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Single Image Super-Resolution with Uncertainty Estimation for Lunar Satellite Images
Jose Delgado-Centeno · Paula Harder · Ben Moseley · Valentin Bickel · Siddha Ganju · Miguel Olivares · Alfredo Kalaitzis
Event URL: https://openreview.net/forum?id=nmBW_I2JV_8 »

Recently, there has been a renewed interest in returning to the Moon, with many1planned 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.

Author Information

Jose Delgado-Centeno (University of Luxembourg)
Paula Harder (Fraunhofer ITWM)
Ben Moseley (University of Oxford)
Valentin Bickel (Max Planck Institute for Solar System Research)
Siddha Ganju (Nvidia)

Siddha Ganju, an AI researcher who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. As an AI Advisor to NASA FDL, she helped build an automated meteor detection pipeline for the CAMS project at NASA, which ended up discovering a comet. Previously at Deep Vision, she developed deep learning models for resource constraint edge devices. Her work ranges from Visual Question Answering to Generative Adversarial Networks to gathering insights from CERN's petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS. She has served as a featured jury member in several international tech competitions including CES. As an advocate for diversity and inclusion in technology, she speaks at schools and colleges to motivate and grow a new generation of technologies from all backgrounds. She is also the author of O'Reilly's Practical Deep Learning for Cloud, Mobile and Edge.

Miguel Olivares (Universidad de Luxemburgo)
Alfredo Kalaitzis (University of Oxford)

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