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Advanced Techniques for Ground Penetrating Radar Imaging

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 43522

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Special Issue Editors

Dr. Yuri Álvarez López

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Guest Editor

Area of Signal Theory and Communications - University of Oviedo, Edificio Polivalente, Módulo 8, Room 1.8.02, Campus Universitario de Gijón, E-33203 Gijón (Asturias), Spain
Interests: antenna measurement; antenna diagnostics; inverse scattering; microwave imaging; ground-penetrating radar; indoor locations systems

Dr. María García Fernández

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Guest Editor

School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT7 1NN, Northern Ireland, UK
Interests: inverse scattering; microwave imaging; ground penetrating radar; antenna measurement; unmanned aerial vehicles; positioning and geo-referring systems
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Dear Colleagues,

Ground-penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in nondestructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. Furthermore, it can also provide images from the underground, thus improving detection capabilities. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology (detection of cavities, mineral deposits), glaciology (measurement of ice thickness), civil engineering and civil works (detection of cracks and defects in infrastructure), archaeology, and security and defense (detection of buried landmines and improvised explosive devices).

In recent decades, improvements in georeferring and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others.

This Special Issue is intended to provide an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives. Review papers on GPR imaging are also welcome. Topics of interest include but are not limited to:

- Advances in GPR RF front-end: antenna design, antenna arrays, radar devices;
- Novel GPR architectures;
- Synthetic aperture radar techniques for GPR;
- Clutter mitigation algorithms;
- Motion compensation and autofocus techniques for GPR;
- Soil composition estimation;
- Multilayered techniques for GPR imaging (e.g., based on Green’s function of layered media);
- Compressed sensing techniques applied to GPR;
- Artificial Intelligence techniques for GPR images (e.g., automatic target recognition for landmine and IEDs detection).

Dr. Yuri Álvarez López
Ms. María García Fernández
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

ground penetrating radar
synthetic aperture radar
clutter removal
motion compensation
soil composition
multilayer media
compressive sensing
subsurface imaging
landmine detection
through-the-wall imaging

Published Papers (11 papers)

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Editorial

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4 pages, 199 KiB

Open AccessEditorial

Editorial for the Special Issue “Advanced Techniques for Ground Penetrating Radar Imaging”

by Yuri Álvarez López and María García-Fernández

Remote Sens. 2021, 13(18), 3696; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183696 - 16 Sep 2021

Cited by 1 | Viewed by 1876

Abstract

Ground Penetrating Radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in Non-Destructive Testing (NDT), since it is able to detect both metallic and nonmetallic targets [...] Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

Research

Jump to: Editorial

14 pages, 3976 KiB

Open AccessArticle

Underground Pipeline Identification into a Non-Destructive Case Study Based on Ground-Penetrating Radar Imaging

by Nicoleta Iftimie, Adriana Savin, Rozina Steigmann and Gabriel Silviu Dobrescu

Remote Sens. 2021, 13(17), 3494; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173494 - 03 Sep 2021

Cited by 18 | Viewed by 5010

Abstract

Ground-penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in nondestructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR has proven its ability to work in electromagnetic frequency range for subsoil investigations, and it is a risk-reduction strategy for surveying underground various targets and their identification and detection. This paper presents the results of a case study which exceeds the laboratory level being realized in the field in a real case where the scanning conditions are much more difficult using GPR signals for detecting and assessing underground drainage metallic pipes which cross an area with large buildings parallel to the riverbed. The two urban drainage pipes are detected based on GPR imaging. This provides an approximation of their location and depth which are convenient to find from the reconstructed profiles of both simulated and practical GPR signals. The processing of data recorded with GPR tools requires appropriate software for this type of measurement to detect between different reflections at multiple interfaces located at different depths below the surface. In addition to the radargrams recorded and processed with the software corresponding to a GPR device, the paper contains significant results obtained using techniques and algorithms of the processing and post-processing of the signals (background removal and migration) that gave us the opportunity to estimate the location, depth, and profile of pipes, placed into a concrete duct bank, under a structure with different layers, including pavement, with good accuracy. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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15 pages, 6160 KiB

Open AccessArticle

GPR Clutter Reflection Noise-Filtering through Singular Value Decomposition in the Bidimensional Spectral Domain

by Rui Jorge Oliveira, Bento Caldeira, Teresa Teixidó and José Fernando Borges

Remote Sens. 2021, 13(10), 2005; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13102005 - 20 May 2021

