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Special Issue "Advances in Signal Processing Techniques for Ground Penetrating Radar Applications"

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 6121

Special Issue Editors

Prof. Dr. Fabio Tosti
E-Mail Website
Guest Editor
School of Computing and Engineering, University of West London, St Mary’s Rd, Ealing, London W5 5RF, UK
Interests: ground-penetrating radar; signal processing; remote sensing; deflection-based methods; numerical simulations; forestry engineering; airfield and highway pavement engineering; structural health monitoring; construction materials; civil engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Amir M. Alani
E-Mail Website
Guest Editor
School of Computing and Engineering, University of West London, St Mary’s Rd, Ealing, London W5 5RF, UK
Interests: ground-penetrating radar; signal processing; modelling and simulation; remote sensing; non-destructive testing; concrete technology; forestry engineering; soil engineering; civil engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Bianchini Ciampoli
E-Mail Website
Guest Editor
Department of Engineering, Roma Tre University, Rome, Italy
Interests: ground-penetrating radar; remote sensing; InSAR; non-destructive testing; modeling and simulation; railway engineering; road safety and highway engineering; civil engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Evert C. Slob
E-Mail Website
Guest Editor
Department of Geoscience and Engineering, Delft University of Technology, Delft, The Netherlands
Interests: ground-penetrating radar; signal processing, imaging and inversion techniques for electromagnetic diffusive fields and wave fields; radar interferometry

Special Issue Information

Ground Penetrating Radar (GPR) technology has recently seen enormous advancements in a wide range of applications including the development of new data processing algorithms and interpretation techniques. Numerous GPR applications, which include civil and geotechnical engineering, highway and transport infrastructure, sedimentology, ground-water contamination, glaciology, archaeology and cultural heritage management, forestry, planetary exploration and demining, amongst others, currently exist. Wide applicability of GPR together with the recent advancements within the field has made the GPR technology an area of endeavour with high reliability and trust. Within this framework, the main scope of this Special Issue is to promote the publication of state-of-the-art research-based investigations on GPR signal processing techniques and applications. Hence, papers with a focus on advanced GPR signal processing techniques in areas including, but not limited to, civil and environmental engineering, geology, archaeology, cultural heritage, and forestry management are encouraged. The following are the areas of interest and priority for this Special Issue:

  • Non-linear GPR signal processing
  • Signal processing for clutter reduction and increase of the SNR for GPR data
  • GPR signal, inverse scattering, and image processing techniques for data interpretation
  • Velocity and attribute analysis
  • Simplified electromagnetic models for real-time GPR signal processing
  • Estimation of the electromagnetic properties of materials from radar signals
  • Frequency dependent attenuation analysis
  • Signal processing for the development of new algorithms and numerical models
  • Ground Penetrating Radar signal processing applications
  • Artificial Intelligence for GPR
  • GPR modelling and inversion for non-conventional platforms (forward looking, radar on UAVs and airborne,..) and MIMO configurations (multiview/multistatic)

Review papers in the above outlined research areas will be also considered.

Prof. Dr. Fabio Tosti
Prof. Dr. Amir M. Alani
Prof. Dr. Francesco Benedetto
Dr. Luca Bianchini Ciampoli
Prof. Dr. Evert C. Slob
Dr. Francesco Soldovieri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 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

  • GPR
  • resampling
  • deconvolution
  • band-pass filtering
  • SNR enhancement
  • EM noise
  • attenuation analysis,
  • attribute analysis
  • migration
  • inverse scattering

Published Papers (6 papers)

