DR-GAN:一种无监督学习的探地雷达杂波抑制方法  

DR-GAN:An Unsupervised Learning Approach to Clutter Suppression for Ground Penetrating Radar

作　　者：雷文太[1] 毛凌青 庞泽邦 任强[2] 王成浩[2] 隋浩 辛常乐 LEI Wentai;MAO Lingqing;PANG Zebang;REN Qiang;WANG Chenghao;SUI Hao;XIN Changle(School of Computer Science,Central South University,Changsha 410083,China;China Research Institute of Radiowave Propagation,Qingdao 266107,China)

机构地区：[1]中南大学计算机学院,长沙410083 [2]中国电波传播研究所,青岛266107

出　　处：《电子与信息学报》2023年第10期3776-3785,共10页Journal of Electronics & Information Technology

基　　金：中国电波传播研究所稳定支持科研经费(A131903W13)。

摘　　要：探地雷达(GPR)是一种基于电磁波的地下无损探测技术,广泛应用于市政工程、交通、军事等领域。在数据采集过程中,由于发射天线和接收天线之间的耦合、起伏地面的散射以及地下随机媒质的复杂性等原因,采集得到的GPR B-scan回波中通常存在杂波,杂波严重影响了地下目标的检测和特征提取。该文提出一种用于GPR B-scan图像杂波抑制的解纠缠表示生成对抗网络(DR-GAN),设计了目标特征编码器和杂波特征编码器用来提取GPR B-scan图像中的目标特征和杂波特征,设计了杂波抑制生成器用来获取杂波抑制后的GPR B-scan图像。与现有的基于监督学习的GPR杂波抑制方法相比,该方法在网络训练时不需要成对的匹配数据,可以更好地应用于实测GPR图像的杂波抑制。在仿真和实测GPR数据上的实验结果表明,DR-GAN这一无监督学习网络具有更好的杂波抑制性能。对石英砂中埋设的钢筋进行数据采集,运用DR-GAN对含杂波的实测数据进行处理,处理结果的改善系数(IF)指标较现有的鲁棒非负矩阵分解(RNMF)方法提高了17.85 dB。Ground Penetrating Radar(GPR)is an underground nondestructive detection technology based on electromagnetic wave,which is widely used in municipal engineering,transportation,military and other fields.In the process of data acquisition,due to the coupling between transmitting antenna and receiving antenna,scattering from undulating ground and the complexity of underground random media,there is usually clutter in the GPR B-scan,which affects seriously the detection and feature extraction of underground targets.A Disentanglement Representation Generative Adversarial network(DR-GAN)for clutter suppression in GPR B-scan images is proposed.A target feature encoder and a clutter feature encoder are designed to extract target features and clutter features in GPR B-scan images.A clutter suppression generator is designed to obtain the GPR B-scan image after clutter suppression.Compared with the existing GPR clutter suppression methods based on supervised learning,the proposed method does not need pairwise matching data during network training,and can be better applied to the clutter suppression of measured GPR images.Experimental results on simulated and measured GPR data show that DR-GAN is an unsupervised learning network with better clutter suppression performance.The data of reinforcement embedded in quartz sand are collected,and the measured data containing clutter are processed by DR-GAN.The Improvement Factor(IF)index of the processing results is 17.85 dB higher than that of the existing Robust Nonnegative Matrix Factorization(RNMF)method.

关 键 词：探地雷达 杂波抑制 无监督学习 解纠缠表示 生成对抗网络 

分 类 号：TN957.52[电子电信—信号与信息处理]