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Radar Techniques for Structures Characterization and Monitoring

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 25707

Special Issue Editors

COMEGI, Faculty of Engineering and Technologies, University of Lusiada Norte, Famalicão, Lisbon, Portugal
Interests: GPR; NDT; masonry; modeling design
The Urban and Civil Engineering Testing and Modeling Laboratory (EMGCU), Department of Materials and Structures (MAST), Université Gustave Eiffe, 14-20 Boulevard Newton, Champs-sur-Marne, 77447 Marne-la-Vallée Cedex 2, Bâtiment, Paris, France
Interests: ground penetrating radar; NDT applied to structural damage assessment; structural health monitoring; transport infrastructure inspection; masonry structures; seismic risk assessment; civil engineering; numerical modelling and data analysis
Special Issues, Collections and Topics in MDPI journals
Dipartimento di Ingegneria, Università Degli Studi di Napoli “Parthenope”, 80133 Napoli, NA, Italy
Interests: microwave remote sensing; signal processing; synthetic aperture radar (SAR); differential interferometry; DEM reconstruction; SAR tomography; image processing for remote sensing applications; radar signal processing; urban remote sensing; structure monitoring.
School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: computational electromagnetics; ground-penetrating radar applications in civil engineering and geosciences; borehole geophysics; artificial intelligence applications in non-destructive testing

Special Issue Information

Dear Colleagues,

Many existing buildings and structures are currently built according to outdated and obsolete structural and construction codes. These buildings will undergo some sort of structural rehabilitation in the near future, especially if they are located in a highly seismic area. It is widely accepted that for proper rehabilitation design a thorough characterization of the structural capacity is essential. Additionally, continuous monitoring of these structures is very important for detecting possible anomalies in their static behavior so that appropriate measures can be put in place to prevent serious structural problems, thus producing the best results with minimal economic effort.

To obtain information relative to the current state of structures, remote methods are preferred due to their speed, nondestructive nature, repeatability, wide area coverage, etc. There has been active research in these techniques for the last three decades. Among the vast number of remote methods that exist, radar-based techniques (such as ground-penetrating radar and SAR) provide the most precise (very deep penetration, high resolution, and ability to detect millimeter-scale movements) and flexible (can travel through air, cross voids, be handheld or launched in vehicles or even from satellites, etc.) technologies. In this scenario, the present Special Issue on “Radar Techniques for Structures Characterization and Monitoring” aims to be a state-of-the-art collection of studies on radar techniques that are commonly used for the characterization and monitoring of civil engineering structures, showing some of the most relevant research currently being carried out, highlighting future challenges, and including several case studies.

Topics of interest include (but are not limited to) the following:

  • Inspection of structures using ground-penetrating radar;
  • Inspection and monitoring of structures using synthetic aperture radar (SAR);
  • Data processing.
Prof. Dr. Francisco Fernandes
Dr. Mezgeen Rasol
Prof. Dr. Gilda Schirinzi
Dr. Feng Zhou
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 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
  • Remote sensing
  • Synthetic aperture radar
  • Inspection
  • Structural health monitoring

Published Papers (11 papers)

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Editorial

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2 pages, 178 KiB  
Editorial
Editorial for the Special Issue “Radar Techniques for Structures Characterization and Monitoring”
by Francisco Fernandes, Mezgeen Rasol, Gilda Schirinzi and Feng Zhou
Remote Sens. 2023, 15(13), 3382; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15133382 - 03 Jul 2023
Cited by 1 | Viewed by 701
Abstract
This Special Issue focuses on the potential of radar-based remote techniques for characterizing and monitoring natural and building structures [...] Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)

