VideoSAR moving target detection and tracking algorithm based on deep learning
QIU Lei1,2(), ZHANG Xuezhi2, HAO Dawei2
1. Ordnance Enginnering College, Naval University of Engineering, Wuhan 430033, China 2. Ordnance NCO Academy, Army Engineering University of PLA, Wuhan 430075, China
The video synthetic aperture radar (VideoSAR) technology is widely used in military reconnaissance, geological exploration, and disaster prediction, among other fields. Owing to multiple interference factors in SAR videos, such as speckle noise, specular reflection, and overlay effect, moving targets are easily mixed with background or other targets. Therefore, this study proposed an effective VideoSAR target detection and tracking algorithm. Firstly, several features of VideoSAR were extracted to construct multichannel feature maps. Then, deeper features were extracted using the improved lightweight EfficientDet network, thus improving the accuracy of SAR target detection while considering algorithm efficiency. Finally, the trajectory association strategy based on bounding boxes was employed to associate the same target in VideoSAR. The experimental results show that the method proposed in this study is effective for SAR shadow target detection and tracking.
Wells L, Sorensen K, Doerry A, et al. Developments in SAR and IFSAR systems and technologies at Sandia National Laboratories[C]// Aerospace Conference.IEEE, 2005.
[2]
Wang D, Zhu D Y, Liu R. Video SAR high-speed processing technology based on FPGA[C]// 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC).IEEE, 2019, 1:1-4.
[3]
丁金闪. 视频SAR成像与动目标阴影检测技术[J]. 雷达学报, 2020, 9(2):14.
Ding J S. Focusing algorithms and moving target detection based on video SAR[J]. Journal of Radars, 2020, 9(2):14.
Lin X, Hong J, Sun X, et al. New CFAR ship detection algorithm based on a daptive back-ground clutter model in wide swath SAR images[J]. Remote Sensing Technology and Application, 2014, 29(1):75-81.
[5]
Huang Y, Liu F. Detecting cars in VHR SAR images via semantic CFAR algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(6):801-805.
doi: 10.1109/LGRS.2016.2546309
[6]
Kaplan L M. Improved SAR target detection via extended fractal features[J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(2) :436-451.
doi: 10.1109/7.937460
Liu D, Zhang G. Special target detection of the SAR image via exponential wavelet fractal[J]. Journal of Xidian University, 2010, 37(2):366-373.
[8]
Sommer L, Schmidt N, Schumann A, et al. Search area reduction Fast-RCNN for fast vehicle detection in large aerial imagery[C]// 25th IEEE International Conference on Image Processing (ICIP).IEEE, 2018:3054-3058.
[9]
Li J W, Qu C W, Peng S J. Ship detection in SAR images based on an improved Faster R-CNN[C]// 2017 SAR in Big Data Era: Models,Methods and Applications (BIGSARDATA).IEEE, 2017.
[10]
Jiang S, Zhu M, He Y, et al. Ship detection with SAR based on YOLO[C]// IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium.IEEE, 2020.
[11]
Kang M, Ji K, Leng X, et al. Contextual region-based convolutional neural network with multilayer fusion for SAR ship detection[J]. Remote Sensing, 2017, 9(8):860.
doi: 10.3390/rs9080860
[12]
Wang R, Xu F, Pei J, et al. An improved Faster R-CNN based on MSER decision criterion for SAR image ship detection in harbor[C]// IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium.IEEE, 2019:1322-1325.
[13]
An Q, Pan Z X, Liu L, et al. DRBox-v2:An improved detector with rotatable boxes for target detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11) :8333-8349.
doi: 10.1109/TGRS.36
Zhang Y, Zhu W G, Wu X. Target detection based on fully convolutional neural network for SAR images[J]. Telecommunication Engineering, 2018, 58(11) :1244-1251.
[15]
Wang H, Chen Z S, Zheng S C. Preliminary research of low-RCS moving target detection based on Ka-band video SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6):811-815.
doi: 10.1109/LGRS.2017.2679755
[16]
Zhang Y, Yang S Y, Li H B, et al. Shadow tracking of moving target based on CNN for video SAR system[C]// IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium.IEEE, 2018:4399-4402.
[17]
Xu Z H, Zhang Y, Li H B, et al. A new shadow tracking method to locate the moving target in SAR imagery based on KCF[C]// International Conference in Communications,Signal Processing,and Systems.Springer, 2017:2661-2669.
[18]
Liang Z H, Liang C J, Zhang Y, et al. Tracking of moving target based on SiamMask for video SAR system[C]// 2019 IEEE International Conference on Signal,Information and Data Processing.IEEE, 2019:1-4.
[19]
Zhang Y, Zhu D Y, Yu X, et al. Approach to moving targets shadow detection for video SAR[J]. Journal of Electronics and Information Technology, 39(9):2197-2202
[20]
Henke D, Magnard C, Frioud M, et al. Moving-target tracking in single-channel wide-beam SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(11):4735-4747.
doi: 10.1109/TGRS.2012.2191561
[21]
Henke D, Dominguez E M, Small D, et al. Moving target tracking in single-and multichannel SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(6):3146-3159.
doi: 10.1109/TGRS.2014.2369060
[22]
Ding J, Wen L, Zhong C, 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.36
[23]
Zhao B, Han Y, Wang H, et al. Robust shadow tracking for video SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(5):821-825.
doi: 10.1109/LGRS.2020.2988165
He Z H, Chen X, Yu C R, et al. A robust moving target shadow detection and tracking method for VideoSAR[J]. Journal of Electronics and Information Technology, 2022, 44:1-9.
Liu Y J, Qi X Y. Moving ship tracking method based on long time interval sequential SAR images[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(5):7.
[26]
胡瑶. 基于阴影的SAR多目标跟踪方法研究[D]. 成都: 电子科技大学, 2021.
Hu Y. Research on shadow-based SAR multi-target tracking method[D]. Chengdu: University of Electronic Science and Technology of China, 2021.
[27]
Lin T Y, Dollar P, Girshick R, et al. Feature pyramid networks for object detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE, 2017:936-944.
[28]
Tan M, Pang R, Le Q V. EfficientDet:Scalable and efficient object detection[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE, 2020:10778-10787.
2023, Vol. 35 
), 张学志2, 郝大为2
