Ground penetrating radar data imaging via Kirchhoff migration method

Liu, X.; Serhir, Mohammed; Kameni, Abelin; Lambert, Marc; Pichon, Lionel

doi:10.23919/ropaces.2017.7916395

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…The techniques considered in this study were the following: Stripmap Synthetic Aperture Radar (SAR), Kirchhoff Migration (KM) and Frequency Wavenumber (FK). The techniques in question were subsequently implemented and used in focusing the GPR images collected during the measurement campaign in the reference church (Liu, Serhir, Kameni, Lambert, & Pichon, 2017).…”

Section: Migration Algorithmsmentioning

confidence: 99%

Integration of geomatics methodologies and creation of a cultural heritage app using augmented reality

Barrile

Fotia

Bilotta

et al. 2019

Virtual archaeol. rev.

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3D modelling of archaeological and historical structures is the new frontier in the field of conservation science. Similarly, the identification of buried finds, which enhances their multimedia diffusion and restoration, has gained relevance. As such sites often have a high level of structural complexity and complicated territorial geometries, accuracy in the creation of 3D models and the use of sophisticated algorithms for georadar data analysis are crucial. This research is the first step in a larger project aimed at reclaiming the ancient villages located in the Greek area of southern Italy. The present study focuses on the restoration of the village of Africo (RC), a village hit by past flooding. The survey began with a laser scan of the church of St. Nicholas, using both the Faro Focus3D and the Riegl LMS-Z420i laser scanner. At the same time, georadar analyses were carried out in order to pinpoint any buried objects. In the processing phase, our own MATLAB algorithms were used for both laser scanner and georadar datasets and the results compared with those obtained from the scanners’ respective proprietary software. We are working to develop a tourism app in both augmented and virtual reality environments, in order to disseminate and improve access to cultural heritage. The app allows users to see the 3D model and simultaneously access information on the site integrated from a variety of repositories. The aim is to create an immersive visit, in this case, to the church of St. Nicholas.Highlights:<ul><li>Use of different algorithms for registration of terrestrial laser scans and analysis of the data obtained.</li><li>3D acquisition, processing and restitution methodology from georadar data.</li><li>Implementation of a tourist app in both virtual and augmented reality by integrating geomatics methodologies.</li></ul>

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Section: Migration Algorithmsmentioning

confidence: 99%

Integration of geomatics methodologies and creation of a cultural heritage app using augmented reality

Barrile

Fotia

Bilotta

et al. 2019

Virtual archaeol. rev.

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“…Kirchhoff migration method [7] is first introduced in 1970s, [8] tests Kirchhoff migration method by using both synthetic and real GPR data, the results show that though the localization of the buried targets could be achieved, the information about target shape and extension are not provided by implementing this method. In the meanwhile, [9] represents that Kirchhoff migration method is capable to focus on the target position, however, it's processing speed is slower than the rest of migration approaches.…”

Section: Related Workmentioning

confidence: 99%

GPR-based Model Reconstruction System for Underground Utilities Using GPRNet

Feng¹,

Yang²,

Hoxha³

et al. 2020

Preprint

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Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) instruments to detect and locate underground objects (i.e. rebars, utility pipes). Many of the previous researches focus on GPR imagebased feature detection only, and none can process sparse GPR measurements to successfully reconstruct a very fine and detailed 3D model of underground objects for better visualization. To address this problem, this paper presents a novel robotic system to collect GPR data, localize the underground utilities, and reconstruct the underground objects' dense point cloud model. This system is composed of three modules: 1) visual-inertial-based GPR data collection module which tags the GPR measurements with positioning information provided by an omnidirectional robot; 2) a deep neural network (DNN) migration module to interpret the raw GPR B-scan image into a cross-section of object model; 3) a DNN-based 3D reconstruction module, i.e., GPRNet, to generate underground utility model with the fine 3D point cloud. The experiments show that our method can generate a dense and complete point cloud model of pipe-shaped utilities based on a sparse input, i.e., GPR raw data, with various levels of incompleteness and noise. The experiment results on synthetic data as well as field test data verified the effectiveness of our method.

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“…These methods are known to provide good results to reconstruct the shapes and locations of unknown inhomogeneities when using enough incident fields [10,11,12,13], but might fail when not as exemplified in [12]. Practically speaking, retrieving the information about the inhomogeneities with only a few number of incident fields is an important issue for various applications such as Synthetic Aperture Radar (SAR) imaging [14,15,16,17] and Ground Penetrating Radar (GPR) [18,19,20], etc.…”

Section: Introductionmentioning

confidence: 99%

“…where α := max {α m , m = 1, 2 • • • , M } for a generic incident field E i ∞ (r m ,ŷ). Introducing the far-field pattern of the dyadic Green function (16) and the expression of plane wave field due to the dipole (17) in (19) leads to…”

Section: Introductionmentioning

confidence: 99%

Structure analysis of direct sampling method in 3D electromagnetic inverse problem: near- and far-field configuration

Kang

Lambert

2021

Inverse Problems

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In this paper, we study the structure analysis of the Direct Sampling Method (DSM) in the threedimensional inverse electromagnetic scattering problem. Even though the DSM is well-known to the robust, fast, and efficient non-iterative type algorithm to estimate the support and shape of unknown inhomogeneities from scattered data, the inhomogeneities might not be detected in the DSM with improper test polarization. To explain the reason for the phenomenon, we carefully analyze the indicator function of DSM using the asymptotic formula of the scattered field under a small volume hypothesis of well-separated inhomogeneities. The analytic representation formula of the indicator function of DSM is presented by establishing the relationship of spherical Bessel functions, the polarization of the incident wave and the test dipole, and the polarization tensor of the inhomogeneities, which depends on their sizes, permittivity, etc. Through such theoretical results, we also show the validity of the guideline to choose the test polarization given by other work. Furthermore, we propose our method to select the polarization of the test dipole for better efficiency. With a similar path of derivation, we also introduce and analyze the indicator function of DSM in far-field configurations. Various numerical simulations with synthetic and experimental data validate our theoretical results and proposals.

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Ground penetrating radar data imaging via Kirchhoff migration method

Cited by 10 publications

References 8 publications

Integration of geomatics methodologies and creation of a cultural heritage app using augmented reality

Integration of geomatics methodologies and creation of a cultural heritage app using augmented reality

GPR-based Model Reconstruction System for Underground Utilities Using GPRNet

Structure analysis of direct sampling method in 3D electromagnetic inverse problem: near- and far-field configuration

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