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DR-GAN:一种无监督学习的探地雷达杂波抑制方法

雷文太 毛凌青 庞泽邦 任强 王成浩 隋浩 辛常乐

雷文太, 毛凌青, 庞泽邦, 任强, 王成浩, 隋浩, 辛常乐. DR-GAN:一种无监督学习的探地雷达杂波抑制方法[J]. 电子与信息学报, 2023, 45(10): 3776-3785. doi: 10.11999/JEIT221072
引用本文: 雷文太, 毛凌青, 庞泽邦, 任强, 王成浩, 隋浩, 辛常乐. DR-GAN:一种无监督学习的探地雷达杂波抑制方法[J]. 电子与信息学报, 2023, 45(10): 3776-3785. doi: 10.11999/JEIT221072
LEI Wentai, MAO Lingqing, PANG Zebang, REN Qiang, WANG Chenghao, SUI Hao, XIN Changle. DR-GAN: An Unsupervised Learning Approach to Clutter Suppression for Ground Penetrating Radar[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3776-3785. doi: 10.11999/JEIT221072
Citation: LEI Wentai, MAO Lingqing, PANG Zebang, REN Qiang, WANG Chenghao, SUI Hao, XIN Changle. DR-GAN: An Unsupervised Learning Approach to Clutter Suppression for Ground Penetrating Radar[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3776-3785. doi: 10.11999/JEIT221072

DR-GAN:一种无监督学习的探地雷达杂波抑制方法

doi: 10.11999/JEIT221072
基金项目: 中国电波传播研究所稳定支持科研经费(A131903W13)
详细信息
    作者简介:

    雷文太:男,教授,研究方向为探地雷达信号处理技术

    毛凌青:男,硕士生,研究方向为探地雷达信号处理技术

    庞泽邦:男,博士生,研究方向为基于深度学习的探地雷达智能数据处理

    任强:男,高级工程师,研究方向为探地雷达系统集成技术

    王成浩:男,高级工程师,研究方向为探地雷达信息处理

    隋浩:男,硕士生,研究方向为探地雷达信号处理技术

    辛常乐:男,硕士生,研究方向为探地雷达信号处理技术

    通讯作者:

    庞泽邦 pangzebang@csu.edu.cn

  • 中图分类号: TN957.52

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

Funds: The Stable-Support Scientific Project of China Research Institute of Radiowave Propagation (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。
  • 图  1  DR-GAN的网络框架

    图  2  GPR图像的杂波抑制流程

    图  3  DR-GAN的编码器与生成器的网络结构

    图  4  DR-GAN的判别器网络结构

    图  5  仿真场景模型

    图  6  DR-GAN在仿真数据上的杂波抑制效果

    图  7  不同方法对仿真数据的杂波抑制效果

    图  8  实测场景

    图  9  实测杂波数据的制作

    图  10  DR-GAN在实测数据上的杂波抑制效果

    图  11  不同方法对实测数据的杂波抑制效果

    表  1  仿真场景参数

    模型参数扫描场景参数
    模型尺寸1.8 m×0.002 m×0.45 m发射源波形瑞克子波
    单元格大小0.002 m×0.002 m×0.002 m发射中心频率2 GHz
    土壤沙子重量百分比50%仿真时窗10 ns
    土壤粘土重量百分比50%发射天线起点(0.1 m,0.002 m,0.4 m)
    土壤的容重2.0 g/cm3接收天线起点(0.2 m,0.002 m,0.4 m)
    土壤的沙粒密度2.66 g/cm3每次扫描天线移动距离0.01 m
    土壤体积含水率范围0.001~0.150目标形状圆柱、方柱
    土壤材料种类50种圆柱半径0.03~0.05 m
    土壤厚度0.4 m方柱边长0.04~0.06 m
    土壤表面高度起伏范围0.38~0.41 m目标高度0.2~0.3 m
    目标水平位置0.35~1.45 m
    下载: 导出CSV

    表  2  各种杂波抑制算法的平均PSNR(dB)/平均SSIM

    目标类型MSSVDNMFRPCARNMFRAE本文DR-GAN
    PVC圆柱体0.85/0.01422.38/0.36522.23/0.30523.54/0.25922.72/0.20522.56/0.15436.66/0.965
    空洞圆柱体6.62/0.06022.96/0.42022.96/0.39825.94/0.47024.35/0.33822.99/0.36342.73/0.989
    金属圆柱体12.58/0.12625.26/0.58825.18/0.56227.59/0.61225.86/0.47125.92/0.43244.70/0.992
    PVC方柱体6.41/0.02123.09/0.35724.06/0.36527.11/0.43725.84/0.35125.22/0.28743.65/0.980
    空洞方柱体12.45/0.07225.79/0.53525.38/0.49428.78/0.59726.43/0.44425.80/0.36947.92/0.993
    金属方柱体16.97/0.18125.97/0.53825.39/0.50429.15/0.65227.45/0.53826.72/0.45848.00/0.993
    下载: 导出CSV

    表  3  各种杂波抑制方法的平均用时(s)

    MSSVDNMFRPCARNMFRAEDR-GAN(GPU)
    时间0.002 90.010 20.013 62.083 96.063 63.872 60.103 0
    下载: 导出CSV

    表  4  实测数据杂波抑制的平均IF(dB)

    目标类型MSSVDNMFRPCARNMFRAE本文DR-GAN
    空心PVC管10.4924.1924.5920.6824.7412.0141.34
    空心塑料瓶13.2624.5023.8223.1824.6718.4045.79
    钢筋13.1926.0725.9626.2828.1626.6546.01
    下载: 导出CSV
  • [1] 乔尔, 雷文太, 童孝忠, 周肠, 等译. 探地雷达理论与应用[M]. 北京: 电子工业出版社, 2011.

