UAV data reliability improvements based on multifunctional GCPs

J. Alex Thomasson; Xiongzhe Han; Cody Bagnall; Chao Sima; Yeyin Shi; William Rooney; Chenghai Yang; Jinha Jung; Anjin Chang

doi:10.1117/12.2518541

7 June 2019 UAV data reliability improvements based on multifunctional GCPs

J. Alex Thomasson, Xiongzhe Han, Cody Bagnall, Chao Sima, Yeyin Shi, William Rooney, Chenghai Yang, Jinha Jung, Anjin Chang

Author Affiliations +

Proceedings Volume 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV; 110080A (2019) https://doi.org/10.1117/12.2518541
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Commercial off-the shelf systems of UAVs and sensors are touted as being able to collect remote-sensing data on crops that include spectral reflectance and plant height. Historically a great deal of effort has gone into quantifying and reducing the error levels in the geometry of UAV-based orthomosaics, but little effort has gone into quantifying and reducing the error of the reflectance and plant-height. We have been developing systems and protocols involving multifunctional ground-control points (GCPs) in order to produce crop phenotypic data that are as repeatable as possible. These multifunctional GCPs aid not only geometric correction, but also image calibration of reflectance and plantheight. The GCPs have known spectral-reflectance characteristics that are used to enable reference-based digital numberto-reflectance calibration of multispectral images. They also have known platform heights that are used to enable reference-based digital surface model-to-height maps. Results show that using these GCPs for reflectance and plantheight calibrations significantly reduces the error levels in reflectance (ca. 50% reduction) and plant-height (ca. 20% reduction) measurements.

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Citation Download Citation

J. Alex Thomasson, Xiongzhe Han, Cody Bagnall, Chao Sima, Yeyin Shi, William Rooney, Chenghai Yang, Jinha Jung, and Anjin Chang "UAV data reliability improvements based on multifunctional GCPs", Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 110080A (7 June 2019); https://doi.org/10.1117/12.2518541

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