Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review

Travassos, Xisto Lucas; Avila, Sergio; Ida, Nathan

doi:10.1016/j.aci.2018.10.001

Cited by 89 publications

(47 citation statements)

References 56 publications

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“…Many machine learning methods have been developed for visual imagery and text as outlined above. Yet more and more methods are being developed and adapted to non-visual imagery such as X-ray computed tomography, ground-penetrating radar, and hyperspectral imagery (Zare and Ho, 2013;Rogers et al, 2016;Travassos et al, 2018). Théroux-Rancourt et al (2020) developed a threedimensional segmentation and characterization approach for leaf internal anatomy using X-ray microcomputed tomography.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Plants meet machines: Prospects in machine learning for plant biology

et al. 2020

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Plants meet machines: Prospects in machine learning for plant biology

et al. 2020

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“…There are three major types of spectroscopy techniques which Raman spectroscopy, Ground Penetrating Radar (GPR) and dielectric spectroscopy. Raman spectroscopy involved the excitation of atoms to a higher energy state and resulting in frequency shifting [30]. A research has been done to detect sulphate (SO2-4) ions and methane (CH4) in drinking water by using Raman Spectroscopy.…”

Section: Spectroscopy Techniquesmentioning

confidence: 99%

Review on water quality monitoring technologies

Ting

Yee

2020

IJEECS

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<span>Water quality monitoring is always the prior element to ensure the drinking water is safe to be consumed. A lot of researches have been carried out over the past decades to design and develop a robust and cost-effective water monitoring system. The conventional water monitoring techniques were based on laboratory instruments which is time-consuming and laborious. Furthermore, it is not suitable when the water sampling point is far from the commercial laboratory centres. These limitations were then been solved by the developments of portable testing kits and microwave technique. The microwave techniques such as spectroscopy techniques and microwave sensor approach have improved the water quality monitoring experience which is convenient without sacrificing the measurement accuracy and sensitivity. Its portability enables the on-site measurement at rural areas and thus reduce the transportation and manpower cost. This paper intends to review the water contaminant detection techniques which include standardized drinking water parameter testing and microwave-based in terms of physical, chemical and microbiological parameters. Furthermore, this review also emphasizes the current trend of the water quality testing method in microwave technique. At the end of this paper, a significant advantages and drawbacks of the techniques are summarized, and recommendations are provided for future development in the water quality monitoring.</span>

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“…More recently, neural networks and convolutional neural networks (CNNs) were widely used as a promising tool for automated GPR image recognition and classification. Mazurkiewicz et al tried to identify underground objects using neural networks while reducing the processing time and human intervention [21][22][23]. Then, Al-Nuaimy et al utilized both neural network and pattern recognition methods to automatically detect the buried objects [24].…”

Section: Introductionmentioning

confidence: 99%

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

Kang

Kim

Im³

et al. 2019

Remote Sensing

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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.

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Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review

Cited by 89 publications

References 56 publications

Plants meet machines: Prospects in machine learning for plant biology

Plants meet machines: Prospects in machine learning for plant biology

Review on water quality monitoring technologies

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

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