Wavelet scattering network-based machine learning for ground penetrating radar imaging

Wavelet scattering network-based machine learning for ground penetrating radar imaging: Application in pipeline identification

Jin, J. (TU Delft Railway Engineering)
Duan, Yunling (Tsinghua University)

2020

Automatic and efficient ground penetrating radar (GPR) data analysis remains a bottleneck, especially restricting applications in real-time monitoring systems. Deep learning approaches have good practice in automatic object identification, but their intensive data requirement has reduced their applicability. This paper developed a machine learning framework based on wavelet scattering networks to analyze GPR data for subsurface pipeline identification. Wavelet scattering network is functionally equivalent to convolutional neural networks, and its null-parameter property is intended for non-intensive datasets. A double-channel framework is designed with wavelet scattering networks followed by support vector machines to determine the existence of pipelines on vertical and horizontal traces separately. Classification accuracy rates arrive around 98% and 95% for datasets without and with noises, respectively, as well as 97% for considering surface roughness. Pipeline locations and diameters are convenient to determine from the reconstructed profiles of both simulated and practical GPR signals. However, the results of 5 cm pipelines are sensitive to noises. Nonetheless, the developed machine learning approach presents promising applicability in subsurface pipeline identification.

Ground penetrating radar
Machine learning
Pipeline identification
Support vector machine
Wavelet scattering network

http://resolver.tudelft.nl/uuid:6a374b20-da28-4ee5-9cae-5c2426ac3818

https://doi.org/10.3390/rs12213655

2072-4292

Remote Sensing, 12 (21), 1-24

Institutional Repository

journal article

© 2020 J. Jin, Yunling Duan