Publication

Abstract :

The presence of clutter in ground-penetrating radar (GPR) images obscures or distorts the responses of subsurface targets, significantly impacting the accuracy of detecting and identifying targets. Current clutter removal approaches often result in residual clutter or deformation of target responses, especially when confronted with intricate and uneven clutter in real-world GPR signatures. To address this issue in clutter removal under realistic conditions, this research introduces the spatial attention-based convoluted gazelle neural network (SACGNN). SACGNN is trained on a large-scale dataset designed to reflect real-time conditions and incorporates a residual block into its spatial attention-based architecture. This integration enhances its ability to suppress clutter and restore target reflections on surfaces like homogeneous, rough, water, and grass. Tuning the SACGNN using the gazelle optimization algorithm (GOA) excels in clutter removal by offering faster convergence, maintaining a balanced exploration–exploitation trade-off, and enhancing robustness across various data patterns.