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Challenges and best practices for deriving temperature data from an uncalibrated UAV thermal infrared camera

Kelly, Julia LU ; Kljun, Natascha LU ; Olsson, Per Ola LU ; Mihai, Laura ; Liljeblad, Bengt ; Weslien, Per ; Klemedtsson, Leif and Eklundh, Lars LU (2019) In Remote Sensing 11(5).
Abstract

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... (More)

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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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Calibration, FLIR, NUC, Radiometric, Remote sensing, Temperature, Thermal infrared, UAS, UAV, Vignetting
in
Remote Sensing
volume
11
issue
5
article number
567
pages
21 pages
publisher
MDPI AG
external identifiers
  • scopus:85062945269
ISSN
2072-4292
DOI
10.3390/rs11050567
language
English
LU publication?
yes
id
577c7b0c-2990-4f53-a341-22d613a61fc8
date added to LUP
2019-03-25 08:47:29
date last changed
2020-01-16 03:49:10
@article{577c7b0c-2990-4f53-a341-22d613a61fc8,
  abstract     = {<p>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</p>},
  author       = {Kelly, Julia and Kljun, Natascha and Olsson, Per Ola and Mihai, Laura and Liljeblad, Bengt and Weslien, Per and Klemedtsson, Leif and Eklundh, Lars},
  issn         = {2072-4292},
  language     = {eng},
  month        = {03},
  number       = {5},
  publisher    = {MDPI AG},
  series       = {Remote Sensing},
  title        = {Challenges and best practices for deriving temperature data from an uncalibrated UAV thermal infrared camera},
  url          = {https://lup.lub.lu.se/search/ws/files/62130044/kellyetal2019.pdf},
  doi          = {10.3390/rs11050567},
  volume       = {11},
  year         = {2019},
}