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Remote Sensing
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Latest Results on GPR Algorithms, Applications and Systems
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Latest Results on GPR Algorithms, Applications and Systems

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 (31 July 2022) | Viewed by 10690

Special Issue Editors

Dr. Sebastiano D’Amico

E-Mail Website
Guest Editor
Department of Geosciences, University of Malta, MSD2080 Msida, Malta
Interests: applied geophysics; forensic geosciences; scientific methods applied to cultural heritage; geomatics and remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Raffaele Persico

E-Mail Website
Guest Editor
Department of Environmental Engineering DIAM, University of Calabria, 87036 Rende, Italy
Interests: ground penetrating radar; electromagnetic wave propagation; magnetic permeability; permittivity; frequency-domain analysis; geophysical techniques; radar imaging; remote sensing by radar; time-domain
Special Issues, Collections and Topics in MDPI journals
Dr. Mercedes Solla

E-Mail Website
Guest Editor
GeoTECH Research Group, CINTECX, Universidade de Vigo, 36310 Vigo, Spain
Interests: ground penetrating radar; signal processing; numerical modeling; civil and environmental engineering; cultural heritage; archaeology; geographic information systems (GIS)
Special Issues, Collections and Topics in MDPI journals
Dr. Craig Warren

E-Mail Website
Guest Editor
Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
Interests: computational modelling; optimisation of electromagnetic (EM) sensing systems, such as Ground Penetrating Radar (GPR)

Special Issue Information

Dear Colleague,

After a pause due to the pandemic, the scientific community working on GPR issues is going to restart exchanging ideas and looking toward innovative solutions. A constant dialogue between academia and companies traditionally accompanies this progress, which has over the years transformed the systems, led to new applications beyond the most traditional ones, and introduced deeper and deeper studies into data processing. The next IWAGPR conference, to be held in Valletta, Malta, on 17–20 October, 2021, will be a meaningful occasion for re-launching this ever-stimulating debate. Associated to the conference, the present Special Issue in Remote Sensing is proposed, in which the authors of contributions to IWAGPR will have the possibility to expand their work and publish them in an open access format.

The Special Issue is aimed at works that introduce any innovation in the state-of-the-art of either GPR systems (antennas, electronics, positioning systems, etc.), or some novelty regarding data gathering and processing (filtering procedures, migration, nonlinear algorithms, particular measurement configurations) or some innovation possibly introduced in the applications, which can be either “classical” such as archaeology, pavement monitoring, tunneling, etc. or more recent and particular, such as monitoring of irrigation, applications with drones, and so on.

In particular, in this Special Issue, we do not look for mere case histories, but we are aware that from case histories, some interesting tips can often arrive because of unexpected problems arising in the field explored. Contributions from participants of the IWAGPR2021 as well as external scientists are equally welcome.

Dr. Sebastiano D’Amico
Prof. Raffaele Persico
Dr. Mercedes Solla
Dr. Craig Warren
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 Geophysics Antennas

Published Papers (6 papers)

