Citation: Wang, H.; Ouyang, S.; Liu,
Q.; Liao, K.; Zhou, L. Deep-Learning-
Based Method for Estimating
Permittivity of Ground-Penetrating
Radar Targets. Remote Sens. 2022, 14,
4293. https://doi.org/10.3390/
rs14174293
Academic Editor: Roberto Orosei
Received: 21 July 2022
Accepted: 29 August 2022
Published: 31 August 2022
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Article
Deep-Learning-Based Method for Estimating Permittivity of
Ground-Penetrating Radar Targets
Hui Wang
1,2
, Shan Ouyang
1,
* , Qinghua Liu
1
, Kefei Liao
1
and Lijun Zhou
3
1
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
2
School of Artificial Intelligence, Hezhou University, Hezhou 542899, China
3
Shanxi Transportation Technology R&D Co., Ltd., Taiyuan 030032, China
* Correspondence: hmoysh@guet.edu.cn; Tel.: +86-077-3229-0203
Abstract:
Correctly estimating the relative permittivity of buried targets is crucial for accurately de-
termining the target type, geometric size, and reconstruction of shallow surface geological structures.
In order to effectively identify the dielectric properties of buried targets, on the basis of extracting the
feature information of B-SCAN images, we propose an inversion method based on a deep neural
network (DNN) to estimate the relative permittivity of targets. We first take the physical mechanism
of ground-penetrating radar (GPR), working in the reflection measurement mode as the constrain
condition, and then design a convolutional neural network (CNN) to extract the feature hyperbola
of the underground target, which is used to calculate the buried depth of the target and the relative
permittivity of the background medium. We further build a regression network and train the network
model with the labeled sample set to estimate the relative permittivity of the target. Tests were
carried out on the GPR simulation dataset and the field dataset of underground rainwater pipelines,
respectively. The results show that the inversion method has high accuracy in estimating the relative
permittivity of the target.
Keywords:
ground-penetrating radar (GPR); deep learning; regression network; parameter inversion
1. Introduction
GPR has been widely used in shallow subsurface detection using non-destructive
remote sensing measurement technology. The GPR transmits electromagnetic pulse signals
underground and inverts the received GPR echo data to obtain information such as the
shape, size, location, and dielectric parameters of the buried target. The commonly used
GPR inversion methods include reverse-time migration (RTM) methods [
1
], common
midpoint (CMP) measurements [
2
], diffraction tomography (DT) approaches [
3
], and full
wave inversion (FWI) [
4
]. The migration method converts (or migrates) an unfocused space-
time GPR image into a focused one, showing the true location and size of the target, but it
cannot get the electromagnetic parameters of the targets [
5
,
6
]. The CMP method is usually
used to invert the permittivity of the medium, which constructs the propagation equation
of electromagnetic waves under different transmission and reception distances by using
the B-SCAN data with the change of the transmission and reception distance [
7
,
8
]. The DT
imaging method is established based on the Fourier Transform and Born approximation,
which can be deduced from the linear relationship between the spatial Fourier transform
of the object contrast function and the back-scattered field of the targets [
9
,
10
]. FWI is a
data-fitting technique to estimate the permittivity and conductivity of the medium through
the minimization of the distance between the observed data and those predicted by the
adopted model [
11
,
12
], but this inversion method is very sensitive to the initial model; a
poor starting model can easily lead the inversion into a local minimum or cycle skipping.
Fundamentally, estimating the electrical property parameters of the subsurface medium
based on the scattered field obtained by GPR entails solving an electromagnetic inverse
Remote Sens. 2022, 14, 4293. https://doi.org/10.3390/rs14174293 https://www.mdpi.com/journal/remotesensing
