scholarly journals Thermal Imaging Camera Supporting the Navigation of UAVs

2021 ◽  
Vol 25 (3) ◽  
pp. 43-50
Author(s):  
Grzegorz Bieszczad ◽  
Krzysztof Sawicki ◽  
Sławomir Gogler ◽  
Andrzej Ligienza ◽  
Mariusz Mścichowski
Keyword(s):  
Electromagnetic Interference ◽  
Situational Awareness ◽  
Weather Conditions ◽  
Translation Vector ◽  
Distribution Analysis ◽  
Thermal Camera ◽  
Infrared Band ◽  
Radiation Distribution ◽  
Long Wave Infrared ◽  
Magnetic Field Variations

The topic of this paper is an evaluation of developed sensor intended for navigation aid of unmanned aerial vehicles (UAVs). Its operation is based on processing images acquired with a thermal camera operating in the long-wave infrared band (LWIR) placed underneath a vehicle’s chassis. The vehicle’s spatial displacement is determined by analyzing movement of characteristic thermal radiation points (ground, forest, buildings, etc.) in pictures acquired by the thermal camera. Magnitude and direction of displacement is obtained by processing the stream of consecutive pictures with optical-flow based algorithm in real time. Radiation distribution analysis allows to calculate camera’s self-translation vector. Advantages of measuring translation based on thermal image analysis is lack of drift effect, resistance to magnetic field variations, low susceptibility to electromagnetic interference and change in weather conditions as compared to traditional inertial navigation sensors. As opposed to visible light situational awareness sensors, it offers operation in complete darkness (harsh weather, nights and indoors).The topic of this paper is an evaluation of developed sensor intended for navigation aid of unmanned aerial vehicles (UAVs). Its operation is based on processing images acquired from a thermal camera operating in the long wave infrared band (LWIR) placed underneath a vehicle’s chassis. The vehicle’s spatial displacement is determined by analyzing movement of characteristic thermal radiation points (ground, forest, buildings, etc.) in pictures acquired by the thermal camera. Magnitude and direction of displacement is obtained by processing the stream of consecutive pictures with optical-flow based algorithm in real time. Radiation distribution analysis allows to calculate camera’s self-translation vector. Advantages of measuring translation based on thermal image analysis is lack of drift effect, resistance to magnetic field variations, low susceptibility to electromagnetic interference and change in weather conditions as compared to traditional inertial navigation sensors. As opposed to visible light situational awareness sensors, it offers operation in complete darkness (harsh weather, nights and indoors).

scholarly journals Estimating evaporation with thermal UAV data and two-source energy balance models

2016 ◽  
Vol 20 (2) ◽  
pp. 697-713 ◽  
Author(s):  
H. Hoffmann ◽  
H. Nieto ◽  
R. Jensen ◽  
R. Guzinski ◽  
P. Zarco-Tejada ◽  
...  
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
High Resolution ◽  
Eddy Covariance ◽  
Land Surface ◽  
Weather Conditions ◽  
Heat Fluxes ◽  
Thermal Camera ◽  
Model Input ◽  
Eddy Covariance Measurements

