Clutter Suppression in GPR B-Scan Images Using Robust Autoencoder

Ground-penetrating radar (GPR) is a well-known geophysical electromagnetic method used to detect the underground facilities such as landmines, pipelines, and cavities. In general, the clutter presented in GPR B-scan image obscures the underground objects, thus damaging the performance of the underground object detection algorithm. In this letter, we proposed a new clutter suppression method based on robust autoencoder (RAE). The proposed algorithm decomposes a GPR B-scan image into its low-rank and sparse components. The low-rank component catches the clutter, whereas the sparse component captures the underground object responses. The commonly used clutter removal algorithms, mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and morphological component analysis (MCA), are compared with the proposed algorithm on both the numerical simulated data and real GPR data. The visual and quantitative results demonstrate the effectiveness of the proposed RAE-based algorithm over the widely used state-of-the-art clutter removal algorithms.

Automated summary

This letter proposes a new clutter suppression method based on robust autoencoder (RAE) for ground-penetrating radar (GPR). The method decomposes a GPR B-scan image into low-rank and sparse components to capture clutter and underground object responses respectively. The results show that the proposed RAE-based algorithm outperforms commonly used clutter removal algorithms.