Research

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22 pages, 11394 KiB  
Article
Application of 3D Laser Scanning Technology Using Laser Radar System to Error Analysis in the Curtain Wall Construction
by Jiehui Wang, Tianqi Yi, Xiao Liang and Tamon Ueda
Remote Sens. 2023, 15(1), 64; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15010064 - 23 Dec 2022
Cited by 8 | Viewed by 3377
Abstract
With the fast growth and rapid development of the construction industry, building design is not satisfied with only safety, accessibility, and habitability. People are requiring more multifunctional layouts and beautifully designed buildings. Thus, special and unique-shaped buildings with various curved curtain walls have [...] Read more.
With the fast growth and rapid development of the construction industry, building design is not satisfied with only safety, accessibility, and habitability. People are requiring more multifunctional layouts and beautifully designed buildings. Thus, special and unique-shaped buildings with various curved curtain walls have emerged more than ever in recent years. As for these curtain walls, it is difficult to perform the size measurement for panel design and calibration, as well as the on-site material cutting and assembly accurately and efficiently. The occurrence and continuous progress of 3D laser scanning technology combined with building information modeling (BIM) technology have been paid attention to and applied for curtain wall engineering to overcome this problem, particularly the construction-related progress, such as developed design and on-site installation. The 3D laser scanning technology can achieve fast and high-precision measurement by creating a “point cloud” dataset of the target building and its components, based on which an accurate as-built 3D BIM model of the scanned items can be established. By comparing and calibrating with the as-planned curtain wall design, engineers can update the real-time information (locations, shape, dimensions, etc.) for the following developed design and assembly production of the curtain wall. Compared to the conventional approach using manual locating and measurement, the progress of the curtain wall design and installation can be achieved in a more accurate and efficient manner by employing 3D laser scanning technology. Based on these considerations, in this present study, the basic concept, workflow, a case study with practical strategies of the application of 3D laser scanning technology in the curtain wall design and installation field, including the scanning operation, point cloud data acquisition and processing, 3D BIM model reconstruction, and relevant BIM model practice have been elaborated and discussed. Also, the 3D model that represents the actual construction condition established based on the point cloud data was used to compare with the originally designed BIM model. It was found discrepancies existed in the dimensions and positions between the as-built and as-designed BIM models, which can thus be used to revise the manufacture design and improve the installation plan of curtain walls. Furthermore, the difference, benefits, great significance of replacing conventional methods with 3D laser scanning technology, and instructions, limitations, recommendations for practical application, along with challenges and future directions open to research in the curtain wall construction field, were also presented in this work. Therefore, this work provides technical support to the application of 3D laser scanning technology and its combination with the BIM platform in the curtain wall construction. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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13 pages, 8010 KiB  
Article
A Multi-Path Encoder Network for GPR Data Inversion to Improve Defect Detection in Reinforced Concrete
by Yuanzheng Wang, Hui Qin and Feng Miao
Remote Sens. 2022, 14(22), 5871; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225871 - 19 Nov 2022
Cited by 3 | Viewed by 1489
Abstract
Ground penetrating radar (GPR) has been extensively used in the routine inspection of reinforced concrete structures. However, the signatures in GPR images are reflected electromagnetic waves rather than their actual shapes. The interpretation of GPR data is a mandatory but time- and labor-consuming [...] Read more.
Ground penetrating radar (GPR) has been extensively used in the routine inspection of reinforced concrete structures. However, the signatures in GPR images are reflected electromagnetic waves rather than their actual shapes. The interpretation of GPR data is a mandatory but time- and labor-consuming task. Furthermore, the rebars in the near-surface of concrete cause clutter in the GPR images, which hinders the interpretation of GPR data. This work presents a deep learning network to invert GPR B-scan images to permittivity maps of subsurface structures. The proposed network has a multi-path encoder which enables the network to leverage three kinds of GPR data: the original, migrated, and encoder–decoder-processed GPR data. Each type of processing method is designed to serve a different purpose: the original GPR images retain all the waveforms; the migration method intensifies the vertices of the subsurface anomalies; the encoder–decoder network suppresses rebar clutter and enhances the visibility of the defect echoes. The outputs of three processing methods are jointly used to interpret GPR B-scan images. We demonstrated the superiority of the proposed network by comparing it with a network with a single-path encoder. We also validated the proposed network with synthetic and experimental GPR data. The results indicate that the proposed network effectively reconstructs the defects in the reinforced concrete. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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12 pages, 722 KiB  
