Wavelet Scattering Network-Based Machine Learning for Ground Penetrating Radar Imaging: Application in Pipeline Identification

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 21; p. 3655
• Main Authors: Jin, Yang, Duan, Yunling
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2020
• Subjects: Accuracy; Antennas; Artificial intelligence; Artificial neural networks; Data analysis; Data processing; Datasets; Decomposition; Deep learning; Ground penetrating radar; Identification; Investigations; Learning algorithms; Machine learning; Neural networks; pipeline identification; Pipelines; Radar; Radar imaging; Remote sensing; Scattering; Simulation; support vector machine; Support vector machines; Surface roughness; Wavelet analysis; wavelet scattering network; Wavelet transforms
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12213655

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.