Assessing Radiometric Corrections for UAS Multi-Spectral Imagery in Horticultural Environments

Yu Hsuan Tu, Stuart Phinn, Kasper Johansen, Andrew Robson

Research output: Chapter in Book/Report/Conference proceedingConference contribution


UAS-based multi-spectral imagery is becoming ubiquitous for monitoring and managing various horticultural crops. To accurately measure and monitor their structure and condition and estimate yields, appropriately corrected data must be used to drive the necessary algorithms. There are several popular radiometric correction methods commonly used for UAS-based data correction. However, their relative and absolute accuracies are not known. This study used three flight datasets, including along- and across-tree-row flight patterns in an avocado orchard. Four correction methods were applied to produce at-surface reflectance image mosaics for each flight pattern as well as the grid pattern and the results were compared to assess the reflectance consistency. Results show that no method provided consistently correct at-surface reflectance for the same features. A BRDF correction workflow was being developed to address these limitations. Preliminary application of the BRDF correction shows that it significantly improves the brightness consistency of features across different images.
Original languageEnglish (US)
Title of host publicationIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Print)9781538671504
StatePublished - Nov 16 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The research uses data under the project "Multi-Scale Monitoring Tools for Managing Australia Tree Crops - Industry Meets Innovation", which is funded by Australian Government Department of Agriculture and Water Resources as part of its Rural R&D for Profit Program. All the Python code in this study can be found on author's GitHub:


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