Article
Assessment of Material Layers in Building Walls Using GeoRadar
by Ildar Gilmutdinov, Ingrid Schlögel, Alois Hinterleitner, Peter Wonka and Michael Wimmer
Remote Sens. 2022, 14(19), 5038; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14195038 - 09 Oct 2022
Cited by 1 | Viewed by 1393
Abstract
Assessing the structure of a building with non-invasive methods is an important problem. One of the possible approaches is to use GeoRadar to examine wall structures by analyzing the data obtained from the scans. However, so far, the obtained data have to be [...] Read more.
Assessing the structure of a building with non-invasive methods is an important problem. One of the possible approaches is to use GeoRadar to examine wall structures by analyzing the data obtained from the scans. However, so far, the obtained data have to be assessed manually, relying on the experience of the user in interpreting GPR radargrams. We propose a data-driven approach to evaluate the material composition of a wall from its GPR radargrams. In order to generate training data, we use gprMax to model the scanning process. Using simulation data, we use a convolutional neural network to predict the thicknesses and dielectric properties of walls per layer. We evaluate the generalization abilities of the trained model on the data collected from real buildings. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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29 pages, 9691 KiB  
Article
Range-Ambiguous Clutter Suppression via FDA MIMO Planar Array Radar with Compressed Sensing
by Yuzhuo Wang, Shengqi Zhu, Lan Lan, Ximin Li, Zhixin Liu and Zhixia Wu
Remote Sens. 2022, 14(8), 1926; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081926 - 15 Apr 2022
Cited by 4 | Viewed by 1539
Abstract
Range-ambiguous clutter is an inevitable issue for airborne forward-looking array radars, especially with the high pulse repetition frequency (PRF). In this paper, a method to suppress the range-ambiguous clutter is proposed in an FDA-MIMO radar with a forward-looking planar array. Compressed sensing FDA [...] Read more.
Range-ambiguous clutter is an inevitable issue for airborne forward-looking array radars, especially with the high pulse repetition frequency (PRF). In this paper, a method to suppress the range-ambiguous clutter is proposed in an FDA-MIMO radar with a forward-looking planar array. Compressed sensing FDA technology is used to suppress the range-ambiguous clutter and the forward-looking non-uniformity short-range clutter of radar. Specifically, first, the range ambiguous clutter in different regions is separated by the characteristics of the planar array radar elevation dimension and FDA radar range coupling. Meanwhile, regarding the issue of the FDA radar main lobe moving between coherent pulses, a main lobe correction (MLC) algorithm proposes a solution for the issue, where the FDA radar cannot coherently accumulate signals in the case of non-full angle illumination. Finally, compressed sensing technology and elevation dimension filtering are utilized to suppress the range ambiguous clutter at the receiver, with the approach alleviating the range dependence of clutter in the observation region. A small number of clutter snapshots can obtain an approximately ideal clutter covariance matrix through compressed sensing sparse recovery. The method not only reduces the number of training samples, but also overcomes the problem of clutter non-uniformity in the forward-looking array. Therefore, the clutter suppression problems faced by the high repetition frequency airborne radar forward-looking array structure are solved. At the analysis stage, a comparison among the conventional MIMO and FDA methods is carried on by analyzing the improvement factor (IF) curves. Numerical results verify the effectiveness of the proposed method in range-ambiguous clutter suppression. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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15 pages, 1780 KiB  
Article
Radar Signal Intrapulse Modulation Recognition Based on a Denoising-Guided Disentangled Network
by Xiangli Zhang, Jiazhen Zhang, Tianze Luo, Tianye Huang, Zuping Tang, Ying Chen, Jinsheng Li and Dapeng Luo
Remote Sens. 2022, 14(5), 1252; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051252 - 04 Mar 2022
Cited by 12 | Viewed by 2482
Abstract
