Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 60, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3076011
Keywords
Artificial light-at-night (ALAN); elastic map approach; International Space Station (ISS); multiple linear regression; non-linear kernel regression; panchromatic nighttime imagery; RGB nighttime imagery; validation; Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS/DNB)
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Funding
- 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 (PRAIRIE 3IA Institute) [ANR-19-P3IA-0001]
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In this article, machine learning techniques were used to transform panchromatic night light images into RGB images. The analysis showed that model-estimated RGB images demonstrated a high degree of correspondence with the original RGB images. Linear, kernel, and random forest regressions provided better correlations, contrast similarity, and lower WMSEs levels, while RGB images generated using the elastic map approach provided higher consistency of predictions.
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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