Automatic Material Classification of Targets from GPR Data using Artificial Neural Networks

Abstract

Ground penetrating radar (GPR) is a preferred non-destructive method to study and identify buried objects in the field of geology, civil engineering, archaeology, military, etc. Landmines are now largely composed of plastic and other non-metallic materials, while archaeologists must deal with buried artefacts such as ceramics, pillars, and walls built of a range of materials. As a result, understanding the material properties of buried artefacts is critical. This study presents an ANN model for automatic classification of buried objects from GPR A-Scan data. The proposed ANN model is trained and validated using a synthetic dataset generated using gprMax. The model performs well in classifying three different object classes of aluminium, iron and limestone, while achieving an overall accuracy of 95%.