SCB-ADAE: An attention-based deep autoencoder for ground penetrating radar signal denoising

Abstract

In buried object detection, recorded signals of a ground penetrating radar (GPR) inevitably include noise interference owing to complex underground environments. Existing rule- and data-driven denoising methods struggle to handle non-Gaussian and real-world noise because the rule-driven ones rely on the assumptions of simplified noise characteristics and the data-driven ones cannot capture fine- and global-scale features of a GPR signal well. To address the problem, this study proposes an attention-based denoising model called the Swin-Conv Block with Attention Denoising Autoencoder (SCB-ADAE). The model first feeds a GPR signal into a SCB module, which extracts a tensor with the fine-scale features in the signal, such as sharp reflective interfaces and abrupt amplitude variations. The feature tensor then passes through an ADAE module that uses encoder-decoder structure with the self-attention to enhances the representation of the global-scale signal features. Finally, the feature tensor from the ADAE module is decoded by another SCB module to generate a denoised GPR signal, where the tensor includes the fine-scale and global features of the raw signal. An experiment with three types of GPR signals demonstrates the effectiveness of the proposed model: radar signals with Gaussian noise, radar signals with inhomogeneous-material noise, and real-world signals. radar signals with Gaussian noise, radar signals with inhomogeneous-material noise, and real-world signals. Experimental results demonstrate that the proposed model outperforms other state-of-the-art denoising methods on denosing the three types of GPR signals, where the signal-to-noise ratio, peak signal-to-noise ratio, and structural similarity index are improved to 20.64, 14.59, and 0.366, respectively.