Lunar exploration missions require detailed and accurate planning to ensure their safety. Remote sensing data, such as optical satellite imagery acquired by lunar orbiters, are key for the identification of future landing and mission sites. Here robot- and astronaut-scale obstacles are the most relevant to resolve; however, the spatial resolution of the available image data is often insufficient, particularly in the poorly illuminated polar regions of the moon, leading to uncertainty. This work shows how a novel single-image superresolution (SISR) application, the Adversarial Network for Uncertainty-Based Image SR (ANUBIS), can enhance lunar surface imagery by improving the resolution by a factor of two, outperforming other approaches and benchmarks. The enhanced images improve the reliability and detail of lunar traverse planning and topographic reconstruction, while providing an estimate of the uncertainty associated with the enhancement process, vital to ensure mission planning integrity. This work demonstrates how machine-learning-driven processing can enhance existing data products to maximize their value for science and the exploration of the moon and other celestial bodies.
