianwen, et al. ShadowDeNet: A moving target shadow detection network for video SAR[J]. Remote Sensing, 2022, 14(2): 320. doi: 10.3390/rs14020320.
    [15]
    FANG Hui, LIAO Guisheng, LIU Yongjun, et al. Siam-Sort: Multi-target tracking in video SAR based on tracking by detection and Siamese network[J]. Remote Sensing, 2023, 15(1): 146. doi: 10.3390/rs15010146.
    [16]
    ZHONG Chao, DING Jinshan, and ZHANG Yuhong. Video SAR moving target tracking using joint kernelized correlation filter[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 1481–1493. doi: 10.1109/JSTARS.2022.3146035.
    [17]
    KUHN H W. The Hungarian method for the assignment problem[J]. Naval Research Logistics Quarterly, 1955, 2(1/2): 83–97. doi: 10.1002/nav.3800020109.
    [18]
    KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960, 82(1): 35–45. doi: 10.1115/1.3662552.
    [19]
    BEWLEY A, GE Zongyuan, OTT L, et al. Simple online and realtime tracking[C]. 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, USA, 2016: 3464–3468. doi: 10.1109/ICIP.2016.7533003.
    [20]
    ZHANG Yifu, SUN Peize, JIANG Yi, et al. ByteTrack: Multi-object tracking by associating every detection box[C]. 17th European Conference on Computer Vision – ECCV 2022, Tel Aviv, Israel, 2022: 1–21. doi: 10.1007/978-3-031-20047-2_1.
    [21]
    YANG Lihe, ZHAO Zhen, and ZHAO Hengshuang. UniMatch V2: Pushing the limit of semi-supervised semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(4): 3031–3048. doi: 10.1109/TPAMI.2025.3528453.
    [22]
    GOODMAN J W. Some fundamental properties of speckle[J]. Journal of the Optical Society of America, 1976, 66(11): 1145–1150. doi: 10.1364/JOSA.66.001145.
    [23]
    GOODMAN J W. Speckle Phenomena in Optics: Theory and Applications[M]. Englewood, Roberts & Company Publishers, 2007 : 73-82.
    [24]
    GOODFELLOW I J, SHLENS J, and SZEGEDY C. Explaining and harnessing adversarial examples[C]. International Conference on Learning Representations (ICLR), San Diego, USA, 2015. doi: 10.48550/arXiv.1412.6572.
    [25]
    OQUAB M, DARCET T, MOUTAKANNI T, et al. DINOv2: Learning robust visual features without supervision[OL]. arXiv preprint arXiv: 2304.07193, 2023. doi: 10.48550/arXiv.2304.07193.
    [26]
    BERNARDIN K and STIEFELHAGEN R. Evaluating multiple object tracking performance: The CLEAR MOT metrics[J]. EURASIP Journal on Image and Video Processing, 2008, 2008(1): 246309. doi: 10.1155/2008/246309.
    [27]
    RISTANI E, SOLERA F, ZOU R, et al. Performance measures and a data set for multi-target, multi-camera tracking[C]. Computer Vision – ECCV 2016 Workshops, Amsterdam, The Netherlands, 2016: 17–35. doi: 10.1007/978-3-319-48881-3_2.
    [28]
    WANG Haonan, ZHANG Qixiang, LI Yi, et al. AllSpark: Reborn labeled features from unlabeled in transformer for semi-supervised semantic segmentation[C]. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2024: 3627–3636. doi: 10.1109/CVPR52733.2024.00348.
    [29]
    HOYER L, TAN D J, NAEEM M F, et al. SemiVL: Semi-supervised semantic segmentation with vision-language guidance[C]. 18th European Conference on Computer Vision – ECCV 2024, Milan, Italy, 2025: 257–275. doi: 10.1007/978-3-031-72933-1_15.
    [30]
    ZHANG Yifu, WANG Chunyu, WANG Xinggang, et al. FairMOT: On the fairness of detection and re-identification in multiple object tracking[J]. International Journal of Computer Vision, 2021, 129(11): 3069–3087. doi: 10.1007/s11263-021-01513-4.
    [31]
    CAO Jinkun, PANG Jiangmiao, WENG Xinshuo, et al. Observation-centric SORT: Rethinking SORT for robust multi-object tracking[C]. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 2023: 9686–9696. doi: 10.1109/CVPR52729.2023.00934.
    [32]
    ZHANG Wensi, ZHANG Xiaoling, XU Xiaowo, et al. GNN-JFL: Graph neural network for video SAR shadow tracking with joint motion-appearance feature learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5209117. doi: 10.1109/TGRS.2024.3383870.
    [33]
    SU Mingjie, NI Peishuang, PEI Hao, et al. Graph feature representation for shadow-assisted moving target tracking in video SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2025, 22: 4004905. doi: 10.1109/LGRS.2025.3539748.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(46) PDF downloads(0) Cited by()
    Proportional views
    Related

    京ICP备20021838号-14   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return