Moving object detection in video satellite image based on deep learning

Xueyang Zhang; Junhua Xiang

doi:10.1117/12.2296714

15 November 2017 Moving object detection in video satellite image based on deep learning

Xueyang Zhang, Junhua Xiang

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106054H (2017) https://doi.org/10.1117/12.2296714
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

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.

Citation Download Citation

Xueyang Zhang and Junhua Xiang "Moving object detection in video satellite image based on deep learning", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106054H (15 November 2017); https://doi.org/10.1117/12.2296714

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