NDLI: Frequency–Wavenumber Analysis of Deep Learning-based Super Resolution 3D GPR Images

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Frequency–Wavenumber Analysis of Deep Learning-based Super Resolution 3D GPR Images

Content Provider	MDPI
Author	Kang, Man-Sung An, Yun-Kyu
Copyright Year	2020
Description	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 the f–k analysis 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 the f–k analysis technique by a deep learning network in this study. The proposed f–k analysis 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.
Starting Page	3056
e-ISSN	20724292
DOI	10.3390/rs12183056
Journal	Remote Sensing
Issue Number	18
Volume Number	12
Language	English
Publisher	MDPI
Publisher Date	2020-09-18
Access Restriction	Open
Subject Keyword	Remote Sensing Imaging Science Ground Penetrating Radar (gpr) Frequency–wavenumber (f–k) Analysis Super Resolution (sr) Image Deep Learning Noise Reduction Directivity Analysis
Content Type	Text
Resource Type	Article

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