A novel method for GPR imaging based on neural networks and dictionary learning

In this paper, we propose a novel framework to reconstruct image of a buried object from the ground-penetrating radar (GPR) reflection hyperbolas. The first reflection hyperbola of a buried object determines its relative permittivity and shape class (circle, rectangle, and triangle). The first two reflections are used to reconstruct image of a homogeneous buried object. After five preprocessing steps to extract region of interest of b-scan corresponding to a buried object, a Convolutional Neural Network (CNN) estimates the relative permittivity value of the buried object. Then, two novel schemes, including CNN and Dictionary Learning (DL) methods are proposed to reconstruct 2D image of the buried object. Image reconstructors are trained with a dataset containing buried objects with different shapes, sizes, and reference relative permittivity. Since two objects having the same size but different materials lead to different hyperbola reflections in their B-scans, we propose a novel transformation to convert the reflection scans from the estimated relative permittivity to a reference relative permittivity so that the image reconstructors can be used to rebuild images. The results reveal that the DL method outperforms the CNN method on noisy measurements, while the CNN method performs better when dealing with fewer traces.