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Article

Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture

by
Krishna Ribeiro-Gomes
1,
David Hernández-López
2,
José F. Ortega
1,
Rocío Ballesteros
1,
Tomás Poblete
3 and
Miguel A. Moreno
1,*
1
Regional Centre of Water Research, University of Castilla-La Mancha, 02071 Albacete, Spain
2
Institute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, Spain
3
Centro de Investigación y Transferencia en Riegoy Agroclimatología, Universidad de Talca, Talca 3460000, Chile
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(10), 2173; https://doi.org/10.3390/s17102173
Submission received: 2 August 2017 / Revised: 15 September 2017 / Accepted: 19 September 2017 / Published: 23 September 2017
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Abstract

The acquisition, processing, and interpretation of thermal images from unmanned aerial vehicles (UAVs) is becoming a useful source of information for agronomic applications because of the higher temporal and spatial resolution of these products compared with those obtained from satellites. However, due to the low load capacity of the UAV they need to mount light, uncooled thermal cameras, where the microbolometer is not stabilized to a constant temperature. This makes the camera precision low for many applications. Additionally, the low contrast of the thermal images makes the photogrammetry process inaccurate, which result in large errors in the generation of orthoimages. In this research, we propose the use of new calibration algorithms, based on neural networks, which consider the sensor temperature and the digital response of the microbolometer as input data. In addition, we evaluate the use of the Wallis filter for improving the quality of the photogrammetry process using structure from motion software. With the proposed calibration algorithm, the measurement accuracy increased from 3.55 °C with the original camera configuration to 1.37 °C. The implementation of the Wallis filter increases the number of tie-point from 58,000 to 110,000 and decreases the total positing error from 7.1 m to 1.3 m.
Keywords: uncooled thermal camera calibration; microbolometer; unmanned aerial vehicle; image filtering; structure from motion; irrigation management uncooled thermal camera calibration; microbolometer; unmanned aerial vehicle; image filtering; structure from motion; irrigation management

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MDPI and ACS Style

Ribeiro-Gomes, K.; Hernández-López, D.; Ortega, J.F.; Ballesteros, R.; Poblete, T.; Moreno, M.A. Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture. Sensors 2017, 17, 2173. https://doi.org/10.3390/s17102173

AMA Style

Ribeiro-Gomes K, Hernández-López D, Ortega JF, Ballesteros R, Poblete T, Moreno MA. Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture. Sensors. 2017; 17(10):2173. https://doi.org/10.3390/s17102173

Chicago/Turabian Style

Ribeiro-Gomes, Krishna, David Hernández-López, José F. Ortega, Rocío Ballesteros, Tomás Poblete, and Miguel A. Moreno. 2017. "Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture" Sensors 17, no. 10: 2173. https://doi.org/10.3390/s17102173

APA Style

Ribeiro-Gomes, K., Hernández-López, D., Ortega, J. F., Ballesteros, R., Poblete, T., & Moreno, M. A. (2017). Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture. Sensors, 17(10), 2173. https://doi.org/10.3390/s17102173

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