A novel method to remove GPR background noise based on the similarity of non-neighboring regions

Ground penetrating radar (GPR) is a non-destructive technique that has been widely used in many areas of research, such as landmine detection or subsurface anomalies, where it is required to locate targets embedded within a background medium. One of the major challenges in the research of GPR data remains the improvement of the image quality of stone materials by means of detection of true anisotropies since most of the errors are caused by an incorrect interpretation by the users. However, it is complicated due to the interference of the horizontal background noise, e.g., the air-ground interface, that reduces the high-resolution quality of radargrams. Thus, weak or deep anisotropies are often masked by this type of noise. In order to remove the background noise obtained by GPR, this work proposes a novel background removal method assuming that the horizontal noise shows repetitive two-dimensional regions along the movement of the GPR antenna. Specifically, the proposed method, based on the non-local similarity of regions over the distance, computes similarities between different regions of the same depth in order to identify most repetitive regions using a criterion to avoid closer regions. Evaluations are performed using a set of synthetic and real GPR data. Experimental results show that the proposed method obtains promising results compared to the classic background removal techniques and the most recently published background removal methods.