A Data-Efficient Deep Learning Method for Rough Surface Clutter Reduction in GPR Images

In ground penetrating radar (GPR) B-scan images, ground surface clutter is the main source of interference, often obscuring or distorting subsurface target signals. We propose a deep autoencoder-based method to mitigate rough surface clutter in GPR images by treating it as an anomaly detection problem. First, the rough surface region in a B-scan image is partitioned into small patches, which act as the training dataset for the deep autoencoder. Through the training process, the autoencoder learns and captures the patterns associated with the rough surface patches. Following training, the entire B-scan image is divided into small patches of the same size as the training patches, and each of them is fed into the autoencoder to compute an anomaly score. To reconstruct a clutter-reduced B-scan image, we employ a weighted sum approach to aggregate all patches based on their anomaly scores. We evaluate our method against conventional subspace projection techniques using simulated and field-collected B-scans. The results clearly indicate that our approach surpasses these subspace methods. Furthermore, we employ t-distributed stochastic neighbor embedding (t-SNE) analysis to gain deeper insights into our method's effectiveness in reducing rough surface clutter. The outcomes of this analysis reinforce the practical viability of our approach for GPR image processing.