In this paper, a Machine Learning (ML), and more specifically, a Deep Learning (DL) approach, is applied to the resolution of a typical electromagnetic problem such as the analysis and classification of Ground Penetrating Radar (GPR) radargrams. In particular, the study employs a DL architecture, known as DenseNet, to classify a set of radargrams, generated through the gprMax simulation software, and representing the scattering from perfect electric conductor (PEC) cylinders of infinite length, buried in various media, at different depths, and with different radius amplitudes. The network was trained to be able to extract the information hidden in the images through a multi-label approach. The objective of this study is both the generation of a dataset that can be employed to train DL algorithms and the exploration of the suitability of the DenseNet architecture to the classification of adargrams. The study has shown interesting results in terms of the ability of the DenseNet in extracting multi-label informations from adargrams given a small set of images. Limits of the approach considered are also highlighted and addressed.

Deep Learning for applications to Ground Penetrating Radar and electromagnetic diagnostic / Ponti, Francesca; Barbuto, F.; Di Gregorio, Pietro Paolo; Mangini, Fabio; Simeoni, Patrizio; Troiano, Maurizio; Frezza, Fabrizio. - (2019), pp. 547-551. ((Intervento presentato al convegno PIERS 2019 tenutosi a Roma.

Deep Learning for applications to Ground Penetrating Radar and electromagnetic diagnostic

Ponti, Francesca;Di Gregorio, Pietro Paolo;Mangini, Fabio;Simeoni, Patrizio;Troiano, Maurizio;Frezza, Fabrizio
2019

Abstract

In this paper, a Machine Learning (ML), and more specifically, a Deep Learning (DL) approach, is applied to the resolution of a typical electromagnetic problem such as the analysis and classification of Ground Penetrating Radar (GPR) radargrams. In particular, the study employs a DL architecture, known as DenseNet, to classify a set of radargrams, generated through the gprMax simulation software, and representing the scattering from perfect electric conductor (PEC) cylinders of infinite length, buried in various media, at different depths, and with different radius amplitudes. The network was trained to be able to extract the information hidden in the images through a multi-label approach. The objective of this study is both the generation of a dataset that can be employed to train DL algorithms and the exploration of the suitability of the DenseNet architecture to the classification of adargrams. The study has shown interesting results in terms of the ability of the DenseNet in extracting multi-label informations from adargrams given a small set of images. Limits of the approach considered are also highlighted and addressed.
PIERS 2019
machine learning; deep learning; gpr
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Deep Learning for applications to Ground Penetrating Radar and electromagnetic diagnostic / Ponti, Francesca; Barbuto, F.; Di Gregorio, Pietro Paolo; Mangini, Fabio; Simeoni, Patrizio; Troiano, Maurizio; Frezza, Fabrizio. - (2019), pp. 547-551. ((Intervento presentato al convegno PIERS 2019 tenutosi a Roma.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1291306
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