Single Image Super-Resolution with Uncertainty Estimation for Lunar Satellite Images

Recently, there has been a renewed interest in returning to the Moon, with many planned missions targeting the south pole. This region is of high scientific and commercial interest, mostly due to the presence of water-ice and other volatiles which could enable our sustainable presence on the Moon and beyond. In order to plan safe and effective crewed and robotic missions, access to high-resolution (<0.5 m) surface imagery is critical. However, the overwhelming majority (99.7%) of existing images over the south pole have spatial resolutions >1 m. In order to obtain better images, the only currently available way is to launch a new satellite mission to the Moon with better equipment to gather more precise data. In this work we develop an alternative that can be used directly on previously gathered data and therefore saving a lot of resources. It consist of a single image super-resolution (SR) approach based on generative adversarial networks that is able to super-resolve existing images from 1 m to 0.5 m resolution, unlocking a large catalogue of images (∼50,000) for a more accurate mission planning in the region of interest for the upcoming missions. We show that our enhanced images reveal previously unseen hazards such as small craters and boulders, allowing safer traverse planning. Our approach also includes uncertainty estimation, which allows mission planners to understand the reliability of the super-resolved images.