[1]WANG Xin,TIAN Tian,TIAN Jinwen.Moving target shadow detection in VideoSAR based on background modeling[J].CAAI Transactions on Intelligent Systems,2022,17(1):59-68.[doi:10.11992/tis.202107019]
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Moving target shadow detection in VideoSAR based on background modeling

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 59-68 Column: 学术论文—机器学习 Public date: 2022-01-05
Title:
Moving target shadow detection in VideoSAR based on background modeling
Author(s):
WANG Xin1;  TIAN Tian1; 2;  TIAN Jinwen1; 2
1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;
2. National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Huangzhong University of Science and Technology, Wuhan 430074, China
Keywords:
VideoSAR; moving target detection; convolutional neural network; multiplicative noise; denoising; image registration; single Gaussian model; region growing
CLC:
TP391
DOI:
10.11992/tis.202107019
Abstract:
Aiming at the problem of ground moving target detection based on Video Synthetic Aperture Radar (VideoSAR) data, a VideoSAR moving target shadow detection method based on single Gaussian background model is proposed in this paper, which uses a time-dimensional sliding window to process the video sequence: The RED20 deep neural network model is first used to suppress the speckle noise of VideoSAR image, and then the interframe registration algorithm is applied to quickly register the image sequence of the window. After that, the binary foreground of the last frame of the window is obtained by sequence modeling and background subtraction. Finally, false targets are eliminated by connected region screening and region growing. The proposed approach is validated on the VideoSAR video published by Sandia National Laboratory, and experimental results show that the algorithm can accurately detect the shadow of moving targets.
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