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Research

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Article
Missing Data Recovery via Deep Networks for Limited Ground Penetrating Radar Measurements
Remote Sens. 2022, 14(3), 754; https://doi.org/10.3390/rs14030754 - 06 Feb 2022
Viewed by 563
Abstract
Missing data problem frequently occurs during data acquisition in ground-penetrating radar (GPR) and recovery of the missing entries prior to any processing is vital in GPR imaging. Existing missing data recovery methods are based on low-rank matrix completion or the recently proposed deep [...] Read more.
Missing data problem frequently occurs during data acquisition in ground-penetrating radar (GPR) and recovery of the missing entries prior to any processing is vital in GPR imaging. Existing missing data recovery methods are based on low-rank matrix completion or the recently proposed deep generative networks. However, the former approaches suffer from producing satisfying results under severe missing data cases and the latter require a large amount of data for training. This study proposes two methods based on deep networks for the missing data recovery. The first method uses pyramid-context encoder network (PEN-Net) architecture which consists of three parts: attention transfer network, guided Pyramid-context encoder, and a multi-scale decoder. Although the method needs training, it requires considerably less data compared to the existing U-Net based method. The second method, deep image prior (DIP), is a regularization based data recovery method which uses an untrained network as a prior. This method does not need any training, network weights are initialized randomly and updated during the iterations to minimize the cost function. Different experiments are reported for both pixel and column-wise missing cases in simulated and real data. The simulated data results show that the proposed methods have a noticeably better performance than conventional methods for the challenging pixel-wise case around 17–27% and moderate level column-wise missing case around 15%. Besides, they can also deal with extreme column-wise missing data cases where the conventional methods fail completely. Real data results further verify the superiority of the proposed methods. Full article
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Article
Effect of Moisture Content on Calculated Dielectric Properties of Asphalt Concrete Pavements from Ground-Penetrating Radar Measurements
Remote Sens. 2022, 14(1), 34; https://doi.org/10.3390/rs14010034 - 22 Dec 2021
Cited by 1 | Viewed by 993
Abstract
Moisture presence in asphalt concrete (AC) pavement is a major cause of damage to the pavement. In recent decades, an increasing need exists for non-destructive detection and monitoring of the moisture content in AC pavement. This paper provides a simulated approach to quantify [...] Read more.
Moisture presence in asphalt concrete (AC) pavement is a major cause of damage to the pavement. In recent decades, an increasing need exists for non-destructive detection and monitoring of the moisture content in AC pavement. This paper provides a simulated approach to quantify the effect of internal moisture content on AC pavement dielectric properties using ground-penetrating radar (GPR). A heterogeneous numerical model was developed to simulate AC pavement with internal moisture at various saturation levels. The numerical model was validated using GPR surveys on cold-in-place recycling treated pavements. An empirical formula was derived from the simulation to correlate the dielectric constant with the moisture content for non-dry AC pavement. The results validated the proposed model and, hence, demonstrated the ability of GPR to monitor moisture variation in AC pavements. Full article
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Article
An Improved Particle Swarm Optimization Based on Total Variation Regularization and Projection Constraint with Applications in Ground-Penetrating Radar Inversion: A Model Simulation Study
Remote Sens. 2021, 13(13), 2514; https://doi.org/10.3390/rs13132514 - 27 Jun 2021
Cited by 1 | Viewed by 650
Abstract
The chaos oscillation particle swarm optimization (COPSO) algorithm is prone to binge trapped in the local optima when dealing with certain complex models in ground-penetrating radar (GPR) data inversion, because it inherently suffers from premature convergence, high computational costs, and extremely slow convergence [...] Read more.
The chaos oscillation particle swarm optimization (COPSO) algorithm is prone to binge trapped in the local optima when dealing with certain complex models in ground-penetrating radar (GPR) data inversion, because it inherently suffers from premature convergence, high computational costs, and extremely slow convergence times, especially in the middle and later periods of iterative inversion. Considering that the bilateral connections between different particle positions can improve both the algorithmic searching efficiency and the convergence performance, we first develop a fast single-trace-based approach to construct an initial model for 2-D PSO inversion and then propose a TV-regularization-based improved PSO (TVIPSO) algorithm that employs total variation (TV) regularization as a constraint technique to adaptively update the positions of particles. B by adding the new velocity variations and optimal step size matrices, the search range of the random particles in the solution space can be significantly reduced, meaning blindness in the search process can be avoided. By introducing constraint-oriented regularization to allow the optimization search to move out of the inaccurate region, the premature convergence and blurring problems can be mitigated to further guarantee the inversion accuracy and efficiency. We report on three inversion experiments involving multilayered, fluctuated terrain models and a typical complicated inner-interface model to demonstrate the performance of the proposed algorithm. The results of the fluctuated terrain model show that compared with the COPSO algorithm, the fitness error (MAE) of the TVIPSO algorithm is reduced from 2.3715 to 1.0921, while for the complicated inner-interface model the fitness error (MARE) of the TVIPSO algorithm is reduced from 1.9539 to 1.5674. Full article
