Uncrewed aerial vehicle radiometric calibration: A comparison of auto exposure and fixed exposure images

Authors: BAGNALL, G - Texas A&M University; THOMASSON, J - Texas A&M University; Yang, Chenghai; WANG, TIANYI - Texas A&M University; HAN, XIONGZHE - Texas A&M University; SIMA, CHAO - Texas A&M University; CHANG, ANJIN - Texas A&M University

Submitted to: The Plant Phenome Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/8/2023
Publication Date: 9/13/2023
Citation: Bagnall, G.C., Thomasson, J.A., Yang, C., Wang, T., Han, X., Sima, C., Chang, A. 2023. Uncrewed aerial vehicle radiometric calibration: A comparison of auto exposure and fixed exposure images. Journal of Applied Remote Sensing (JARS). https://doi.org/10.1002/ppj2.20082.
DOI: https://doi.org/10.1002/ppj2.20082

Interpretive Summary: Remote sensing with unmanned aerial vehicles (UAVs) is increasingly being used in agriculture to provide data on the physical characteristics of plants under field conditions. Data accuracy is critical to provide researchers and growers with the ability to make decisions based on the data with a high level of confidence. This study compared two camera calibration methods for image data collected with a UAV: an autoexposure method and a fixed-exposure method. Both methods were compared to reflectance data from four ground calibration targets and a manned-aircraft image calibrated with commercial calibration tarps. Data analysis showed that the auto-exposure method produced almost twice as much reflectance error on average compared with fixed exposure. The impact of this error on cotton farm management decisions was illustrated with a simulation that suggested a meaningful improvement in revenue with fixed exposure. The results from this study will be useful for the selection of appropriate camera settings and image calibration methods for obtaining quality UAV image data.

Technical Abstract: Remote sensing with unmanned aerial vehicles (UAVs) is increasingly being used in agriculture to provide data on the physical characteristics of plants under field conditions. Data accuracy is critical to provide researchers and growers with the ability to make decisions based on the data with a high level of confidence. In this work we compared two multispectral camera calibration methods for image data collected with a UAV: (1) an auto-exposure method which relies on a single calibration panel and a post hoc calibration, and (2) a fixed-exposure system that uses three shades of gray calibration panels placed in the field followed by using the empirical line calibration method. Both methods were compared to reflectance data from (a) four ground calibration targets measured with a spectroradiometer, and b) a single manned-aircraft image calibrated with commercial calibration tarps. In a band-by-band comparison, the autoexposure method produced almost twice as much radiometric error on average compared with fixed exposure. Because remote-sensing data are commonly converted to spectral indices, the calibration methods were also evaluated by calculating the Visible Atmospherically Resistant Index (VARI) and comparing the resulting data to the manned-aircraft image. Similarly, the auto-exposure method in this case produced twice the error of the fixed-exposure method. The effect of the error was considered in a production-agriculture context by simulating a remote-sensing based prescription map for pesticide application in a cotton field and calculating the number of mis-labeled management zones. The simulation showed that the autoexposure method would be more costly to the farm because of its higher error, roughly $8.00 USD/ha based on the assumptions made.