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11 pages, 4871 KiB  
Communication
GPR Image Clutter Suppression Using Gaussian Curvature Decomposition in the PCA Domain
by Qibin Su, Beizhen Bi, Pengyu Zhang, Liang Shen, Xiaotao Huang and Qin Xin
Remote Sens. 2022, 14(19), 4879; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194879 - 29 Sep 2022
Cited by 4 | Viewed by 1250
Abstract
Ground penetrating radar (GPR) is one of the most generally used underground sensing equipment, but it is frequently contaminated by clutter and noise during data acquisition, which has a significant impact on the detection performance of buried targets. The purpose of this letter [...] Read more.
Ground penetrating radar (GPR) is one of the most generally used underground sensing equipment, but it is frequently contaminated by clutter and noise during data acquisition, which has a significant impact on the detection performance of buried targets. The purpose of this letter is to present a novel clutter suppression method based on the principal component Gaussian curvature decomposition (PCGCD). First, the GPR B-scan data are divided into different sub-components using principal component analysis (PCA). Then, a Gaussian curvature decomposition (GCD) method is proposed, which can be applied to PCA domain subspaces to recover more target structure information from random noise. The PCGCD method’s performance is evaluated using both numerical simulation and real-world GPR datasets. The visualization and quantitative results demonstrated our method’s superiority in protecting the underground target structure, removing complex random noise, and improving the detection ability of buried targets. Full article
(This article belongs to the Special Issue Latest Results on GPR Algorithms, Applications and Systems)
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22 pages, 10637 KiB  
Article
Source-Independent Waveform Inversion Method for Ground Penetrating Radar Based on Envelope Objective Function
by Xintong Liu, Sixin Liu, Chaopeng Luo, Hejun Jiang, Hong Li, Xu Meng and Zhihui Feng
Remote Sens. 2022, 14(19), 4878; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194878 - 29 Sep 2022
Cited by 1 | Viewed by 1161
Abstract
For the full waveform inversion, it is necessary to provide an accurate source wavelet for forwarding modeling in the iteration. The source wavelet estimation method based on deconvolution technology can solve this problem to some extent, but we find that the estimated source [...] Read more.
For the full waveform inversion, it is necessary to provide an accurate source wavelet for forwarding modeling in the iteration. The source wavelet estimation method based on deconvolution technology can solve this problem to some extent, but we find that the estimated source wavelet is not accurate and needs to be manually corrected repeatedly in the iteration. This process is highly operator-intensive, and the update process is time-consuming and increases the potential for errors. We propose a source-independent waveform inversion (SIEWI) scheme for cross-hole GPR data, and use the envelope objective function combined with this method to effectively reduce the nonlinearity of inversion. The residual field used by SIEWI to construct the gradient inherits the characteristics of the envelope wavefield. Compared with full waveform inversion (FWI), SIEWI is more robust and less sensitive to frequency components and inaccurate source wavelet. To avoid cycle jumping, the multi-scale strategy effectively utilizes the properties of convolutional wavefields. In one iteration, the wavefield is decomposed into multiple frequency bands through multiple convolutions in the time domain to construct a multi-scale inversion strategy that preferentially inverts low-frequency information. Full article
(This article belongs to the Special Issue Latest Results on GPR Algorithms, Applications and Systems)
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18 pages, 7275 KiB  
Article
Wavefield Reconstruction Inversion Based on the Multi-Scale Cumulative Frequency Strategy for Ground-Penetrating Radar Data: Application to Urban Underground Pipeline
by Deshan Feng, Siyuan Ding, Xun Wang, Xuan Su, Shuo Liu and Cen Cao
Remote Sens. 2022, 14(9), 2162; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092162 - 30 Apr 2022
Cited by 2 | Viewed by 1613
Abstract
High-precision detection of the underground pipelines is an indispensable part of the development and construction of cities. At present, the inversion technology for ground-penetrating radar (GPR) data is an effective means of realizing shallow-underground-space visualization in the field of geophysical exploration. However, the [...] Read more.
High-precision detection of the underground pipelines is an indispensable part of the development and construction of cities. At present, the inversion technology for ground-penetrating radar (GPR) data is an effective means of realizing shallow-underground-space visualization in the field of geophysical exploration. However, the traditional full-waveform inversion (FWI) method usually faces the problems of strong nonlinearity of the objective function, high dependence on the initial model, and huge calculation cost. For improving the accuracy and efficiency of GPR data inversion, a wavefield reconstruction inversion (WRI) strategy is used for GPR data imaging to reduce the nonlinearity of the inversion problem and the dependence on the initial model. Then, the frequency weighting strategy and the multi-scale method are adopted to avoid the high-frequency component data dominating the optimization process and enhance the stability of inversion. In this paper, two numerical experiments of pipeline models with different materials and spacing or buried depths verified that the proposed method can effectively reconstruct the subsurface pipelines, and further performance of our algorithm on the field data verified the reliability of high-precision imaging of urban underground pipelines, which shows great potential of application in the field of high-precision detection of the urban underground pipelines. Full article
(This article belongs to the Special Issue Latest Results on GPR Algorithms, Applications and Systems)
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30 pages, 7516 KiB  
Article
A New Methodology for the Detection and Extraction of Hyperbolas in GPR Images
by Klaudia Onyszko and Anna Fryśkowska-Skibniewska
