Subsurface Diagnosis With Time-Lapse GPR Slices and Change Detection Algorithms

This article explores the capability of applying time-lapse ground penetrating radar (GPR) data to investigate the health condition of an urban subsurface. A workflow is proposed to semi-automatically extract changes from time-lapse GPR C-scans. The developed workflow consists of two main steps, in which the first step is image registration and intensity normalization. The workflow uses benchmark points on the ground to normalize the global intensity of time-lapse GPR C-scans. The second step classifies pixels into change or unchanged group. Two kinds of information are considered to construct two difference-maps: changes in the image intensity and the object structure. K-means clustering is responsible for extracting pixels that possess both intensity changes and object structure changes – where potential subsurface defects most likely occurred. The workflow was verified by a site experiment, and the area of excavation with pipe replacement was successfully identified. The performance of the proposed workflow was promising in excluding small and random scattering noise, which was the main challenge in a time-lapse GPR survey. The article serves as a prototype and demonstrates the feasibility and necessity of conducting temporal diagnosis on the subsurface structure.

• Publication year

  2020

• Venue

  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

• Publication date

  2020-02-28

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/JSTARS.2020.2975659
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• GPR imaging criteria

  2019cited by this paper
• and s

  2019cited by this paper
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  2009cited by this paper
• Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering

  2009influential reference
• Influence of cultivation temperature on the ligninolytic activity of selected fungal strains

  2006cited by this paper
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  2004cited by this paper
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  2002cited by this paper
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  2002cited by this paper
• The impact of misregistration on change detection

  1992cited by this paper
• Information Theory

  1987cited by this paper
• Some methods for classification and analysis of multivariate observations

  1967influential reference
• Cluster analysis of multivariate data : efficiency versus interpretability of classifications

  1965cited by this paper

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