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Remote Sens. 2017, 9(10), 980; doi:10.3390/rs9100980

Two-Dimensional Linear Inversion of GPR Data with a Shifting Zoom along the Observation Line

1
Institute for Archaeological and Monumental Heritage IBAM-CNR, Lecce 73100, Italy
2
International Telematic University Uninettuno UTIU, Rome 00186, Italy
3
Institute for the Electromagnetic Sensing of the Environment IREA-CNR, Napoli 80124, Italy
4
Earth and Life Institute, Environmental Sciences, Université catholique de Louvain, Louvain-la-Neuve 1348, Belgium
*
Author to whom correspondence should be addressed.
Received: 23 May 2017 / Revised: 9 September 2017 / Accepted: 13 September 2017 / Published: 22 September 2017
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
View Full-Text   |   Download PDF [2771 KB, uploaded 27 September 2017]   |  

Abstract

Linear inverse scattering problems can be solved by regularized inversion of a matrix, whose calculation and inversion may require significant computing resources, in particular, a significant amount of RAM memory. This effort is dependent on the extent of the investigation domain, which drives a large amount of data to be gathered and a large number of unknowns to be looked for, when this domain becomes electrically large. This leads, in turn, to the problem of inversion of excessively large matrices. Here, we consider the problem of a ground-penetrating radar (GPR) survey in two-dimensional (2D) geometry, with antennas at an electrically short distance from the soil. In particular, we present a strategy to afford inversion of large investigation domains, based on a shifting zoom procedure. The proposed strategy was successfully validated using experimental radar data. View Full-Text
Keywords: ground-penetrating radar (GPR); linear inverse scattering problem; large scale surveys ground-penetrating radar (GPR); linear inverse scattering problem; large scale surveys
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Persico, R.; Ludeno, G.; Soldovieri, F.; De Coster, A.; Lambot, S. Two-Dimensional Linear Inversion of GPR Data with a Shifting Zoom along the Observation Line. Remote Sens. 2017, 9, 980.

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