Cited by 10 | Viewed by 2584

Abstract

Usually, in ground-penetrating radar (GPR) datasets, the user defines the limits between the useful signal and the noise through standard filtering to isolate the effective signal as much as possible. However, there are true reflections that mask the coherent reflectors that can be considered noise. In archaeological sites these clutter reflections are caused by scattering with origin in subsurface elements (e.g., isolated masonry, ceramic objects, and archaeological collapses). Its elimination is difficult because the wavelet parameters similar to coherent reflections and there is a risk of creating artefacts. In this study, a procedure to filter the clutter reflection noise (CRN) from GPR datasets is presented. The CRN filter is a singular value decomposition-based method (SVD), applied in the 2D spectral domain. This CRN filtering was tested in a dataset obtained from a controlled laboratory environment, to establish a mathematical control of this algorithm. Additionally, it has been applied in a 3D-GPR dataset acquired in the Roman villa of Horta da Torre (Fronteira, Portugal), which is an uncontrolled environment. The results show an increase in the quality of archaeological GPR planimetry that was verified via archaeological excavation. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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22 pages, 30089 KiB

Open AccessArticle

Deep Convolutional Denoising Autoencoders with Network Structure Optimization for the High-Fidelity Attenuation of Random GPR Noise

by Deshan Feng, Xiangyu Wang, Xun Wang, Siyuan Ding and Hua Zhang

Remote Sens. 2021, 13(9), 1761; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091761 - 01 May 2021

Cited by 7 | Viewed by 3176

Abstract

The high-fidelity attenuation of random ground penetrating radar (GPR) noise is important for enhancing the signal-noise ratio (SNR). In this paper, a novel network structure for convolutional denoising autoencoders (CDAEs) was proposed to effectively resolve various problems in the noise attenuation process, including overfitting, the size of the local receptive field, and representational bottlenecks and vanishing gradients in deep learning; this approach also significantly improves the noise attenuation performance. We described the noise attenuation process of conventional CDAEs, and then presented the output feature map of each convolutional layer to analyze the role of convolutional layers and their impacts on GPR data. Furthermore, we focused on the problems of overfitting, the local receptive field size, and the occurrence of representational bottlenecks and vanishing gradients in deep learning. Subsequently, a network structure optimization strategy, including a dropout regularization layer, an atrous convolution layer, and a residual-connection structure, was proposed, namely convolutional denoising autoencoders with network structure optimization (CDAEsNSO), comprising an intermediate version, called atrous-dropout CDAEs (AD-CDAEs), and a final version, called residual-connection CDAEs (ResCDAEs), all of which effectively improve the performance of conventional CDAEs. Finally, CDAEsNSO was applied to attenuate noise for the H-beam model, tunnel lining model, and field pipeline data, confirming that the algorithm adapts well to both synthetic and field data. The experiments verified that CDAEsNSO not only effectively attenuates strong Gaussian noise, Gaussian spike impulse noise, and mixed noise, but it also causes less damage to the original waveform data and maintains high-fidelity information. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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18 pages, 6539 KiB

Open AccessArticle

Analysis and Validation of a Hybrid Forward-Looking Down-Looking Ground Penetrating Radar Architecture

by María García-Fernández, Guillermo Álvarez-Narciandi, Yuri Álvarez López and Fernando Las-Heras Andrés

Remote Sens. 2021, 13(6), 1206; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061206 - 22 Mar 2021

Cited by 6 | Viewed by 2514

Abstract

Ground Penetrating Radar (GPR) has proved to be a successful technique for the detection of landmines and Improvised Explosive Devices (IEDs) buried in the ground. In the last years, novel architectures for safe and fast detection, such as those based on GPR systems onboard Unmanned Aerial Vehicles (UAVs), have been proposed. Furthermore, improvements in GPR hardware and signal processing techniques have resulted in a more efficient detection. This contribution presents an experimental validation of a hybrid Forward-Looking–Down-Looking GPR architecture. The main goal of this architecture is to combine advantages of both GPR architectures: reduction of clutter coming from the ground surface in the case of Forward-Looking GPR (FLGPR), and greater dynamic range in the case of Down-Looking GPR (DLGPR). Compact radar modules working in the lower SHF frequency band have been used for the validation of the hybrid architecture, which involved realistic targets. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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23 pages, 9090 KiB

Open AccessArticle

Analysis of the Snow Water Equivalent at the AEMet-Formigal Field Laboratory (Spanish Pyrenees) During the 2019/2020 Winter Season Using a Stepped-Frequency Continuous Wave Radar (SFCW)

by Rafael Alonso, José María García del Pozo, Samuel T. Buisán and José Adolfo Álvarez

