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Identifying the optimal radiometric calibration method for UAV-based multispectral imaging

Louis Daniels (UGent) , Eline Eeckhout (UGent) , Jana Wieme (UGent) , Yves Dejaegher, Kris Audenaert (UGent) and Wouter Maes (UGent)
(2023) REMOTE SENSING. 15(11).
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Abstract
The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images to surface reflectance. Several calibration methods exist, but the advantages and disadvantages of each are not well understood. We performed an empirical analysis of five different methods for calibrating a 10-band multispectral camera, the MicaSense RedEdge MX Dual Camera System, by comparing multispectral images with spectrometer measurements taken in the field on the same day. Two datasets were collected, one in clear-sky and one in overcast conditions on the same field. We found that the empirical line method (ELM), using multiple radiometric reference targets imaged at mission altitude performed best in terms of bias and RMSE. However, two user-friendly commercial solutions relying on one single grey reference panel were only slightly less accurate and resulted in sufficiently accurate reflectance maps for most applications, particularly in clear-sky conditions. In overcast conditions, the increase in accuracy of more elaborate methods was higher. Incorporating measurements of an integrated downwelling light sensor (DLS2) did not improve the bias nor RMSE, even in overcast conditions. Ultimately, the choice of the calibration method depends on required accuracy, time constraints and flight conditions. When the more accurate ELM is not possible, commercial, user-friendly solutions like the ones offered by Agisoft Metashape and Pix4D can be good enough.
Keywords
radiometric atmospheric correction, remote sensing, multispectral imaging, UAV, empirical line method, EMPIRICAL LINE METHOD, NORMALIZATION, MULTISENSOR, SYSTEMS, IMAGERY, CLOUDS, SENSOR

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Citation

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MLA
Daniels, Louis, et al. “Identifying the Optimal Radiometric Calibration Method for UAV-Based Multispectral Imaging.” REMOTE SENSING, vol. 15, no. 11, 2023, doi:10.3390/rs15112909.
APA
Daniels, L., Eeckhout, E., Wieme, J., Dejaegher, Y., Audenaert, K., & Maes, W. (2023). Identifying the optimal radiometric calibration method for UAV-based multispectral imaging. REMOTE SENSING, 15(11). https://doi.org/10.3390/rs15112909
Chicago author-date
Daniels, Louis, Eline Eeckhout, Jana Wieme, Yves Dejaegher, Kris Audenaert, and Wouter Maes. 2023. “Identifying the Optimal Radiometric Calibration Method for UAV-Based Multispectral Imaging.” REMOTE SENSING 15 (11). https://doi.org/10.3390/rs15112909.
Chicago author-date (all authors)
Daniels, Louis, Eline Eeckhout, Jana Wieme, Yves Dejaegher, Kris Audenaert, and Wouter Maes. 2023. “Identifying the Optimal Radiometric Calibration Method for UAV-Based Multispectral Imaging.” REMOTE SENSING 15 (11). doi:10.3390/rs15112909.
Vancouver
1.
Daniels L, Eeckhout E, Wieme J, Dejaegher Y, Audenaert K, Maes W. Identifying the optimal radiometric calibration method for UAV-based multispectral imaging. REMOTE SENSING. 2023;15(11).
IEEE
[1]
L. Daniels, E. Eeckhout, J. Wieme, Y. Dejaegher, K. Audenaert, and W. Maes, “Identifying the optimal radiometric calibration method for UAV-based multispectral imaging,” REMOTE SENSING, vol. 15, no. 11, 2023.
@article{01H3MCCG3T28HECJYRE54NDN99,
  abstract     = {{The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images to surface reflectance. Several calibration methods exist, but the advantages and disadvantages of each are not well understood. We performed an empirical analysis of five different methods for calibrating a 10-band multispectral camera, the MicaSense RedEdge MX Dual Camera System, by comparing multispectral images with spectrometer measurements taken in the field on the same day. Two datasets were collected, one in clear-sky and one in overcast conditions on the same field. We found that the empirical line method (ELM), using multiple radiometric reference targets imaged at mission altitude performed best in terms of bias and RMSE. However, two user-friendly commercial solutions relying on one single grey reference panel were only slightly less accurate and resulted in sufficiently accurate reflectance maps for most applications, particularly in clear-sky conditions. In overcast conditions, the increase in accuracy of more elaborate methods was higher. Incorporating measurements of an integrated downwelling light sensor (DLS2) did not improve the bias nor RMSE, even in overcast conditions. Ultimately, the choice of the calibration method depends on required accuracy, time constraints and flight conditions. When the more accurate ELM is not possible, commercial, user-friendly solutions like the ones offered by Agisoft Metashape and Pix4D can be good enough.}},
  articleno    = {{2909}},
  author       = {{Daniels, Louis and Eeckhout, Eline and Wieme, Jana and Dejaegher, Yves and Audenaert, Kris and Maes, Wouter}},
  issn         = {{2072-4292}},
  journal      = {{REMOTE SENSING}},
  keywords     = {{radiometric atmospheric correction,remote sensing,multispectral imaging,UAV,empirical line method,EMPIRICAL LINE METHOD,NORMALIZATION,MULTISENSOR,SYSTEMS,IMAGERY,CLOUDS,SENSOR}},
  language     = {{eng}},
  number       = {{11}},
  pages        = {{22}},
  title        = {{Identifying the optimal radiometric calibration method for UAV-based multispectral imaging}},
  url          = {{http://doi.org/10.3390/rs15112909}},
  volume       = {{15}},
  year         = {{2023}},
}

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