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...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.935-940
Main Authors: Luo, Tess Xiang-Huan, Lai, Wallace Wai Lok
Format: Article
Language:English
Subjects:
Algorithms
Benchmark testing
Change detection
Cluster analysis
Clustering
Clustering algorithms
Defects
Diagnosis
Dredging
Excavation
Feasibility studies
Ground penetrating radar
Ground penetrating radar (GPR)
Image registration
Pixels
Prototypes
Radar
Reflection
Scattering
subsurface diagnosis
Surveying
temporal change detection
Three-dimensional displays
time-lapse
Vector quantization
Workflow
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Description
Summary: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.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2020.2975659