GPR radargrams analysis through machine learning approach

F. Ponti, F. Barbuto, P. P. Di Gregorio, F. Frezza, F. Mangini, R. Parisi, P. Simeoni and M. Troiano

Journal of Electromagnetic Waves and Applications, 2021, vol. 35, issue 12, 1678-1686

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.

Date: 2021
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DOI: 10.1080/09205071.2021.1906329

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