Abstract. Estimating evaporation is important when managing water resources and cultivating crops. Evaporation can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST), which have recently become obtainable in very high resolution using lightweight thermal cameras and Unmanned Aerial Vehicles (UAVs). In this study a thermal camera was mounted on a UAV and applied into the field of heat fluxes and hydrology by concatenating thermal images into mosaics of LST and using these as input for the two-source energy balance (TSEB) modelling scheme. Thermal images are obtained with a fixed-wing UAV overflying a barley field in western Denmark during the growing season of 2014 and a spatial resolution of 0.20 m is obtained in final LST mosaics. Two models are used: the original TSEB model (TSEB-PT) and a dual-temperature-difference (DTD) model. In contrast to the TSEB-PT model, the DTD model accounts for the bias that is likely present in remotely sensed LST. TSEB-PT and DTD have already been well tested, however only during sunny weather conditions and with satellite images serving as thermal input. The aim of this study is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to constitute as model input and thus attain accurate and high spatial and temporal resolution surface energy heat fluxes, with special focus on latent heat flux (evaporation). Furthermore, this study evaluates the performance of the TSEB scheme during cloudy and overcast weather conditions, which is feasible due to the low data retrieval altitude (due to low UAV flying altitude) compared to satellite thermal data that are only available during clear-sky conditions. TSEB-PT and DTD fluxes are compared and validated against eddy covariance measurements and the comparison shows that both TSEB-PT and DTD simulations are in good agreement with eddy covariance measurements, with DTD obtaining the best results. The DTD model provides results comparable to studies estimating evaporation with similar experimental setups, but with LST retrieved from satellites instead of a UAV. Further, systematic irrigation patterns on the barley field provide confidence in the veracity of the spatially distributed evaporation revealed by model output maps. Lastly, this study outlines and discusses the thermal UAV image processing that results in mosaics suited for model input. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high-resolution and consistent LST.

scholarly journals Sensing Technology Survey for Obstacle Detection in Vegetation

2021 ◽  
Vol 1 (3) ◽  
pp. 672-685
Author(s):  
Shreya Lohar ◽  
Lei Zhu ◽  
Stanley Young ◽  
Peter Graf ◽  
Michael Blanton
Keyword(s):  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Obstacle Detection ◽  
Agricultural Field ◽  
Thermal Camera ◽  
Agricultural Vehicles ◽  
Sensing Systems ◽  
Sensing Technology ◽  
Hyperspectral Sensing

This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.

A Survey of Approaches for Estimating Meteorological Visibility Distance Under Foggy Weather Conditions

2020 ◽  
pp. 65-92 ◽  
Author(s):  
Faouzi Kamoun ◽  
Hazar Chaabani ◽  
Fatma Outay ◽  
Ansar-Ul-Haque Yasar
Keyword(s):  
Intelligent Transportation Systems ◽  
Situational Awareness ◽  
Wide Spectrum ◽  
Distance Estimation ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Driver Assistance Systems ◽  
Research Issues ◽  
Visibility Distance ◽  
Assistance Systems

The immaturity of fog abatement technologies for highway usage has led to growing interest towards developing intelligent transportation systems that are capable of estimating meteorological visibility distance under foggy weather conditions. This capability is crucial to support next-generation cooperative situational awareness and collision avoidance systems as well as onboard driver assistance systems. This chapter presents a survey and a comprehensive taxonomy of daytime visibility distance estimation approaches based on a review and synthesis of the literature. The proposed taxonomy is both comprehensive (i.e., captures a wide spectrum of earlier contributions) and effective (i.e., enables easy comparison among previously proposed approaches). The authors also highlight some open research issues that warrant further investigation.

Design of concentric multiscale optical system for long-wave infrared band

2020 ◽  
Author(s):  
Yang Shen ◽  
WANG Hu ◽  
XUE Yaoke ◽  
LIU Meiying ◽  
JIE Yongjie ◽  
...  
Keyword(s):  
Optical System ◽  
Infrared Band ◽  
Long Wave ◽  
Long Wave Infrared

Development of a Long-Wave Infrared Band-Edge (LWIR BE) thermometry instrument

2018 ◽  
Vol 89 (7) ◽  
pp. 074903
Author(s):  
J. Marquis ◽  
K. Roodenko ◽  
P. Pinsukanjana ◽  
W. Frensley
Keyword(s):  
Band Edge ◽  
Infrared Band ◽  
Long Wave ◽  
Long Wave Infrared

Feasibility study of electron transfer quantum well infrared photodetectors for spectral tuning in the long-wave infrared band

