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Proceedings Paper

Moving object detection in video satellite image based on deep learning
Author(s): Xueyang Zhang; Junhua Xiang
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Paper Abstract

Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.

Paper Details

Date Published: 15 November 2017
PDF: 8 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106054H (15 November 2017); doi: 10.1117/12.2296714
Show Author Affiliations
Xueyang Zhang, National Univ. of Defense Technology (China)
Junhua Xiang, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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