Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera

Miniaturized thermal infrared (TIR) cameras that measure surface temperature are increasingly available for use with unmanned aerial vehicles (UAVs). However, deriving accurate temperature data from these cameras is non-trivialsince they are highly sensitive to changes in their internal temperature...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 11; no. 5; p. 567
Main Authors: Kelly, Julia, Kljun, Natascha, Olsson, Per-Ola, Mihai, Laura, Liljeblad, Bengt, Weslien, Per, Klemedtsson, Leif, Eklundh, Lars
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2019
Subjects:
Accuracy
Calibration
Cameras
Computer and Information Sciences
Computer graphics and computer vision
Data- och informationsvetenskap (Datateknik)
Datorgrafik och datorseende
Digital imaging
Earth and Related Environmental Sciences
Ecosystems
Environmental Sciences
Field tests
Flight
FLIR
forest
Geovetenskap och relaterad miljövetenskap
imagery
Infrared cameras
Miljövetenskap
Natural Sciences
Naturgeografi
Naturvetenskap
Nonuniformity
NUC
Physical Geography
radiometric
Remote sensing
scale
Sensors
Spectrum analysis
Surface temperature
Temperature
Temperature dependence
Temperature effects
Temperature gradients
thermal infrared
UAS
UAV
Unmanned aerial vehicles
variability
Vignetting
water-stress
Sweden
Germany
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:Miniaturized thermal infrared (TIR) cameras that measure surface temperature are increasingly available for use with unmanned aerial vehicles (UAVs). However, deriving accurate temperature data from these cameras is non-trivialsince they are highly sensitive to changes in their internal temperature and low-cost models are often not radiometrically calibrated. We present the results of laboratory and field experiments that tested the extent of the temperature-dependency of a non-radiometric FLIR Vue Pro 640. We found that a simple empirical line calibration using at least three ground calibration points was sufficient to convert camera digital numbers to temperature values for images captured during UAV flight. Although the camera performed well under stable laboratory conditions (accuracy ±0.5 °C), the accuracy declined to ±5 °C under the changing ambient conditions experienced during UAV flight. The poor performance resulted from the non-linear relationship between camera output and sensor temperature, which was affected by wind and temperature-drift during flight. The camera’s automated non-uniformity correction (NUC) could not sufficiently correct for these effects. Prominent vignetting was also visible in images captured under both stable and changing ambient conditions. The inconsistencies in camera output over time and across the sensor will affect camera applications based on relative temperature differences as well as user-generated radiometric calibration. Based on our findings, we present a set of best practices for UAV TIR camera sampling to minimize the impacts of the temperature dependency of these systems.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs11050567