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Article
An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar
Remote Sens. 2020, 12(20), 3417; https://doi.org/10.3390/rs12203417 - 18 Oct 2020
Cited by 6 | Viewed by 1243
Abstract
The preservation of natural assets is nowadays an essential commitment. In this regard, root systems are endangered by fungal diseases which can undermine the health and stability of trees. Within this framework, ground penetrating radar (GPR) is emerging as a reliable non-destructive method [...] Read more.
The preservation of natural assets is nowadays an essential commitment. In this regard, root systems are endangered by fungal diseases which can undermine the health and stability of trees. Within this framework, ground penetrating radar (GPR) is emerging as a reliable non-destructive method for root investigation. A coherent GPR-based root-detection framework is presented in this paper. The proposed methodology is a multi-stage data analysis system that is applied to semi-circular measurements collected around the investigated tree. In the first step, the raw data are processed by applying several standard and advanced signal processing techniques in order to reduce noise-related information. In the second stage, the presence of any discontinuity element within the survey area is investigated by analysing the signal reflectivity. Then, a tracking algorithm aimed at identifying patterns compatible with tree roots is implemented. Finally, the mass density of roots is estimated by means of continuous functions in order to achieve a more realistic representation of the root paths and to identify their length in a continuous and more realistic domain. The method was validated in a case study in London (UK), where the root system of a real tree was surveyed using GPR and a soil test pit was excavated for validation purposes. Results support the feasibility of the data processing framework implemented in this study. Full article
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Technical Note
MCMC Method of Inverse Problems Using a Neural Network—Application in GPR Crosshole Full Waveform Inversion: A Numerical Simulation Study
Remote Sens. 2022, 14(6), 1320; https://doi.org/10.3390/rs14061320 - 09 Mar 2022
Viewed by 521
Abstract
Ground-penetrating radar (GPR) crosshole tomography is widely applied to subsurface media images. However, the inadequacies of ray methods may limit the resolution of crosshole radar images, since the ray method is a type of high-frequency approximation. To solve this problem, the full waveform [...] Read more.
Ground-penetrating radar (GPR) crosshole tomography is widely applied to subsurface media images. However, the inadequacies of ray methods may limit the resolution of crosshole radar images, since the ray method is a type of high-frequency approximation. To solve this problem, the full waveform method is introduced for GPR inversion. However, full waveform inversion is computationally expensive. In this paper, we introduce a trained neural network that can be evaluated very quickly to replace a computationally intensive forward model. Additionally, the forward error of the trained neural network can be statistically analyzed. We demonstrate a methodology for a full waveform inversion of crosshole ground-penetrating radar data using the Markov chain Monte Carlo (MCMC) method. An accurate forward model based on Maxwell’s equations is replaced by a quickly trained neural network. This method achieves a high computation efficiency, which is four orders of magnitude faster than the accurate forward model. The inversion result of the synthetic waveform data shows a good performance of the trained neural network, which greatly improves the calculation efficiency. Full article
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Technical Note
Recognition of the Typical Distress in Concrete Pavement Based on GPR and 1D-CNN
by , and
Remote Sens. 2021, 13(12), 2375; https://doi.org/10.3390/rs13122375 - 18 Jun 2021
Cited by 5 | Viewed by 879
Abstract
Ground-penetrating radar (GPR) signal recognition depends much on manual feature extraction. However, the complexity of radar detection signals leads to conventional intelligent algorithms lacking sufficient flexibility in concrete pavement detection. Focused on these problems, we proposed an adaptive one-dimensional convolution neural network (1D-CNN) [...] Read more.
Ground-penetrating radar (GPR) signal recognition depends much on manual feature extraction. However, the complexity of radar detection signals leads to conventional intelligent algorithms lacking sufficient flexibility in concrete pavement detection. Focused on these problems, we proposed an adaptive one-dimensional convolution neural network (1D-CNN) algorithm for interpreting GPR data. Firstly, the training dataset and testing dataset were constructed from the detection signals on pavement samples of different types of distress; secondly, the raw signals are were directly inputted into the 1D-CNN model, and the raw signal features of the radar wave are extracted using the adaptive deep learning network; finally, the output used the Soft-Max classifier to provide the classification result of the concrete pavement distress. Through simulation experiments and actual field testing, the results show that the proposed method has high accuracy and excellent generalization performance compared to the conventional method. It also has practical applications. Full article
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