电波科学学报2025，Vol.40Issue(1)：141-154,14.DOI:10.12265/j.cjors.2024018
基于生成对抗网络的两阶段探地雷达图像反演方法
Two-stage GPR image inversion method based on generative adversarial network
摘要
Abstract
In the application of ground penetrating radar(GPR),the inversion imaging is the key technology for interpreting the data information of GPR.Existing GPR image inversion techniques based on deep learning are mostly applied to the ideal environment of underground homogeneous media.However,the data collected in real environments usually contain complex noise and clutter signals,which greatly affect the accuracy of the inversion.To address this issue,this paper proposes a two-stage GPR image inversion network based on generative adversarial network(GAN),named TSInvNet,to reconstruct the spatial distribution of underground targets in real environments.This method processes the GPR B-scan images through a denoising network,TSInvNet1,using an improved spatially-adaptive normalization(SPADE)generator,and then inputs the processed images into an inversion network,TSInvNet2,which introduces a shuffle attention(SA)model for inversion.Experimental results on simulated and real data demonstrate that TSInvNet can accurately invert the underground target positions based on GPR B-scan images,showing strong robustness and precise inversion performance in applications involving complex noise and multiple targets.关键词
探地雷达(GPR)/反演成像/深度学习/生成对抗网络(GAN)/注意力模型Key words
ground penetrating radar(GPR)/inversion imaging/deep learning/generative adversarial network(GAN)/attention model分类
信息技术与安全科学引用本文复制引用
武铭泽,刘庆华,欧阳缮..基于生成对抗网络的两阶段探地雷达图像反演方法[J].电波科学学报,2025,40(1):141-154,14.基金项目
国家自然科学基金(62361015) （62361015）
