Research on Video SAR Moving Target Detection Algorithm Based on Improved Faster Region-based CNN

He YAN; Jia HUANG; Ruian LI; Xudong WANG; Jingdong ZHANG; Daiyin ZHU

doi:10.11999/JEIT200630

Volume 43 Issue 3

Mar. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(3): 615-622

He YAN, Jia HUANG, Ruian LI, Xudong WANG, Jingdong ZHANG, Daiyin ZHU. Research on Video SAR Moving Target Detection Algorithm Based on Improved Faster Region-based CNN[J]. Journal of Electronics & Information Technology, 2021, 43(3): 615-622. doi: 10.11999/JEIT200630

Citation:

He YAN, Jia HUANG, Ruian LI, Xudong WANG, Jingdong ZHANG, Daiyin ZHU. Research on Video SAR Moving Target Detection Algorithm Based on Improved Faster Region-based CNN[J]. Journal of Electronics & Information Technology, 2021, 43(3): 615-622. doi: 10.11999/JEIT200630

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Research on Video SAR Moving Target Detection Algorithm Based on Improved Faster Region-based CNN

doi: 10.11999/JEIT200630

College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China

Funds: The Special Fund for Basic Scientific Research Business Expenses of Central Universities (NS2019024)

Received Date: 2020-07-29
Rev Recd Date: 2020-12-14

Available Online: 2020-12-21

Publish Date: 2021-03-22

Abstract

Abstract

To solve the problems of inter-frame registration difficult, unclear shadow characteristics of fast moving targets and high false alarm probability in traditional Video Synthetic Aperture Radar (ViSAR) moving target detection methods, a novel video SAR moving target detection method based on improved Faster Region-based Convolutional Neural Networks (Faster R-CNN) is proposed. Combining with the deep learning algorithm of Faster R-CNN, the new method applies the K-means clustering method to preprocess the length, width and aspect ratio of the anchor box. Besides, the Feature Pyramid Networks (FPN) network architecture is used to detect the ‘bright line’ feature of the video SAR moving targets. Compared with traditional methods, the proposed method has the advantages of simple implementation, high detection probability and low false alarm probability. Finally, the effectiveness of the new method is verified by the measured video SAR data obtained from the Mini-SAR system developed by our project team.

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References(15)

References

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