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

TL;DR

This study compares various machine learning methods to transform panchromatic nighttime satellite images into RGB images, validating their effectiveness across multiple urban areas and land-use proxies.

Contribution

It introduces a comparative analysis of machine learning techniques for converting panchromatic nighttime satellite images into RGB images, highlighting their relative performance.

Findings

Linear, kernel, and random forest regressions show higher correlation and lower error.

Elastic map approach yields more consistent predictions.

Models effectively replicate original RGB images from panchromatic data.

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 paper, 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 the 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.