一种VideoSAR动目标阴影检测方法

张营; 朱岱寅; 俞翔; 毛新华

doi:10.11999/JEIT161394

一种VideoSAR动目标阴影检测方法

doi: 10.11999/JEIT161394

1.
(南京航空航天大学电子信息工程学院南京 211106) ②(南京工程学院计算机工程学院南京 211167)

基金项目:

国家自然科学基金(61671240)，江苏省自然科学基金青年基金(BK20150730)，中央高校基本科研业务费(NZ2016105)，南京航空航天大学研究生创新基地(实验室)开放基金资助项目(kfjj20170401)

计量
- 文章访问数: 1940
- HTML全文浏览量: 259
- PDF下载量: 377
- 被引次数: 0
出版历程
- 收稿日期: 2016-12-29
- 修回日期: 2017-04-24
- 刊出日期: 2017-09-19

Approach to Moving Targets Shadow Detection for VideoSAR

1.
(College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Funds:

The National Natural Science Foundation of China (61671240), The Natural Science Foundation of Jiangsu Province for Youths (BK20150730), The Fundamental Research Funds for the Central Universities (NZ2016105), The Foundation of Graduate Innovation Center in NUAA (kfjj20170401)

摘要

摘要: 在高帧率的视频合成孔径雷达(VideoSAR)成像模式获得的图像序列中，多普勒频移使运动目标在实际位置留下阴影，且相邻帧图像具有很强相关性。该文针对上述现象提出一种VideoSAR图像中动目标阴影检测的方法。首先，对每帧图像通过结合尺度不变特征变换(SIFT)和随机抽样一致性(RANSAC)算法实现配准并进行背景补偿，再采用CattePM模型抑制相干斑噪声。然后通过Tsallis灰度熵的最大化阈值分割方法自动分离目标和背景，获得二值图像。最后，对相邻多帧图像背景建模并差分，再结合三帧间差分法提取动目标阴影，结果标记至原帧图像相应位置。基于美国Sandia实验室公布的VideoSAR成像片段，实现了多个移动车辆的检测，验证了所提算法的有效性。
- 视频合成孔径雷达 /
- 动目标阴影检测 /
- 图像配准 /
- 阈值分割 /
- 帧间差分
Abstract: In the image sequence obtained by the high frame rate Video Synthetic Aperture Radar (VideoSAR) mode, the Doppler shift results in some shadows of the moving targets in their actual position, and a strong correlation exists between adjacent frames. Based on the above rationale, this paper proposes an approach to detecting moving targets shadow in VideoSAR imagery. First, the Scale-Invariant Feature Transform (SIFT) with RANdom SAmple Consensus (RANSAC) registration algorithm is used to compensate for the change of background of each frame, and the CattePM model is employed to suppress the speckle noise effectively. Then, in order to separate the targets and the background and generate binary images automatically, a threshold segmentation algorithm, called maximizing the Tsallis entropy, is applied. Finally, shadow detection is accomplished by the background difference with three frame difference method, and the detection results are marked on the corresponding position in the original frame. Experimental results utilizing the VideoSAR imaging fragment published by Sandia National Laboratories show that multiple moving vehicles are detected effectively, hence the validity of the approach is demonstrated.
- Video Synthetic Aperture Radar (VideoSAR) /
- Moving target shadow detection /
- Image registration /
- Threshold segmentation /
- Frame difference

HTML全文

参考文献(20)

WELLS L, SORENSEN K, DOERRY A, et al. Developments in SAR and IFSAR systems and technologies at Sandia national laboratories[C]. 2003 IEEE Aerospace Conference Proceedings, Big Sky, Montana, USA, 2003, Vol. 2: 1085-1095. doi: 10.1109/AERO.2003.1235522.

MILLER J, BISHOP E, and DOERRY A. An application of backprojection for video SAR image formation exploiting a subaperature circular shift register[C]. Proceedings of SPIE Defense, Security, and Sensing, Baltimore, Maryland, USA, 2013: 874609.

LIU B, ZHANG X P, TANG K, et al. Spaceborne video-SAR moving target surveillance system[C]. 2016 IEEE International Geoscience and Remote Sensing Sympsium, Beijing, China, 2016: 2348-2351. doi: 10.1109/IGARSS.2016. 7729606.

