期刊
REMOTE SENSING
卷 12, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/rs12183056
关键词
ground penetrating radar (GPR); frequency-wavenumber (f-k) analysis; super resolution (SR) image; deep learning; noise reduction; directivity analysis
类别
资金
- Sejong University
This paper proposes a frequency-wavenumber (f-k) analysis technique through deep learning-based super resolution (SR) ground penetrating radar (GPR) image enhancement. GPR is one of the most popular underground investigation tools owing to its nondestructive and high-speed survey capabilities. However, arbitrary underground medium inhomogeneity and undesired measurement noises often disturb GPR data interpretation. Although thef-kanalysis can be a promising technique for GPR data interpretation, the lack of GPR image resolution caused by the fast or coarse spatial scanning mechanism in reality often leads to analysis distortion. To address the technical issue, we propose thef-kanalysis technique by a deep learning network in this study. The proposedf-kanalysis technique incorporated with the SR GPR images generated by a deep learning network makes it possible to significantly reduce the arbitrary underground medium inhomogeneity and undesired measurement noises. Moreover, the GPR-induced electromagnetic wavefields can be decomposed for directivity analysis of wave propagation that is reflected from a certain underground object. The effectiveness of the proposed technique is numerically validated through 3D GPR simulation and experimentally demonstrated using in-situ 3D GPR data collected from urban roads in Seoul, Korea.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据