alexa Motion Detection in Satellite Video
ISSN: 2469-4134

Journal of Remote Sensing & GIS
Open Access

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Research Article

Motion Detection in Satellite Video

Aigong Xu1*, Jiaqi Wu1,2,3, Guo Zhang2, Shenlin Pan3, Taoyang Wang4, Yonghua Jang4 and Xin Shen2

1School of Geomatics, Liaoning Technical University, Fuxin 123000, China

2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

3Zhuhai Orbita Control Engineering Co. Ltd., Zhuhai, 519080, China

4School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

*Corresponding Author:
Aigong Xu
School of Geomatics, Institute for Remote Sensing
Science and Application, Liaoning Technical
University, Fuxin, Liaoning 123000, China
Tel: +864183350478
Fax: +864183350478
E-mail: [email protected]

Received date: February 08, 2017; Accepted date: March 27, 2017; Published date: March 29, 2017

Citation: Xu A, Wu J, Zhang G, Pan S, Wang T, et al. (2017) Motion Detection in Satellite Video. J Remote Sensing & GIS 6: 194. doi: 10.4172/2469-4134.1000194

Copyright: © 2017 Xu A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

In view of the problem of satellite video motion detection, a background subtraction method of combining global motion compensation and local dynamic updating is proposed. In the first instance, the improved ViBE model method is used to establish the background model in the middle frame. The background model has one more dynamic update factor. Secondly, the motion model of global scene between frames is estimated by using uniform blocked forward-back LK optical flow, and the global motion compensation is performed. Last but not least, comparison between compensated frame and model, and connected domain analysis are employed to detect and segment the motion objects. Even more, we can correct the update factor of model according to the “pseudo motion” judgment. And then, the model would be updated locally and adaptively. “Target-wise” evaluation recall rate method is proposed which statistic the object entirety but not pixels. We do four experiments using Skysat and JL1H video. The results show that the proposed method perform a favorable effect on “Target-wise” recall rate and the error detection rate is low. The “Target-wise” recall rate is better than 80%. The error detection rate is reduced by at least 10 times, and even more than 160 times, compared with the classical method. The method could be suitable for advanced application and motion analysis in satellite video

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