电子学报 ›› 2019, Vol. 47 ›› Issue (6): 1366-1372.DOI: 10.3969/j.issn.0372-2112.2019.06.025

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面向双曲线形态的探地雷达图像识别技术综述

郝彤1,2, 赵杰1,2   

  1. 1. 同济大学空间信息科学及可持续发展应用中心, 上海 200092;
    2. 同济大学测绘与地理信息学院, 上海 200092
  • 收稿日期:2019-01-06 修回日期:2019-04-10 出版日期:2019-06-25 发布日期:2019-06-25
  • 通讯作者: 郝彤
  • 作者简介:赵杰 男,1994年1月出生,福建三明人,硕士研究生.主要研究方向为探地雷达图像处理,计算机视觉.E-mail:zhaojie_tongji_3s@tongji.edu.cn

A Brief Review of the Hyperbola Signature Recognition Techniques for Ground Penetrating Radar

HAO Tong1,2, ZHAO Jie1,2   

  1. 1. Application Center for Spatial Information Science and Sustainable Development, Tongji University, Shanghai 200092, China;
    2. College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China
  • Received:2019-01-06 Revised:2019-04-10 Online:2019-06-25 Published:2019-06-25

摘要: 探地雷达作为一种重要的无损探测技术,可以准确快速对浅层地表进行成像,以获取埋设目标的空间与几何信息.该文在分析探地雷达成像模型与目标识别基本思路的基础上,简述和归纳了七类探地雷达图像双曲线特征的检测方法,分别为基于双曲线性质、时域信号分析、数字图像分析、机器学习、数学模型、综合性方法以及深度学习方法,最后展望了深度学习在双曲线形态识别中的应用前景.

关键词: 物探技术, 无损探测, 探地雷达, 目标识别, 双曲线特征, 深度学习

Abstract: Ground penetrating radar (GPR),as an important non-destructive detection technology,can image the shallow subsurface accurately and quickly to obtain the spatial and geometric information of the buried targets.This paper describes and summarizes seven detection methods of hyperbolic signatures in GPR images,i.e.,based on hyperbolic properties,based on the time domain signal analysis,based on the digital image analysis,based on the machine learning,based on the mathematical model,based on the comprehensive and deep learning methods.Finally,we discuss the application potential of the deep learning method for the recognition of hyperbolic signatures.

Key words: geophysical technology, non-destructive detection, GPR, object recognition, hyperbola signature, deep learning

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