Satellite Video Motion Detection Supported by Decision Tree Weak Classification

WU Jiaqi; JIANG Yonghua; SHEN Xin; LI Beibei; PAN Shenlin

doi:10.13203/j.whugis20180094

Volume 44 Issue 8

Aug. 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(8): 1182-1190. > DOI: 10.13203/j.whugis20180094

WU Jiaqi, JIANG Yonghua, SHEN Xin, LI Beibei, PAN Shenlin. Satellite Video Motion Detection Supported by Decision Tree Weak Classification[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1182-1190. DOI: 10.13203/j.whugis20180094

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Satellite Video Motion Detection Supported by Decision Tree Weak Classification

WU Jiaqi^{1, 2, 3,},
JIANG Yonghua^2, ,,
SHEN Xin⁴,
LI Beibei⁵,
PAN Shenlin³

1.
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
2.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
3.
Zhuhai Orbita Aerospace Science & Technology Co Ltd, Zhuhai 519080, China
4.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
5.
Chang Guang Satellite Technology Co Ltd, Changchun 130000, China

Funds:

The National Key Research and Development Program of China 2016YFB0500801

the National Natural Science Foundation of China 91538106

the National Natural Science Foundation of China 41501503

the National Natural Science Foundation of China 41601490

the National Natural Science Foundation of China 41501383

Fund of Zhuhai Introducing Innovative Team ZH0111-0405-160001-P-WC

Local Innovation Team of Guangdong Pearl River Talent Scheme 2017BT01G115

More Information

Author Bio:
WU Jiaqi, PhD candidate, majors in satellite video data processing. E-mail: jiaqiwu@126.com
Corresponding author:
JIANG Yonghua, PhD, associate professor. E-mail: jiangyh@whu.edu.cn
Received Date: September 01, 2018
Published Date: August 04, 2019

Graphical Abstract

Abstract

Abstract

For the satellite video motion detection, two issues are harmful to the subsequent application. One is the local parallax"pseudo-motion", and the other is"Ghost pseudo-motion", which may lead to serious error detection. For these two issues, a decision tree-supported local differentiated updating background subtraction method is proposed. Firstly, the initial background model is established. One other parameter, the updating factor, is added based on the ViBe model. Each pixel in the model has a unique dynamic updating factor. In the next place, we perform the pixel-wise motion detection based on the modified model. The detection results are segmented into targets by using the connected component analysis-labeling method. Afterwards, the decision tree model is established based on the target characteristics. And the targets can be weekly classified into different categories. In the end, the background model samples and the factor parameters are differentiated updated according to the categories results. Using SkySat and Jilin No. 1 video data to do experiments. The experimental results show that our proposed method has favourable effect and performance, with the recall rate of results superior to 0.909. Comparing with the classical ViBe method, the error detection removal rate is superior to 90%.
- decision tree,
- satellite video,
- motion detection,
- "pseudo-motion",
- differentiated updating

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