DR-GAN: An Unsupervised Learning Approach to Clutter Suppression for Ground Penetrating Radar

LEI Wentai; MAO Lingqing; PANG Zebang; REN Qiang; WANG Chenghao; SUI Hao; XIN Changle

doi:10.11999/JEIT221072

Volume 45 Issue 10

Oct. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(10): 3776-3785

LEI Wentai, MAO Lingqing, PANG Zebang, REN Qiang, WANG Chenghao, SUI Hao, XIN Changle. DR-GAN: An Unsupervised Learning Approach to Clutter Suppression for Ground Penetrating Radar[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3776-3785. doi: 10.11999/JEIT221072

Citation:

LEI Wentai, MAO Lingqing, PANG Zebang, REN Qiang, WANG Chenghao, SUI Hao, XIN Changle. DR-GAN: An Unsupervised Learning Approach to Clutter Suppression for Ground Penetrating Radar[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3776-3785. doi: 10.11999/JEIT221072

Citation:

LEI Wentai, MAO Lingqing, PANG Zebang, REN Qiang, WANG Chenghao, SUI Hao, XIN Changle. DR-GAN: An Unsupervised Learning Approach to Clutter Suppression for Ground Penetrating Radar[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3776-3785. doi: 10.11999/JEIT221072

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DR-GAN: An Unsupervised Learning Approach to Clutter Suppression for Ground Penetrating Radar

doi: 10.11999/JEIT221072

1.
School of Computer Science, Central South University, Changsha 410083, China
2.
China Research Institute of Radiowave Propagation, Qingdao 266107, China

Funds: The Stable-Support Scientific Project of China Research Institute of Radiowave Propagation (A131903W13)

Received Date: 2022-08-15
Rev Recd Date: 2023-02-16

Available Online: 2023-02-22

Publish Date: 2023-10-31

Abstract

Abstract

Ground Penetrating Radar (GPR) is an underground nondestructive detection technology based on electromagnetic wave, which is widely used in municipal engineering, transportation, military and other fields. In the process of data acquisition, due to the coupling between transmitting antenna and receiving antenna, scattering from undulating ground and the complexity of underground random media, there is usually clutter in the GPR B-scan, which affects seriously the detection and feature extraction of underground targets. A Disentanglement Representation Generative Adversarial network (DR-GAN) for clutter suppression in GPR B-scan images is proposed. A target feature encoder and a clutter feature encoder are designed to extract target features and clutter features in GPR B-scan images. A clutter suppression generator is designed to obtain the GPR B-scan image after clutter suppression. Compared with the existing GPR clutter suppression methods based on supervised learning, the proposed method does not need pairwise matching data during network training, and can be better applied to the clutter suppression of measured GPR images. Experimental results on simulated and measured GPR data show that DR-GAN is an unsupervised learning network with better clutter suppression performance. The data of reinforcement embedded in quartz sand are collected, and the measured data containing clutter are processed by DR-GAN. The Improvement Factor (IF) index of the processing results is 17.85 dB higher than that of the existing Robust Nonnegative Matrix Factorization (RNMF) method.
- Ground Penetrating Radar (GPR),
- Clutter suppression,
- Unsupervised learning,
- Disentangled Representations (DR),
- Generative Adversarial Networks (GAN)

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References(22)

References

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