GPR radargrams analysis through machine learning approach

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.

Keywords:

• GPR
• machine learning
• neural network
• classification

Disclosure statement

No potential conflict of interest was reported by the author(s).