    JOL H M, LEI Wentai, TONG Xiaozhong, ZHOU Yang, et al. translation. Ground Penetrating Radar: Theory and Applications[M]. Beijing: Publishing House of Electronics Industry, 2011.
    [2] 刘澜波, 钱荣毅. 探地雷达: 浅表地球物理科学技术中的重要工具[J]. 地球物理学报, 2015, 58(8): 2606–2617. doi: 10.6038/cjg20150802

    LIU Lanbo and QIAN Rongyi. Ground penetrating radar: A critical tool in near-surface geophysics[J]. Chinese Journal of Geophysics, 2015, 58(8): 2606–2617. doi: 10.6038/cjg20150802
    [3] TONG Zheng, YUAN Dongdong, GAO Jie, et al. Pavement-distress detection using ground-penetrating radar and network in networks[J]. Construction and Building Materials, 2020, 233: 117352. doi: 10.1016/j.conbuildmat.2019.117352
    [4] 刘海, 黄肇刚, 岳云鹏, 等. 地下管线渗漏环境下探地雷达信号特征分析[J]. 电子与信息学报, 2022, 44(4): 1257–1264. doi: 10.11999/JEIT211213

    LIU Hai, HUANG Zhaogang, YUE Yunpeng, et al. Characteristics analysis of ground penetrating radar signals for groundwater pipe leakage environment[J]. Journal of Electronics &Information Technology, 2022, 44(4): 1257–1264. doi: 10.11999/JEIT211213
    [5] SOLIMENE R, CUCCARO A, DELL' AVERSANO A, et al. Ground clutter removal in GPR surveys[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(3): 792–798. doi: 10.1109/JSTARS.2013.2287016
    [6] ABUJARAD F, NADIM G, and OMAR A. Clutter reduction and detection of landmine objects in ground penetrating radar data using Singular Value Decomposition (SVD)[C]. The 3rd International Workshop on Advanced Ground Penetrating Radar, Delft, Netherlands, 2005: 37–41.
    [7] CHEN Gaoxiang, FU Liyun, CHEN Kanfu, et al. Adaptive ground clutter reduction in ground-penetrating radar data based on principal component analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(6): 3271–3282. doi: 10.1109/TGRS.2018.2882912
    [8] ABUJARAD F and OMAR A. Comparison of independent component analysis (ICA) algorithms for GPR detection of non-metallic land mines[J]. SPIE, 2006, 6365: 636516.
    [9] KUMLU D and ERER I. Performance evaluation of NMF methods with different divergence metrics for landmine detection in GPR[J]. SPIE, 2018, 10794: 107940I.
    [10] TEMLIOGLU E and ERER I. Clutter removal in ground-penetrating radar images using morphological component analysis[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1802–1806. doi: 10.1109/LGRS.2016.2612582
    [11] KUMLU D and ERER I. Improved clutter removal in GPR by robust nonnegative matrix factorization[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(6): 958–962. doi: 10.1109/LGRS.2019.2937749
    [12] SONG Xiaosong, XIANG Deliang, ZHOU Kai, et al. Improving RPCA-based clutter suppression in GPR detection of antipersonnel mines[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(8): 1338–1342. doi: 10.1109/LGRS.2017.2711251
    [13] NI Zhikang, YE Shengbo, SHI Cheng, et al. Clutter suppression in GPR B-scan images using robust autoencoder[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3500705. doi: 10.1109/LGRS.2020.3026007
    [14] ZHOU Huilin, WANG Yi, LIU Qiegen, et al. RNMF-guided deep network for signal separation of GPR without labeled data[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3507705. doi: 10.1109/LGRS.2021.3099161
    [15] GENG Jianrong, HE Juan, YE Hongxia, et al. A clutter suppression method based on LSTM network for ground penetrating radar[J]. Applied Sciences, 2022, 12(13): 6457. doi: 10.3390/app12136457
    [16] TEMLIOGLU E and ERER I. A novel convolutional autoencoder-based clutter removal method for buried threat detection in ground-penetrating radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5103313. doi: 10.1109/TGRS.2021.3098122
    [17] NI Zhikang, SHI Cheng, PAN Jun, et al. Declutter-GAN: GPR B-scan data clutter removal using conditional generative adversarial nets[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4023105. doi: 10.1109/LGRS.2022.3159788
    [18] HUANG Yongqiang, XIA Wenjun, LU Zexin, et al. Noise-powered disentangled representation for unsupervised speckle reduction of optical coherence tomography images[J]. IEEE Transactions on Medical Imaging, 2021, 40(10): 2600–2614. doi: 10.1109/TMI.2020.3045207
    [19] LEE H Y, TSENG H Y, HUANG Jiabin, et al. Diverse image-to-image translation via disentangled representations[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 36–52.
    [20] ABROL V, SHARMA P, and PATRA A. Improving generative modelling in VAEs using multimodal prior[J]. IEEE Transactions on Multimedia, 2021, 23: 2153–2161. doi: 10.1109/TMM.2020.3008053
    [21] ISOLA P, ZHU Junyan, ZHOU Tinghui, et al. Image-to-image translation with conditional adversarial networks[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017: 5967–5976.
    [22] WARREN C, GIANNOPOULOS A, and GIANNAKIS I. gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar[J]. Computer Physics Communications, 2016, 209: 163–170. doi: 10.1016/j.cpc.2016.08.020
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出版历程
  • 收稿日期:  2022-08-15
  • 修回日期:  2023-02-16
  • 网络出版日期:  2023-02-22
  • 刊出日期:  2023-10-31

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