Abstract:

To address the problem of complex background clutter and interference of strong inter-layer reflection in airport runway Ground Penetrating Radar （GPR） data， an improved U-Net-based deep learning algorithm for clutter suppression was proposed. In the algorithm， a detail enhancement module DE-Conv was introduced at the skip connections of U-Net to improve the network ability to capture details of target signals in multi-scale shallow features， and a feature-pixel dual-level fusion loss function was adopted using clutter-contaminated and clutter-free image pairs to optimize the training process. Specifically， high-dimensional features from both clutter-contaminated and clutter-free data were extracted by a shared-weight encoder， feature-level losses were computed to guide the training of the encoder， and pixel-level losses were computed using the images output by the decoder and the corresponding clutter-free simulated images to optimize the decoder’s performance. Experimental results show that the proposed algorithm achieves a Peak Signal-to-Noise Ratio （PSNR） of 37.114 7 dB and a Structural SIMilarity （SSIM） of 0.999 8 on a synthetic dataset， and an average Signal-to-Clutter Ratio （SCR） of 8.28 dB and Improvement Factor （IF） of 5.90 dB on a real airport runway dataset， outperforming the baseline model by 0.952 8 dB， 0.000 4， 6.58 dB， and 5.32 dB in these four metrics， respectively. Compared with Robust Nonnegative Matrix Factorization （RNMF）， Robust Principal Component Analysis （RPCA）， and the deep learning-based Clutter-removal Neural Network （CR-Net）， the proposed algorithm has superior clutter suppression performance and computational efficiency. At the same time， a lot of ablation experimental results validate the effectiveness of the detail enhancement module and feature-pixel dual-level loss function to clutter removal and target signal recovery.

Key words: Ground Penetrating Radar (GPR), clutter suppression, detail enhancement network, feature-pixel dual-level fusion loss, airport runway

摘要：

针对机场跑道探地雷达（GPR）数据中的复杂背景杂波和层间强反射干扰信号的问题，提出一种基于改进U-Net的深度学习杂波抑制算法。该算法在U-Net的跳跃连接处引入细节增强模块DE-Conv，从而增强网络对多尺度浅层特征中目标信号细节的捕捉能力；同时，采用含杂波-无杂波图像对计算特征-像素双级融合损失函数优化训练过程。具体地，通过共享权重编码器提取的含杂波与无杂波数据的高维特征，计算特征级别损失来指导编码器的训练，并使用解码器输出图像与对应的无杂波仿真图像计算像素级别损失以优化解码器性能。实验结果表明，在合成数据集上，所提算法的峰值信噪比（PSNR）和结构相似度（SSIM）分别达到37.114 7 dB和0.999 8；而在真实机场跑道数据集上，所提算法的平均信杂比（SCR）和改善系数（IF）分别为8.28 dB和5.90 dB，以上4种指标相较于基准模型的数据分别提升了0.952 8 dB、0.000 4、6.58 dB和5.32 dB。与鲁棒非负矩阵分解（RNMF）、鲁棒主成分分析（RPCA）及同样基于深度学习的基于U-Net改进的杂波去除神经网络（CR-Net）相比，所提算法在杂波抑制效果和计算效率上均表现出优势。同时，大量的消融实验结果验证了细节增强模块和特征-像素双级损失函数对杂波去除和目标信号恢复的有效性。

关键词: 探地雷达, 杂波抑制, 细节增强网络, 特征-像素双级融合损失, 机场跑道