ZHAO S, CHEN J, YANG W, et al. Image formation method for spaceborne video SAR[C]. 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar, Marina Bay Sands, Singapore, 2015: 148-151. doi: 10.1109/APSAR.2015. 7306176.

DAMINI A, MANTLE V, and DAVIDSON G. A new approach to coherent change detection in VideoSAR imagery using stack averaged coherence[C]. 2013 IEEE Radar Conference, Ottawa, Ontario, Canada, 2013: 1-5. doi: 10.1109/RADAR.2013.6586152.

HAWLEY R W and GARBER W L. Aperture weighting technique for video synthetic aperture radar[C]. Proceedings of SPIE Defense, Security, and Sensing, Orlando, Florida, USA, 2011: 805107.

RAYNAL A M, BICKEL D L, and DOERRY A W. Stationary and moving target shadow characteristics in synthetic aperture radar[C]. Proceedings of SPIE Defense, Security, and Sensing, Baltimore, Maryland, USA, 2014: 90771B.

JAHANGIR M. Moving target detection for synthetic aperture radar via shadow detection[C]. 2007 IET International Conference on Radar Systems, Edinburgh, UK, 2007: 1-5. doi: 10.1049/cp:20070659.

史洪印, 侯志涛, 郭秀花, 等. 基于阴影检测的单幅高分辨SAR 图像动目标检测方法[J]. 信号处理, 2012, 28(12): 1706-1713. doi: 10.3969/j.issn.1003-0530.2012.12.011.

SHI Hongyin, HOU Zhitao, GUO Xiuhua, et al. Moving targets indication method in single high resolution SAR imagery based on shadow decetion[J]. Journal of Signal Processing, 2012, 28(12): 1706-1713. doi: 10.3969/j.issn.1003- 0530.2012.12.011.

史洪印, 张诺. 基于稀疏表示和道路辅助的单幅SAR图像运动目标检测方法[J]. 电子学报, 2015, 43(3): 431-439. doi: 10.3969/j.issn.0372-2112.2015.03.003.

SHI Hongyin and ZHANG Nuo. Moving targets indication method in single SAR imagery based on sparse representation and road information[J]. Acta Electronica Sinica, 2015, 43(3): 431-439. doi: 10.3969/j.issn.0372-2112.2015.03.003.

张小强, 熊博莅, 匡纲要. 一种基于变化检测技术的 SAR 图像舰船目标鉴别方法[J]. 电子与信息学报, 2015, 37(1): 63-70. doi: 10.11999/JEIT140143.

ZHANG Xiaoqiang, XIONG Boli, and KUANG Gangyao. A ship target discrimination method based on change detection in SAR imagery[J]. Journal of Electronics Information Technology, 2015, 37(1): 63-70. doi: 10.11999/JEIT140143.

LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

申浩, 李书晓, 申意萍, 等. 航拍视频帧间快速配准算法[J]. 航空学报, 2013, 36(6): 1405-1413. doi: 10.7527/S1000-6893. 2013.0239.

SHEN Hao, LI Shuxiao, SHEN Yiping, et al. Fast interframe registration method in aerial videos[J]. Acta Aeronautica et Astronautica Sinica, 2013, 36(6): 1405-1413. doi: 10.7527/ S1000-6893.2013.0239.

CATTE F, LIONS P, MOREL J, et al. Image selective smoothing and edge detection by nonlinear diffusion[J]. SIAM Journal on Numerical Analysis, 1992, 29(3): 182-193.

ALBUQUERQUE M P, ESQUEF I A, and GESUALDI MELLO A R. Image thresholding using Tsallis entropy[J]. Pattern Recognition Letters, 2004, 25(9): 1059-1065. doi: 10.1016/j.patrec.2004.03.003.

TSAI D M and LAI S C. Independent component analysis- based background subtraction for indoor surveillance[J]. IEEE Transactions on Image Processing, 2009, 18(1): 158-167. doi: 10.1109/TIP.2008.2007558.

施引文献

资源附件(0)

访问统计