Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data
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Author
Sagan, VasitMaimaitijiang, Maitiniyazi
Paheding, Sidike
Bhadra, Sourav
Gosselin, Nichole
Burnette, Max
Demieville, Jeffrey
Hartling, Sean
LeBauer, David S.
Newcomb, Maria
Pauli, Duke
Peterson, Kyle T.
Shakoor, Nadia
Stylianou, Abby
Zender, Charles S.
Mockler, Todd C.
Affiliation
School of Plant Sciences, University of ArizonaArizona Experiment Station, University of Arizona
Issue Date
2021-07-15Keywords
Artificial intelligenceAtmospheric measurements
Bidirectional reflectance distribution function (BRDF) correction
Calibration
Cameras
high-throughput phenotyping
Hyperspectral imaging
image quality assessment
Radiometry
shadow compensation
soil removal
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Sagan, V., Maimaitijiang, M., Paheding, S., Bhadra, S., Gosselin, N., Burnette, M., Demieville, J., Hartling, S., LeBauer, D., Newcomb, M., Pauli, D., Peterson, K. T., Shakoor, N., Stylianou, A., Zender, C. S., & Mockler, T. C. (2021). Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data. IEEE Transactions on Geoscience and Remote Sensing.Rights
Copyright © The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform radiometric calibration. A kernel-driven semiempirical BRDF correction method based on the Ross Thick-Li Sparse (RTLS) model was used to normalize the data for both changes in solar elevation and sensor view angle differences attributed to pixel location within the field of view. Following rigorous radiometric and BRDF corrections, novel rule-based methods were developed to conduct automatic soil removal; and a newly proposed approach was used for image quality assessment; additionally, shadow masking and plot-level feature extraction were carried out. Our results show that the automated calibration, processing, storage, and analysis pipeline developed in this work can effectively handle massive amounts of hyperspectral data and address the urgent challenges related to the production of sustainable bioenergy and food crops, targeting methods to accelerate plant breeding for improving yield and biomass traits.Note
Open access articleISSN
0196-2892Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1109/TGRS.2021.3091409
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Except where otherwise noted, this item's license is described as Copyright © The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.