Approach to Moving Targets Shadow Detection for VideoSAR

ZHANG Ying; ZHU Daiyin; YU Xiang; MAO Xinhua

doi:10.11999/JEIT161394

Volume 39 Issue 9

Sep. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2197-2202

ZHANG Ying, ZHU Daiyin, YU Xiang, MAO Xinhua. Approach to Moving Targets Shadow Detection for VideoSAR[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2197-2202. doi: 10.11999/JEIT161394

Citation:

ZHANG Ying, ZHU Daiyin, YU Xiang, MAO Xinhua. Approach to Moving Targets Shadow Detection for VideoSAR[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2197-2202. doi: 10.11999/JEIT161394

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Approach to Moving Targets Shadow Detection for VideoSAR

doi: 10.11999/JEIT161394

1.
(College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)
2.

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)

Received Date: 2016-12-29
Rev Recd Date: 2017-04-24
Publish Date: 2017-09-19

Abstract

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

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References(20)

References

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