3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability

This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-11, Vol.11 (21), p.2545
Main Authors: Kang, Man-Sung, Kim, Namgyu, Im, Seok Been, Lee, Jong-Jae, An, Yun-Kyu
Format: Article
Language:English
Subjects:
Artificial neural networks
automated underground object classification
Automation
Cavities
Classification
Data interpretation
deep convolutional neural network
False alarms
Feature extraction
Ground penetrating radar
Holes
Image classification
Image enhancement
Image resolution
Manholes
Neural networks
Object recognition
phase analysis
Radar imaging
Remote sensing
Roads
Sinkholes
Subsoils
super-resolution
underground cavity detection network
Underground roadways
Urban areas
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Summary:This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11212545