Translating multispectral imagery to nighttime imagery via conditional generative adversarial networks

TL;DR

This paper demonstrates that conditional GANs can effectively translate multispectral satellite images into realistic nighttime imagery, aiding nighttime remote sensing applications.

Contribution

It introduces a modified pix2pix framework for multispectral-to-nighttime translation using satellite data, filling a knowledge gap in nighttime light composition understanding.

Findings

Successful multispectral-to-nighttime image translation achieved

Generated images closely resemble ground-truth with social media data integration

Provides new methods for nighttime remote sensing challenges

Abstract

Nighttime satellite imagery has been applied in a wide range of fields. However, our limited understanding of how observed light intensity is formed and whether it can be simulated greatly hinders its further application. This study explores the potential of conditional Generative Adversarial Networks (cGAN) in translating multispectral imagery to nighttime imagery. A popular cGAN framework, pix2pix, was adopted and modified to facilitate this translation using gridded training image pairs derived from Landsat 8 and Visible Infrared Imaging Radiometer Suite (VIIRS). The results of this study prove the possibility of multispectral-to-nighttime translation and further indicate that, with the additional social media data, the generated nighttime imagery can be very similar to the ground-truth imagery. This study fills the gap in understanding the composition of satellite observed nighttime light and provides new paradigms to solve the emerging problems in nighttime remote sensing fields, including nighttime series construction, light desaturation, and multi-sensor calibration.