Accurate recognition of radar modulation mode helps to better estimate radar echo parameters, thereby occupying an advantageous position in the radar electronic warfare (EW). However, under low signal-to-noise ratio environments, recent deep-learning-based radar signal recognition methods often perform poorly due to the unsuitable [...] Read more.
Accurate recognition of radar modulation mode helps to better estimate radar echo parameters, thereby occupying an advantageous position in the radar electronic warfare (EW). However, under low signal-to-noise ratio environments, recent deep-learning-based radar signal recognition methods often perform poorly due to the unsuitable denoising preprocess. In this paper, a denoising-guided disentangled network based on an inception structure is proposed to simultaneously complete the denoising and recognition of radar signals in an end-to-end manner. The pure radar signal representation (PSR) is disentangled from the noise signal representation (NSR) through a feature disentangler and used to learn a radar signal modulation recognizer under low-SNR environments. Signal noise mutual information loss is proposed to enlarge the gap between the PSR and the NSR. Experimental results demonstrate that our method can obtain a recognition accuracy of 98.75% in the −8 dB SNR and 89.25% in the −10 dB environment of 12 modulation formats. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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16 pages, 6511 KiB  
Article
Detection and Characterization of Cracks in Highway Pavement with the Amplitude Variation of GPR Diffracted Waves: Insights from Forward Modeling and Field Data
by Shili Guo, Zhiwei Xu, Xiuzhong Li and Peimin Zhu
Remote Sens. 2022, 14(4), 976; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040976 - 17 Feb 2022
Cited by 13 | Viewed by 2328
Abstract
It is important to distinguish between two common defects, fatigue cracks and reflective cracks, and determine their locations (the top and bottom) in the highway pavement because they require individually targeted treatment measures. Ground Penetrating Radar (GPR) has the potential to detect cracks [...] Read more.
It is important to distinguish between two common defects, fatigue cracks and reflective cracks, and determine their locations (the top and bottom) in the highway pavement because they require individually targeted treatment measures. Ground Penetrating Radar (GPR) has the potential to detect cracks in the highway pavement due to the change of the electromagnetic properties of highway-pavement media, arising from the existences of cracks. By using a theoretical analysis and a numerical simulation, we compare the characteristics of corresponding radargrams, including the amplitude variation of diffracted waves with various models of presetting cracks inside the layered homogeneous media. For those fatigue cracks and reflective cracks extending to the road surface, the amplitude curves of direct ground wave can intuitively indicate the locations of the top of the cracks and qualitatively compare the width of these cracks. Furthermore, we find that the shape and pattern of diffraction hyperbolas of both types of cracks with bottoms at different locations are quite similar, but their amplitudes are significantly different. To be specific, for those cracks with the same width, the amplitude of diffracted waves generated by fatigue cracks is slightly higher than that generated by reflective cracks at the interface between the asphalt surface and the semi-rigid base layer. In contrast, the amplitude of the former is significantly lower than the latter at the interface between the semi-rigid base and the roadbed. We applied these findings to the interpretation of the field GPR data of a highway pavement in China, and successfully identified the locations of the cracks and corresponding types. Our model results and field results clearly show the efficiency of our findings in the detection of cracks for highway-pavement rehabilitation. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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19 pages, 1866 KiB  
Article
RCE-GAN: A Rebar Clutter Elimination Network to Improve Tunnel Lining Void Detection from GPR Images
by Yuanzheng Wang, Hui Qin, Yu Tang, Donghao Zhang, Donghui Yang, Chunxu Qu and Tiesuo Geng
Remote Sens. 2022, 14(2), 251; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020251 - 06 Jan 2022
Cited by 20 | Viewed by 2515
Abstract
Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this [...] Read more.
Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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21 pages, 7440 KiB  
Article
Experimental Research on Evaluation of Soil Water Content Using Ground Penetrating Radar and Wavelet Packet-Based Energy Analysis
by Sheng Zhang, Liang Zhang, Tonghua Ling, Guihai Fu and Youlin Guo
Remote Sens. 2021, 13(24), 5047; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245047 - 12 Dec 2021
Cited by 9 | Viewed by 2498
Abstract
