期刊
REMOTE SENSING
卷 10, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/rs10091417
关键词
ground-penetrating radar; underground object classification; urban road; sinkhole; signal processing; basis pursuit filter; phase analysis
类别
资金
- Korea Railroad Research Institute (KRRI), Republic of Korea [PK1702C-1]
- National Research Foundation of Korea (NRF), Republic of Korea [NRF-2015R1C1A1A01052625]
- National Research Council of Science & Technology (NST), Republic of Korea [PK1802B2C, PK1702C] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2015R1C1A1A01052625] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Ground-penetrating radar (GPR) has been widely used to detect subsurface objects, such as hidden cavities, buried pipes, and manholes, owing to its noncontact sensing, rapid scanning, and deeply penetrating remote-sensing capabilities. Currently, GPR data interpretation depends heavily on the experience of well-trained experts because different types of underground objects often generate similar GPR reflection features. Moreover, reflection visualizations that were obtained from field GPR data for urban roads are often weak and noisy. This study proposes a novel instantaneous phase analysis technique to address these issues. The proposed technique aims to enhance the visibility of underground objects and provide objective criteria for GPR data interpretation so that the objects can be automatically classified without expert intervention. The feasibility of the proposed technique is validated both numerically and experimentally. The field test utilizes rarely available GPR data for urban roads in Seoul, South Korea and demonstrates that the technique allows for successful visualization and classification of three different types of underground objects.
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