2013 ◽  
Vol 114 (19) ◽  
pp. 194501
Author(s):  
Greg Jolley ◽  
Nima Dehdashti Akhavan ◽  
Gilberto Umana-Membreno ◽  
Jarek Antoszewski ◽  
Lorenzo Faraone
Keyword(s):  
Electron Transfer ◽  
Quantum Well ◽  
Feasibility Study ◽  
Infrared Photodetectors ◽  
Spectral Tuning ◽  
Infrared Band ◽  
Quantum Well Infrared Photodetectors ◽  
Long Wave ◽  
Long Wave Infrared

scholarly journals Design of 40× imaging objective lens in long-wave infrared band

2021 ◽  
Vol 42 (2) ◽  
pp. 229-235
Author(s):  
LI Jincheng ◽  
◽  
◽  
XIE Hongbo ◽  
YANG Lei ◽  
...  
Keyword(s):  
Objective Lens ◽  
Infrared Band ◽  
Long Wave ◽  
Long Wave Infrared

scholarly journals ANALYSIS AND REMEDIATION OF THE 2013 LAC-MÉGANTIC TRAIN DERAILMENT

2016 ◽  
Vol XLI-B8 ◽  
pp. 17-23
Author(s):  
Suzanne Brunke ◽  
Guy Aubé ◽  
Serge Legaré ◽  
Claude Auger
Keyword(s):  
High Resolution ◽  
Situational Awareness ◽  
Value Added ◽  
Weather Conditions ◽  
Time Of Day ◽  
Observation Data ◽  
Train Derailment ◽  
Space Agency ◽  
Information Products ◽  
Earth Observation Data

On July 6, 2013 a train owned by Montréal, Maine & Atlantic Railway (MMA) Company derailed in Lac-Mégantic, Quebec, Canada triggering the explosion of the tankers carrying crude oil. Several buildings in the downtown core were destroyed. The Sûreté du Québec confirmed the death of 47 people in the disaster. Through the Canadian Space Agency (CSA) Rapid Information Products and Services (RIPS) program, MDA developed value-added products that allowed stakeholders and all levels of government (municipal, provincial and federal) to get an accurate picture of the disaster. The goal of this RIPS Project was to identify the contribution that remote sensing technology can provide to disasters such as the train derailment and explosion at Lac-Mégantic through response and remediation monitoring. Through monitoring and analysis, the Lac-Mégantic train derailment response and remediation demonstrated how Earth observation data can be used for situational awareness in a disaster and in documenting the remediation process. Both high resolution optical and RADARSAT-2 SAR image products were acquired and analyzed over the disaster remediation period as each had a role in monitoring. High resolution optical imagery provided a very clear picture of the current state of remediation efforts, however it can be difficult to acquire due to cloud cover and weather conditions. The RADARSAT-2 SAR images can be acquired in all weather conditions at any time of day making it ideal for mission critical information gathering. MDA’s automated change detection processing enabled rapid delivery of advanced information products.

scholarly journals Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

2015 ◽  
Vol 12 (4) ◽  
pp. 1327-1388 ◽  
Author(s):  
R. Fernandes ◽  
F. Braunschweig ◽  
F. Lourenço ◽  
R. Neves
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Oil Spill ◽  
Situational Awareness ◽  
Assessment Tool ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Automatic Identification ◽  
Decision Support Model ◽  
Risk Levels

Abstract. The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events. A combined methodology to dynamically estimate time and space variable shoreline risk levels from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area – Portuguese Continental shelf – with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time. Shoreline risks can be computed in real-time or from previously obtained data. Results show the ability of the proposed methodology to estimate the risk properly sensitive to dynamic metocean conditions and to oil transport behaviour. The integration of meteo-oceanic + oil spill models with coastal vulnerability and AIS data in the quantification of risk enhances the maritime situational awareness and the decision support model, providing a more realistic approach in the assessment of shoreline impacts. The risk assessment from historical data can help finding typical risk patterns, "hot spots" or developing sensitivity analysis to specific conditions, whereas real time risk levels can be used in the prioritization of individual ships, geographical areas, strategic tug positioning and implementation of dynamic risk-based vessel traffic monitoring.

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