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Cited by 10Impact of Light on Environment and HealthSatellite Image Processing and PhotogrammetryImage Enhancement Techniques

Coloring Panchromatic Nighttime Satellite Images: Comparing the Performance of Several Machine Learning Methods

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Rybnikova, Nataliya, Portnov, Boris A., Mirkes, Evgeny M., Zinovyev, Andrei, Brook, Anna, Gorban, Alexander N.·2022·IEEE Transactions on Geoscience and Remote Sensing
DOI 10.1109/tgrs.2021.3076011
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Abstract

Artificial light-at-night (ALAN), emitted from the ground and visible from space, marks human presence on earth. Since the launch of the Suomi National Polar Partnership satellite with the Visible Infrared Imaging Radiometer Suite Day–Night Band (VIIRS/DNB) onboard, global nighttime images have significantly improved; however, they remained panchromatic. Although multispectral images are also available, they are either commercial or free of charge, but sporadic. In this article, we use several machine learning techniques, such as linear, kernel, random forest regressions, and elastic map approach, to transform panchromatic VIIRS/DBN into red, green, blue (RGB) images. To validate the proposed approach, we analyze RGB images for eight urban areas worldwide. We link RGB values, obtained from ISS photographs, to panchromatic ALAN intensities, their pixel-wise differences, and several land-use-type proxies. Each dataset is used for model training, while other datasets are used for model validation. The analysis shows that model-estimated RGB images demonstrate a high degree of correspondence with the original RGB images from the ISS database. Yet, estimates, based on linear, kernel, and random forest regressions, provide better correlations, contrast similarity, and lower WMSEs levels, while RGB images, generated using elastic map approach, provide higher consistency of predictions.

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  • Random forest regression is a powerful tool to map the spatial distribution of humanitarian indicators with NTLs accurately.

    “Second, we employ random forest regression which is a powerful tool to map the spatial distribution of humanitarian indicators with NTLsaccurately [18, 26].”

    From Under the Radar -- Auditing Fairness in ML for Humanitarian Mapping (2021) · cites this paper as [26]

    The Random forest regression section (p. 6) confirms that the paper applies random forest regression to nighttime-light imagery, and the Results (p. 6) report good performance on some measures. But its outcome is estimated RGB light values, not the spatial distribution of humanitarian indicators; the reported performance therefore does not establish accuracy for that claimed use. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Rybnikova, N., Portnov, B. A., Mirkes, E. M., Zinovyev, A., Brook, A., & Gorban, A. N.
Affiliations
  • University of Leicester (GB)
  • University of Haifa (IL)
  • Inserm (FR)
ORCID
Venue
IEEE Transactions on Geoscience and Remote Sensing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022
Volume
60
Pages
1-15
ISSN
  • 0196-2892 (ISSN-L)
  • 1558-0644
DOI
10.1109/tgrs.2021.3076011
OpenAlex
W3162720875
Cited by
10
References
5
Citations by year
2026: 2 · 2025: 2 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 2
FWCI
0.61
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, other-oa)
  • arXiv (Cornell University) (cc-by)
Funders
  • Council for Higher Education
  • University of Leicester
  • Ministry of Science and Higher Education of the Russian Federation (075-15-2020-808)
  • Agence Nationale de la Recherche in the Program Investissements d’Avenir (ANR-19-P3IA-0001)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Impact of Light on Environment and Health
  • Physical Sciences › Engineering › Ocean Engineering › Satellite Image Processing and Photogrammetry
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
Keywords
artificial light at night, nighttime satellite imagery, VIIRS Day/Night Band, Suomi NPP, machine learning regression, random forest regression, kernel regression, linear regression, urban light emission, land use, International Space Station imagery

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