Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions

Potential of tensor decomposition

Sheng Wang, Andreas Baum, Pablo J. Zarco-Tejada, Carsten Dam-Hansen, Anders Thorseth, Peter Bauer-Gottwein, Filippo Bandini, Monica Garcia*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55oN latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3%. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes.

Original languageEnglish
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume155
Pages (from-to)58-71
ISSN0924-2716
DOIs
Publication statusPublished - 2019

Keywords

  • Cloud shadow removal
  • Reflectance
  • Sensor calibration
  • Tucker tensor decomposition
  • Unmanned Aerial System

Cite this

@article{aeeec7f90f6b4b63a8a6bed6b758c0d5,
title = "Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition",
abstract = "Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55oN latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3{\%}. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes.",
keywords = "Cloud shadow removal, Reflectance, Sensor calibration, Tucker tensor decomposition, Unmanned Aerial System",
author = "Sheng Wang and Andreas Baum and Zarco-Tejada, {Pablo J.} and Carsten Dam-Hansen and Anders Thorseth and Peter Bauer-Gottwein and Filippo Bandini and Monica Garcia",
year = "2019",
doi = "10.1016/j.isprsjprs.2019.06.017",
language = "English",
volume = "155",
pages = "58--71",
journal = "I S P R S Journal of Photogrammetry and Remote Sensing",
issn = "0924-2716",
publisher = "Elsevier",

}

TY - JOUR

T1 - Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions

T2 - Potential of tensor decomposition

AU - Wang, Sheng

AU - Baum, Andreas

AU - Zarco-Tejada, Pablo J.

AU - Dam-Hansen, Carsten

AU - Thorseth, Anders

AU - Bauer-Gottwein, Peter

AU - Bandini, Filippo

AU - Garcia, Monica

PY - 2019

Y1 - 2019

N2 - Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55oN latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3%. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes.

AB - Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55oN latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3%. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes.

KW - Cloud shadow removal

KW - Reflectance

KW - Sensor calibration

KW - Tucker tensor decomposition

KW - Unmanned Aerial System

U2 - 10.1016/j.isprsjprs.2019.06.017

DO - 10.1016/j.isprsjprs.2019.06.017

M3 - Journal article

VL - 155

SP - 58

EP - 71

JO - I S P R S Journal of Photogrammetry and Remote Sensing

JF - I S P R S Journal of Photogrammetry and Remote Sensing

SN - 0924-2716

ER -