New Robust Sparse Convolutional Coding Inversion Algorithm for Ground Penetrating Radar Images

In this article, we propose two algorithms to enhance the interpretability of the hyperbola in B-scans obtained with a ground penetrating radar (GPR). These hyperbolas are the responses of buried objects or cavities. To correctly detect and classify them, denoising is typically necessary for GPR images as the signal-to-noise ratio (SNR) is low, and the various interfaces naturally present in the Earth have a strong response. Both algorithms are based on a sparse convolutional coding model plus a low-rank component. It is solved through an alternating direction method of multipliers (ADMM) framework. In order to take into account the presence of outliers and the artifacts caused by the acquisition, the second algorithm is based on the Huber norm instead of the classic L-2 -norm. These algorithms are tested on a real dataset labeled by geophysicists. The results show the denoising efficiency of this approach, and in particular the robustness of the second algorithm.

Automated summary

In this article, two algorithms are proposed to improve the interpretability of hyperbolas in B-scans obtained with ground penetrating radar (GPR). These algorithms, based on a sparse convolutional coding model and a low-rank component, are solved using the alternating direction method of multipliers (ADMM) framework. The second algorithm, based on the Huber norm, is designed to handle outliers and artifacts caused by the acquisition process. Experimental results on a real dataset demonstrate the denoising efficiency and robustness of the proposed approach.