Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

Vasit Sagan, Maitiniyazi Maimaitijiang, Sidike Paheding, Sourav Bhadra, Nichole Gosselin, Max Burnette, Jeffrey Demieville, Sean Hartling, David Lebauer, Maria Newcomb, Duke Pauli, Kyle T. Peterson, Nadia Shakoor, Abby Stylianou, Charles S. Zender, Todd C. Mockler

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
DOIs
StatePublished - 2022

Keywords

  • Artificial intelligence
  • Atmospheric measurements
  • Atmospheric modeling
  • Bidirectional reflectance distribution function (BRDF) correction
  • Calibration
  • Cameras
  • Hyperspectral imaging
  • Radiometry
  • high-throughput phenotyping
  • image quality assessment
  • shadow compensation
  • soil removal.
  • soil removal

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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