Permeability and voids influence on the thermal signal, as inferred by multitemporal UAV-based infrared and visible images

Antoine, Raphaël; Fauchard, Cyrille; Oehler, Jean-François; Joignant, Philippe

doi:10.1016/j.jhydrol.2020.124907

Cited by 7 publications

(4 citation statements)

References 56 publications

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“…The research results of the laboratory experiments carried out in this paper were similar to Bhalla [28], and the temperature difference obtained by the field thermal imaging shot in this paper is similar to Antoine [29]. As a non-contact sensing method, ITNDT has many advantages, such as intuitive image, fast operation, and strong mobility.…”

Section: Discussionsupporting

confidence: 74%

Mechanism of Detecting the Construction Quality of a Diaphragm Wall by an Infrared Thermal Field and Engineering Application

et al. 2023

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During underground space exploitation in the urbanization process, numerous foundation pits were constructed where a diaphragm wall was often used as a retaining structure and waterproof curtain. Due to complicated engineering geological conditions or improper construction, diaphragm walls and wall joints often exhibit quality defects. Groundwater leaked from these quality defects to foundation pits during excavation, endangering the safety of the pit and surrounding facilities. The current leakage identification of the underground retaining structure was performed by artificial visual detection, which cannot satisfy the engineering requirement. The temperature field in the leakage area of the diaphragm wall was different from other areas. The leakage wall imaging system using a thermal imager was efficient in visualizing leaking, which was not visible to the naked eye. In this study, infrared thermal imaging technology was introduced in potential leakage detection for the diaphragm wall of a foundation pit. The infrared radiation characteristics of the diaphragm wall leakage and the potential leakage parts were studied through laboratory simulation tests and on-site detection methods. The maximum temperature appeared at the water outlet and the surface of the defect with hidden defect, and the temperature field was symmetrically distributed along the cross-section direction. In the potential leakage area, the temperature difference at the penetration point was 23.4 °C when the initial water pressure was 10 kPa. The temperature difference at the penetration point was 21.8 °C when the initial water pressure was 30 kPa. In the field test, the maximum temperature difference between the leakage area and the surrounding wall was 4.5 °C. The study can provide a reference for similar engineering.

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Section: Discussionsupporting

confidence: 74%

Mechanism of Detecting the Construction Quality of a Diaphragm Wall by an Infrared Thermal Field and Engineering Application

et al. 2023

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“…Although CNN can complete the task of remote sensing image segmentation, it has fixed requirements on the size of the input image, which is a big problem for large remote sensing images of different sizes. To solve this problem, Jonathan Long et al replaced the last fully connected layer of CNN with a convolutional layer to form a new neural network: Fully Convolutional Networks (FCN) [35], and applied it to the semantics of images. in the segmentation problem.…”

Section: Application Of Cnn and Fcn In Uav Remote Sensing Image Segme...mentioning

confidence: 99%

Research on segmentation algorithm of UAV remote sensing image based on deep learning

Zhou

Sui²,

Ding³

et al. 2023

Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022)

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With the rapid development of UAV technology, the research topic of remote sensing image segmentation has gradually attracted more and more attention. Whether the image can be accurately segmented is a measure of the goodness of the algorithm. In recent years, machine learning methods have been applied to a large number of fields, and deep neural network technology has also been widely used in the field of UAV remote sensing image segmentation. This paper introduces the specific applications of various deep learning methods in remote sensing image segmentation, and briefly analyzes the development of neural networks in this problem according to the research status of several typical deep neural networks in UAV remote sensing image segmentation. Research shows that on the basis of proper adjustment of optimizer, learning rate and loss function, using deep learning method to segment UAV remote sensing images, the accuracy rate can reach more than 96%.

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“…The first multichannel thermal scanner for mapping geological units (the Dedalus 24-channel scanner) was developed in the late 1970s. Since the 1980s, TIR sensors have been utilized in different contexts, such as mining and tunneling, detection of hydrological features in karst watersheds, monitoring of geothermal activity, mapping of geomorphologic and sedimentary features of alluvial fans, agricultural soil analysis for moisture content and LST monitoring [77][78][79][80][81][82][83].…”

Section: Remotely Sensed Tir Application In Geological Domainsmentioning

confidence: 99%

Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides

et al. 2020

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Coastal retreat is a non-recoverable phenomenon that—together with a relevant proneness to landslides—has economic, social and environmental impacts. Quantitative data on geological and geomorphologic features of such areas can help to predict and quantify the phenomena and to propose mitigation measures to reduce their impact. Coastal areas are often inaccessible for sampling and in situ surveys, in particular where steeply sloping cliffs are present. Uses and capability of infrared thermography (IRT) were reviewed, highlighting its suitability in geological and landslides hazard applications. Thanks to the high resolution of the cameras on the market, unmanned aerial vehicle-based IRT allows to acquire large amounts of data from inaccessible steep cliffs. Coupled structure-from-motion photogrammetry and coregistration of data can improve accuracy of IRT data. According to the strengths recognized in the reviewed literature, a three-step methodological approach to produce IRTs was proposed.

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Permeability and voids influence on the thermal signal, as inferred by multitemporal UAV-based infrared and visible images

Abstract: This work presents for the first time associated thermal anomalies and deformations over subsurface cavities/voids located within two harbour quays

Cited by 7 publications

References 56 publications

Mechanism of Detecting the Construction Quality of a Diaphragm Wall by an Infrared Thermal Field and Engineering Application

Mechanism of Detecting the Construction Quality of a Diaphragm Wall by an Infrared Thermal Field and Engineering Application

Research on segmentation algorithm of UAV remote sensing image based on deep learning

Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides

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