Document Type
Article
Publication Date
6-15-2021
Department
Department of Applied Computing
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.
Publication Title
IEEE Transactions on Geoscience and Remote Sensing
Recommended Citation
Sagan, V.,
Maimaitijiang, M.,
Paheding, S.,
Bhadra, S.,
Gosselin, N.,
Burnette, M.,
Demieville, J.,
Hartling, S.,
LeBauer, D.,
Newcomb, M.,
Pauli, D.,
Peterson, K.,
Shakoor, N.,
Stylianou, A.,
Zender, C.,
&
Mockler, T.
(2021).
Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data.
IEEE Transactions on Geoscience and Remote Sensing.
http://doi.org/10.1109/TGRS.2021.3091409
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15127
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2021. Authors. Publisher’s version of record: https://doi.org/10.4271/06-14-01-0004