Bibliographic Details
| Title: |
Improved Cycle-Consistency Generative Adversarial Network-Based Clutter Suppression Methods for Ground-Penetrating Radar Pipeline Data. |
| Authors: |
Lin, Yun, Wang, Jiachun, Ma, Deyun, Wang, Yanping, Ye, Shengbo |
| Source: |
Remote Sensing; Mar2024, Vol. 16 Issue 6, p1043, 18p |
| Subject Terms: |
GROUND penetrating radar, GENERATIVE adversarial networks, DEEP learning |
| Abstract: |
Ground-penetrating radar (GPR) is a widely used technology for pipeline detection due to its fast detection speed and high resolution. However, the presence of complex underground media often results in strong ground clutter interference in the collected B-scan echoes, significantly impacting detection performance. To address this issue, this paper proposes an improved clutter suppression network based on a cycle-consistency generative adversarial network (CycleGAN). By employing the concept of style transfer, the network aims to convert clutter images into clutter-free images. This paper introduces multiple residual blocks into the generator and discriminator, respectively, to improve the feature expression ability of the deep learning model. Additionally, the discriminator incorporates the squeeze and excitation (SE) module, a channel attention mechanism, to further enhance the model's ability to extract features from clutter-free images. To evaluate the effectiveness of the proposed network in clutter suppression, both simulation and measurement data are utilized to compare and analyze its performance against traditional clutter suppression methods and deep learning-based methods, respectively. From the result of the measured data, it can be found that the improvement factor ( I m ) of the proposed method has reached 40.68 dB, which is a significant improvement compared to the previous network. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
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