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

Man-Sung Kang,Yun-Kyu An

doi:10.3390/rs12183056

Abstract

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.

Highlights

In the past few decades, sinkhole accidents of urban roads have posed a serious hazard to buildings, infrastructures and especially inhabitants of the area [1,2]
This paper proposes a frequency–wavenumber (f–k) technique of 3D ground penetrating radar (GPR) data, which enables one to effectively eliminate incoherent noises and precisely analyze the electromagnetic wave propagation directivity
The proposed technique is able to effectively eliminate incoherent noises caused by arbitrary underground medium inhomogeneity and undesired measurement noises, which is one of the biggest technical conundrums in real-world GPR data interpretation

Summary

Introduction

In the past few decades, sinkhole accidents of urban roads have posed a serious hazard to buildings, infrastructures and especially inhabitants of the area [1,2]. Kim et al [22] proposed a convolutional neural network (CNN) combined with a statistical thresholding technique to classify underground objects using GPR B-scan images. High resolution GPR images, which are composed of dense spatial GPR data considering the minimum target underground object size, are necessary for the proper f–k analysis. Numerous deep learning-based SR image generation techniques have been proposed. As for the GPR application, Kang et al proposed a deep learning-based SR GPR image generation network for enhancing underground cavity detectability [42]. The f–k analysis incorporated with a deep learning-based SR network is proposed for unwanted noise reduction and electromagnetic wavefield decomposition. The effectiveness of the proposed technique is numerically validated using 3D GPR simulation data and experimentally demonstrated using in-situ 3D GPR data obtained from complex urban roads at Seoul, Korea

Deep Learning-based SR GPR Image Enhancement

Experimental Validation Using In-Situ 3D GPR Data

Findings

Discussion

Conclusions