Remote Sens. 2021, 13(23), 4892; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234892 - 02 Dec 2021
Cited by 4 | Viewed by 2137
Abstract
Reliable detection of underground infrastructure is essential for infrastructure modernization works, the implementation of BIM technology, and 3D cadasters. This requires shortening the time of data interpretation and the automation of the stage of selecting the objects. The main factor that influences the [...] Read more.
Reliable detection of underground infrastructure is essential for infrastructure modernization works, the implementation of BIM technology, and 3D cadasters. This requires shortening the time of data interpretation and the automation of the stage of selecting the objects. The main factor that influences the quality of radargrams is noise. The paper presents the method of data filtration with use of wavelet analyses and Gabor filtration. The authors were inspired to conduct the research by the fact that the interpretation and analysis of radargrams is time-consuming and by the wish to improve the accuracy of selection of the true objects by inexperienced operators. The authors proposed automated methods for the detection and classification of hyperboles in GPR images, which include the data filtration, detection, and classification of objects. The proposed object classification methodology based on the analytic hierarchy process method introduces a classification coefficient that takes into account the weights of the proposed conditions and weights of the coefficients. The effectiveness and quality of detection and classification of objects in radargrams were assessed. The proposed methods make it possible to shorten the time of the detection of objects. The developed hyperbola classification coefficients show promising results of the detection and classification of objects. Full article
(This article belongs to the Special Issue Latest Results on GPR Algorithms, Applications and Systems)
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12 pages, 20054 KiB  
Technical Note
A Numerical Investigation of the Dispersion Law of Materials by Means of Multi-Length TDR Data
by Raffaele Persico, Iman Farhat, Lourdes Farrugia and Charles Sammut
Remote Sens. 2022, 14(9), 2003; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092003 - 21 Apr 2022
Cited by 3 | Viewed by 1204
Abstract
In this paper, we propose a method for retrieving the dispersion law of a material under test from multi-length TDR measurements in reflection mode, repeated at several frequencies. By replacing the multi-frequency measurements with measurements using multi-length TDR probe, it is possible to [...] Read more.
In this paper, we propose a method for retrieving the dispersion law of a material under test from multi-length TDR measurements in reflection mode, repeated at several frequencies. By replacing the multi-frequency measurements with measurements using multi-length TDR probe, it is possible to retrieve the complex equivalent permittivity of the material in a frequency band of interest. The proposed procedure does not require a priori knowledge of the type of dispersion law of the material, which instead can possibly be inferred from the measured data. The algorithm is validated using numerically simulated data obtained with the commercial code CST Microstudio®. Full article
(This article belongs to the Special Issue Latest Results on GPR Algorithms, Applications and Systems)
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16 pages, 10559 KiB  
Technical Note
Local Freeman Decomposition for Robust Imaging and Identification of Subsurface Anomalies Using Misaligned Full-Polarimetric GPR Data
by Haoqiu Zhou, Xuan Feng, Zejun Dong, Cai Liu, Wenjing Liang and Yafei An
Remote Sens. 2022, 14(3), 804; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030804 - 08 Feb 2022
Cited by 6 | Viewed by 1853
Abstract
A full-polarimetric ground penetrating radar (FP-GPR) uses an antenna array to detect subsurface anomalies. Compared to the traditional GPR, FP-GPR can obtain more abundant information about the subsurface. However, in field FP-GPR measurements, the arrival time of the received electromagnetic (EM) waves from [...] Read more.
A full-polarimetric ground penetrating radar (FP-GPR) uses an antenna array to detect subsurface anomalies. Compared to the traditional GPR, FP-GPR can obtain more abundant information about the subsurface. However, in field FP-GPR measurements, the arrival time of the received electromagnetic (EM) waves from different channels cannot be strictly aligned due to the limitations of human operation errors and the craftsmanship of the equipment. Small misalignments between the radargrams acquired from different channels of an FP-GPR can lead to erroneous identification results of the classic Freeman decomposition (FD) method. Here, we propose a local Freeman decomposition (LFD) method to enhance the robustness of the classic FD method when managing with misaligned FP-GPR data. The tests on three typical targets demonstrate that misalignments will severely interfere with the imaging and the identification results of the classic FD method for the plane and dihedral scatterers. In contrast, the proposed LFD method can produce smooth images and accurate identification results. Besides, the identification of the volume scatterer is not affected by misalignments. A test of ice-fracture detection further verifies the capability of the LFD method in field measurements. Due to the different relative magnitudes of the permittivity of the media on two sides of the interfaces, the ice surface and ice fracture show the features of surface-like and double-bounce scattering, respectively. However, the definition of double-bounce scattering is different from the definition in polarimetric synthetic aperture radar (SAR). Finally, a quantitative analysis shows that the sensitivities of the FD and LFD methods to misalignments are related to both the type of target and the polarized mode of the misaligned data. The tolerable range of the LFD method for misalignments is approximately ±0.2 times the wavelength of the EM wave, which is much wider than that of the FD method. In most cases, the LFD method can guarantee an accurate result of identification. Full article
(This article belongs to the Special Issue Latest Results on GPR Algorithms, Applications and Systems)
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