Removal of Clutter and Random Noise for GPR Images

Abstract

Ground Penetrating Radar (GPR) is one of the most efficient non-invasive geophysical methods for detecting subsurface anomalies, with many applications including landmine, cable, and pipe detection. GPR has the advantage of being able to detect both metallic and nonmetallic buried objects. However, clutters and noise can make it difficult to detect shallowly buried explosive devices. These effects must be removed to extract the target signature successfully. This article employs gradient magnitude with thresholding along with wavelet-based denoising for this purpose. Although gradient with thresholding-based techniques can effectively eliminate clutters such as antenna crosstalk and ground bounce, they cannot completely eliminate random noise. The random noise effects are then removed using wavelet-based denoising. To test the implemented methods, experimental GPR data collected in a facilitated laboratory environment and an accurate dataset offered by the electromagnetic software simulation tool gprMax are used. The results show that the proposed method outperforms traditional methods like mean removal and singular value decomposition (SVD) techniques in terms of peak signal to noise ratio (PSNR) and image entropy.