Hyperbolic Attention-Driven Deep Networks for Enhanced GPR Imaging of Underground Pipelines

In the context of ground-penetrating radar (GPR) surveys for underground engineering and pipeline identification, the processing of electromagnetic reflection data is pivotal for interpreting survey outcomes. The presence of substantial random noise and clutter within these data significantly complicates the imaging process. Despite the advancements brought by deep networks in GPR imaging technology, there remains a pressing need for more targeted techniques to enhance imaging reliability. This study introduces attention-driven deep network designed to enhance the perception of underground pipeline reflection features. The proposed network employs a dual-generative adversarial network (GAN) architecture: hyperbolic extraction (HE) GAN and target pipeline imaging GAN. The HE GAN leverages ResNet as the base model and utilizes localized perception hyperbolic attention to extract high-resolution hyperbolic waves. Meanwhile, the target pipeline imaging GAN, configured with U-Net and driven by rectified hyperbolic attention (RHA), incorporates multilevel attention mechanisms with skip connections to better capture and preserve fine details within the data. Experimental results demonstrate that the localized perception hyperbolic attention mechanism significantly enhances the response to hyperbolic wave features, effectively isolating these features while mitigating clutter and noise interference, thereby improving the reliability. RHA improves the accuracy of the pipeline imaging process.