Soil water content is one of the most important factors affecting the safety and stability of buildings or structures, especially in roadbeds, slopes, earth dams and foundations. Accurate assessments of soil water content can ensure the quality of construction, reduce construction costs and [...] Read more.
Soil water content is one of the most important factors affecting the safety and stability of buildings or structures, especially in roadbeds, slopes, earth dams and foundations. Accurate assessments of soil water content can ensure the quality of construction, reduce construction costs and prevent accidents, among other benefits. In this study, ground penetrating radar (GPR) was used to detect and evaluate changes in soil water content. The GPR signal is usually nonstationary and nonlinear; however, traditional Fourier theory is typically suitable for periodic stationary signals, and cannot reflect the law of the frequency and energy of the GPR signal changing with time. Wavelet transform has good time-frequency localization characteristics, and therefore represents a new method for analyzing and processing GPR signals. According to the time-frequency characteristics of GPR signals, in this paper, a new biorthogonal wavelet basis which was highly matched with the GPR waveform was constructed using the lifting framework of wavelet theory. Subsequently, an evaluation method, namely, the wavelet packet-based energy analysis (WPEA) method, was proposed. The method was utilized to calculate the wavelet packet-based energy indexes (WPEI) of the GPR single-channel signals for clay samples with water contents ranging from 10% to 24%. The research results showed that there was a highly correlated linear relationship between the WPEI and the soil water contents, and the relationship between the two was fitted with a linear fitting function. The feasibility of the method was verified by comparing our results with those obtained using classical wavelet bases to perform the wavelet packet transform. The large-area, continuous scanning measurement method of GPR was shown to be suitable for evaluations of soil water contents in roadbeds, slopes, earth dams, and foundations. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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16 pages, 4507 KiB  
Article
Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks
by Yunpeng Yue, Hai Liu, Xu Meng, Yinguang Li and Yanliang Du
Remote Sens. 2021, 13(22), 4590; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224590 - 15 Nov 2021
Cited by 25 | Viewed by 3490
Abstract
Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high [...] Read more.
Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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18 pages, 6383 KiB  
Technical Note
What Indicative Information of a Subsurface Wetted Body Can Be Detected by a Ground-Penetrating Radar (GPR)? A Laboratory Study and Numerical Simulation
by Ruiyan Wang, Tao Yin, Enlong Zhou and Bowen Qi
Remote Sens. 2022, 14(18), 4456; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184456 - 07 Sep 2022
Cited by 1 | Viewed by 1304
Abstract
Nondestructive and noninvasive visualization and quantification of soil wetted bodies (SWBs) is of great significance to the development of water-saving agriculture. Unfortunately, measuring the parameters of SWBs is difficult due to the invisibility of SWBs buried underneath the ground and the non-variability of [...] Read more.
Nondestructive and noninvasive visualization and quantification of soil wetted bodies (SWBs) is of great significance to the development of water-saving agriculture. Unfortunately, measuring the parameters of SWBs is difficult due to the invisibility of SWBs buried underneath the ground and the non-variability of the soil moisture under partial irrigation conditions. Therefore, we performed a laboratory experiment to investigate what SWB attributes can be detected by a GPR. In the laboratory, three typical partial irrigation experiments were conducted to collect the GPR data of SWBs of different sizes, shapes, and burial depths. Additionally, numerical simulation scenarios were designed according to the laboratory experiment. Then, the simulated and measured GPR data were processed by the FK migration method. Based on the simulation, a calibration model for the width of SWBs was constructed. We found that SWB attributes, such as type and location can be obtained from raw radargrams owing to the obvious reflection of the top and bottom interfaces. The results showed that estimating the depth and thickness of SWBs from FK migration radargrams is more reliable than from raw radargrams. Moreover, estimation of the width of SWBs relies on the FK migration radargrams. Our findings indicate that the size and depth of SWBs dominate the width detection accuracy, and the estimated width gained via the width calibration model is improved. Our results highlight the potential for using GPR data to detect SWBs, as well as the potential of using numerical simulation, FK migration, and calibration modeling in combination to extract the size information of SWBs from GPR radargrams. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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