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

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.

Automated summary

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.