Peering into lunar permanently shadowed regions with deep learning
In: Nature Communications Nature Communications, Jg. 12 (2021), Heft 1, S. 1-12
Online
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Zugriff:
The lunar permanently shadowed regions (PSRs) are expected to host large quantities of water-ice, which are key for sustainable exploration of the Moon and beyond. In the near future, NASA and other entities plan to send rovers and humans to characterize water-ice within PSRs. However, there exists only limited information about the small-scale geomorphology and distribution of ice within PSRs because the orbital imagery captured to date lacks sufficient resolution and/or signal. In this paper, we develop and validate a new method of post-processing LRO NAC images of PSRs. We show that our method is able to reveal previously unseen geomorphological features such as boulders and craters down to 3 meters in size, whilst not finding evidence for surface frost or near-surface ice. Our post-processed images significantly facilitate the exploration of PSRs by reducing the uncertainty of target selection and traverse/mission planning.
Nature Communications, 12
ISSN:2041-1723
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Peering into lunar permanently shadowed regions with deep learning
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| Autor/in / Beteiligte Person: | Lopez-Francos, Ignacio ; Shirley, M. ; Moseley, Benjamin ; Valentin Tertius Bickel |
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| Zeitschrift: | Nature Communications Nature Communications, Jg. 12 (2021), Heft 1, S. 1-12 |
| Veröffentlichung: | Nature Publishing Group UK, 2021 |
| Medientyp: | unknown |
| ISSN: | 2041-1723 (print) |
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