This work proposes a Machine Learning (ML) approach for the analysis and classification of Ground Penetrating Radar (GPR) given a limited number of B-scan images. Specifically, we consider both a custom Convolutional Neural Network (CNN) and a wellestablished Deep Learning (DL) architecture, DenseNet, that is opportunely scaled down to take into account the small dataset. Those networks are then employed to classify B-scan simulations from buried cylinders in order to retrieve the host media permittivity, the cylinder depth respect to surface, and cylinders radius. A prediction based on the mean-square error (MSE) is applied. The main aim of the proposed work is to test the applicability of a scaled-down version of DenseNet architecture to the analysis of B-scan images and compare the performance respect to a classical CNN. The architecture chosen has shown interesting results in retrieving information from a limited data set. Limitations of the considered approach are also discussed.

GPR radargrams analysis through machine learning approach / Ponti, F.; Barbuto, F.; Di Gregorio, P. P.; Frezza, F.; Mangini, F.; Parisi, R.; Simeoni, P.; Troiano, M.. - In: JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS. - ISSN 0920-5071. - 35:12(2021), pp. 1678-1686. [10.1080/09205071.2021.1906329]

GPR radargrams analysis through machine learning approach

F. Ponti;F. Barbuto;P. P. Di Gregorio;F. Frezza;F. Mangini;R. Parisi;P. Simeoni;M. Troiano
2021

Abstract

This work proposes a Machine Learning (ML) approach for the analysis and classification of Ground Penetrating Radar (GPR) given a limited number of B-scan images. Specifically, we consider both a custom Convolutional Neural Network (CNN) and a wellestablished Deep Learning (DL) architecture, DenseNet, that is opportunely scaled down to take into account the small dataset. Those networks are then employed to classify B-scan simulations from buried cylinders in order to retrieve the host media permittivity, the cylinder depth respect to surface, and cylinders radius. A prediction based on the mean-square error (MSE) is applied. The main aim of the proposed work is to test the applicability of a scaled-down version of DenseNet architecture to the analysis of B-scan images and compare the performance respect to a classical CNN. The architecture chosen has shown interesting results in retrieving information from a limited data set. Limitations of the considered approach are also discussed.
GPR; machine learning; neural network; classification
01 Pubblicazione su rivista::01a Articolo in rivista
GPR radargrams analysis through machine learning approach / Ponti, F.; Barbuto, F.; Di Gregorio, P. P.; Frezza, F.; Mangini, F.; Parisi, R.; Simeoni, P.; Troiano, M.. - In: JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS. - ISSN 0920-5071. - 35:12(2021), pp. 1678-1686. [10.1080/09205071.2021.1906329]
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1653177
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact