An Attention U-Net-Based Improved Clutter Suppression in GPR Images
Swarna Laxmi Panda, Upendra Kumar Sahoo, Subrata Maiti, Pradipta Sasmal
Abstract
The existence of strong back-ground clutter often masks the desired target response, and thereby significantly affect the ground penetrating radar (GPR) target detection. This effect is even more pronounced for rough terrain and shallow buried targets. Therefore, it is essential to eliminate the clutter to facilitate the target detection. In this paper, a deep learning based attention U-Net model is proposed for clutter removal of GPR data. This technique integrates a channel attention module (CAM) and a spatial attention module (SAM) with a U-Net architecture to enhance the clutter removal performance. The proposed model implicitely learns to suppress irrelevant clutters while emphasizing the desired target. The effectiveness of the proposed clutter removal approach is validated on synthetic as well as measured data through visual inspection and quantitive evaluation.