Remote Sens. 2021, 13(4), 616; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040616 - 09 Feb 2021

Cited by 7 | Viewed by 2546

Abstract

Snow makes a great contribution to the hydrological cycle in cold regions. The parameter to characterize available the water from the snow cover is the well-known snow water equivalent (SWE). This paper presents a near-surface-based radar for determining the SWE from the measured complex spectral reflectance of the snowpack. The method is based in a stepped-frequency continuous wave radar (SFCW), implemented in a coherent software defined radio (SDR), in the range from 150 MHz to 6 GHz. An electromagnetic model to solve the electromagnetic reflectance of a snowpack, including the frequency and wetness dependence of the complex relative dielectric permittivity of snow layers, is shown. Using the previous model, an approximated method to calculate the SWE is proposed. The results are presented and compared with those provided by a cosmic-ray neutron SWE gauge over the 2019–2020 winter in the experimental AEMet Formigal-Sarrios test site. This experimental field is located in the Spanish Pyrenees at an elevation of 1800 m a.s.l. The results suggest the viability of the approximate method. Finally, the feasibility of an auxiliary snow height measurement sensor based on a 120 GHz frequency modulated continuous wave (FMCW) radar sensor, is shown. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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24 pages, 11578 KiB

Open AccessArticle

Wavelet Scattering Network-Based Machine Learning for Ground Penetrating Radar Imaging: Application in Pipeline Identification

by Yang Jin and Yunling Duan

Remote Sens. 2020, 12(21), 3655; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213655 - 07 Nov 2020

Cited by 22 | Viewed by 4559

Abstract

Automatic and efficient ground penetrating radar (GPR) data analysis remains a bottleneck, especially restricting applications in real-time monitoring systems. Deep learning approaches have good practice in automatic object identification, but their intensive data requirement has reduced their applicability. This paper developed a machine learning framework based on wavelet scattering networks to analyze GPR data for subsurface pipeline identification. Wavelet scattering network is functionally equivalent to convolutional neural networks, and its null-parameter property is intended for non-intensive datasets. A double-channel framework is designed with wavelet scattering networks followed by support vector machines to determine the existence of pipelines on vertical and horizontal traces separately. Classification accuracy rates arrive around 98% and 95% for datasets without and with noises, respectively, as well as 97% for considering surface roughness. Pipeline locations and diameters are convenient to determine from the reconstructed profiles of both simulated and practical GPR signals. However, the results of 5 cm pipelines are sensitive to noises. Nonetheless, the developed machine learning approach presents promising applicability in subsurface pipeline identification. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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25 pages, 10435 KiB

Open AccessArticle

Convolutional Neural Network with Spatial-Variant Convolution Kernel

by Yongpeng Dai, Tian Jin, Yongkun Song, Shilong Sun and Chen Wu

Remote Sens. 2020, 12(17), 2811; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172811 - 30 Aug 2020

Cited by 10 | Viewed by 4371

Abstract

Radar images suffer from the impact of sidelobes. Several sidelobe-suppressing methods including the convolutional neural network (CNN)-based one has been proposed. However, the point spread function (PSF) in the radar images is sometimes spatially variant and affects the performance of the CNN. We propose the spatial-variant convolutional neural network (SV-CNN) aimed at this problem. It will also perform well in other conditions when there are spatially variant features. The convolutional kernels of the CNN can detect motifs with some distinctive features and are invariant to the local position of the motifs. This makes the convolutional neural networks widely used in image processing fields such as image recognition, handwriting recognition, image super-resolution, and semantic segmentation. They also perform well in radar image enhancement. However, the local position invariant character might not be good for radar image enhancement, when features of motifs (also known as the point spread function in the radar imaging field) vary with the positions. In this paper, we proposed an SV-CNN with spatial-variant convolution kernels (SV-CK). Its function is illustrated through a special application of enhancing the radar images. After being trained using radar images with position-codings as the samples, the SV-CNN can enhance the radar images. Because the SV-CNN reads information of the local position contained in the position-coding, it performs better than the conventional CNN. The advance of the proposed SV-CNN is tested using both simulated and real radar images. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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17 pages, 8066 KiB

Open AccessArticle

High-Resolution Coherency Functionals for Improving the Velocity Analysis of Ground-Penetrating Radar Data

by Eusebio Stucchi, Adriano Ribolini and Andrea Tognarelli

Remote Sens. 2020, 12(13), 2146; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12132146 - 04 Jul 2020

