New approach to thermal drift correction in microbolometer thermal cameras

Olbrycht, R.; Więcek, B.

doi:10.1080/17686733.2015.1055675

Cited by 32 publications

(24 citation statements)

References 5 publications

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“…This also explains the pronounced skewness in the non-corrected histograms presented in Figure 5. Based on the authors' results, the temperature variation for this sensor under normal conditions was less than 0.5 • C per minute, similar to the variations reported by Olbrycht and Więcek (2015) [29].…”

Section: Validationsupporting

confidence: 88%

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Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

et al. 2018

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The development of lightweight sensors compatible with mini unmanned aerial vehicles (UAVs) has expanded the agronomical applications of remote sensing. Of particular interest in this paper are thermal sensors based on lightweight microbolometer technology. These are mainly used to assess crop water stress with thermal images where an accuracy greater than 1 • C is necessary. However, these sensors lack precise temperature control, resulting in thermal drift during image acquisition that requires correction. Currently, there are several strategies to manage thermal drift effect. However, these strategies reduce useful flight time over crops due to the additional in-flight calibration operations. This study presents a drift correction methodology for microbolometer sensors based on redundant information from multiple overlapping images. An empirical study was performed in an orchard of high-density hedgerow olive trees with flights at different times of the day. Six mathematical drift correction models were developed and assessed to explain and correct drift effect on thermal images. Using the proposed methodology, the resulting thermally corrected orthomosaics yielded a rate of error lower than 1 • C compared to those where no drift correction was applied.

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Section: Validationsupporting

confidence: 88%

“…Hence, it is necessary to perform thermal drift correction and periodically update the correction values for each detector [28]. Without this correction, the temperature error would increase by approximately 0.7 • C per minute [29].…”

Section: Introductionmentioning

confidence: 99%

Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

et al. 2018

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“…Although external flight characteristics and environmental metrics explained 66% of the long profile variability, the remaining 34% is unaccounted for. This remaining variability is presumably a combination of true diffuse river temperature heterogeneity and internal sensor warming due to power dissipation from camera circuitry (Olbrycht & Więcek, ; Strąkowki, ). Not only does this partially account for why drift is present in night‐time flights, it also indicates that even through minimising all external sources of drift, it will still be difficult to separate true diffuse heterogeneity from drift caused by internal sensor warming.…”

Section: Discussionmentioning

confidence: 99%

“…Not only does this partially account for why drift is present in night‐time flights, it also indicates that even through minimising all external sources of drift, it will still be difficult to separate true diffuse heterogeneity from drift caused by internal sensor warming. Although researchers have published a range of drift compensation methods, these are either experimental hardware‐based techniques (e.g., Olbrycht & Więcek, ; Ribeiro‐Gomes et al, ) or involve modelling or additional image acquisition to remove interimage bias and “normalise” image sequences (e.g., Abolt et al, ; Jensen, McKee, & Chen, ; Mesas‐Carrascosa et al, ), which, when applied over the spatial scales at which diffuse thermal heterogeneity occurs (10 2 –10 4 m), may also have the unwanted effect of removing true longitudinal temperature variability present within the image series. We therefore advocate the development of new processing techniques specific to river environments that are capable of compensating for sUAS‐based TIR drift while preserving true streamwise temperature variability.…”

Section: Discussionmentioning

confidence: 99%

Assessing the potential of drone‐based thermal infrared imagery for quantifying river temperature heterogeneity

Dugdale

Kelleher

Malcolm

et al. 2019

Hydrological Processes

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Climate change is altering river temperature regimes, modifying the dynamics of temperature‐sensitive fishes. The ability to map river temperature is therefore important for understanding the impacts of future warming. Thermal infrared (TIR) remote sensing has proven effective for river temperature mapping, but TIR surveys of rivers remain expensive. Recent drone‐based TIR systems present a potential solution to this problem. However, information regarding the utility of these miniaturised systems for surveying rivers is limited. Here, we present the results of several drone‐based TIR surveys conducted with a view to understanding their suitability for characterising river temperature heterogeneity. We find that drone‐based TIR data are able to clearly reveal the location and extent of discrete thermal inputs to rivers, but thermal imagery suffers from temperature drift‐induced bias, which prevents the extraction of accurate temperature data. Statistical analysis of the causes of this drift reveals that drone flight characteristics and environmental conditions at the time of acquisition explain ~66% of the variance in TIR sensor drift. These results shed important light on the factors influencing drone‐based TIR data quality and suggest that further technological development is required to enable the extraction of robust river temperature data. Nonetheless, this technology represents a promising approach for augmenting in situ sensor capabilities and improved quantification of advective inputs to rivers at intermediate spatial scales between point measurements and “conventional” airborne or satellite remote sensing.

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“…Infrared cameras based on this type of uncooled detectors are cheaper than cooled photon detectors. The disadvantage of cooled detector is the thermal drift [2]. Infrared measurements are exposed to high impact of external conditions, disturbances and ambiguities in the interpretation [3].…”

Section: Introductionmentioning

confidence: 99%

Leaks detection in sequence of environmental infrared images

Bukowska-Belniak¹,

Leśniak²

2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

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This work presents the method of leaks detection in sequence of infrared images. Infrared field measurements were taken on small experimental embankment during flood process. The leak processes that took place in the embankment are observed during experiment in infrared images. Image processing method allowed to localize the leak in the thermal images, despite the impact of external factors that disturb the measurements and differences in objects emissivity in the observed scene.

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New approach to thermal drift correction in microbolometer thermal cameras

Cited by 32 publications

References 5 publications

Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

Assessing the potential of drone‐based thermal infrared imagery for quantifying river temperature heterogeneity

Leaks detection in sequence of environmental infrared images

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