Cited by 3 | Viewed by 2706

Abstract

We aim at verifying whether the use of high-resolution coherency functionals could improve the signal-to-noise ratio (S/N) of Ground-Penetrating Radar data by introducing a variable and precisely picked velocity field in the migration process. After carrying out tests on synthetic data to schematically simulate the problem, assessing the types of functionals most suitable for GPR data analysis, we estimated a varying velocity field relative to a real dataset. This dataset was acquired in an archaeological area where an excavation after a GPR survey made it possible to define the position, type, and composition of the detected targets. Two functionals, the Complex Matched Coherency Measure and the Complex Matched Analysis, turned out to be effective in computing coherency maps characterized by high-resolution and strong noise rejection, where velocity picking can be done with high precision. By using the 2D velocity field thus obtained, migration algorithms performed better than in the case of constant or 1D velocity field, with satisfactory collapsing of the diffracted events and moving of the reflected energy in the correct position. The varying velocity field was estimated on different lines and used to migrate all the GPR profiles composing the survey covering the entire archaeological area. The time slices built with the migrated profiles resulted in a higher S/N than those obtained from non-migrated or migrated at constant velocity GPR profiles. The improvements are inherent to the resolution, continuity, and energy content of linear reflective areas. On the basis of our experience, we can state that the use of high-resolution coherency functionals leads to migrated GPR profiles with a high-grade of hyperbolas focusing. These profiles favor better imaging of the targets of interest, thereby allowing for a more reliable interpretation. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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20 pages, 68569 KiB

Open AccessArticle

Beyond Amplitudes: Multi-Trace Coherence Analysis for Ground-Penetrating Radar Data Imaging

by Immo Trinks and Alois Hinterleitner

Remote Sens. 2020, 12(10), 1583; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101583 - 16 May 2020

Cited by 15 | Viewed by 4073

Abstract

Under suitable conditions, ground-penetrating radar (GPR) measurements harbour great potential for the non-invasive mapping and three-dimensional investigation of buried archaeological remains. Current GPR data visualisations almost exclusively focus on the imaging of GPR reflection amplitudes. Ideally, the resulting amplitude maps show subsurface structures of archaeological interest in plan view. However, there exist situations in which, despite the presence of buried archaeological remains, hardly any corresponding anomalies can be observed in the GPR time- or depth-slice amplitude images. Following the promising examples set by seismic attribute analysis in the field of exploration seismology, it should be possible to exploit other attributes than merely amplitude values for the enhanced imaging of subsurface structures expressed in GPR data. Coherence is the seismic attribute that is a measure for the discontinuity between adjacent traces in post-stack seismic data volumes. Seismic coherence analysis is directly transferable to common high-resolution 3D GPR data sets. We demonstrate, how under the right circumstances, trace discontinuity analysis can substantially enhance the imaging of structural information contained in GPR data. In certain cases, considerably improved data visualisations are achievable, facilitating subsequent data interpretation. We present GPR trace coherence imaging examples taken from extensive, high-resolution archaeological prospection GPR data sets. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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20 pages, 9739 KiB

Open AccessFeature PaperArticle

Autonomous Airborne 3D SAR Imaging System for Subsurface Sensing: UWB-GPR on Board a UAV for Landmine and IED Detection

by Maria Garcia-Fernandez, Yuri Alvarez-Lopez and Fernando Las Heras

Remote Sens. 2019, 11(20), 2357; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202357 - 11 Oct 2019

Cited by 48 | Viewed by 6979

Abstract

This work presents an enhanced autonomous airborne Synthetic Aperture Radar (SAR) imaging system able to provide full 3D radar images from the subsurface. The proposed prototype and methodology allow the safe detection of both metallic and non-metallic buried targets even in difficult-to-access scenarios without interacting with the ground. Thus, they are particularly suitable for detecting dangerous targets, such as landmines and Improvised Explosive Devices (IEDs). The prototype is mainly composed by an Ultra-Wide-Band (UWB) radar module working from Ultra-High-Frequency (UHF) band and a high accuracy dual-band Real Time Kinematic (RTK) positioning system mounted on board an Unmanned Aerial Vehicle (UAV). The UAV autonomously flies over the region of interest, gathering radar measurements. These measurements are accurately geo-referred so as to enable their coherent combination to obtain a well-focused SAR image. Improvements in the processing chain are also presented in order to deal with some issues associated to UAV-based measurements (such as non-uniform acquisition grids) as well as to enhance the resolution and the signal to clutter ratio of the image. Both the prototype and the methodology were validated with measurements, showing their capability to provide high-resolution 3D SAR images. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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