Peering into lunar permanently shadowed regions with deep learning.
In: Nature Communications, Jg. 12 (2021-09-23), Heft 1, S. 1-12
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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. Some regions on the Moon are permanently covered in shadow and are therefore extremely difficult to see into. We develop a deep learning driven algorithm which enhances images of these regions, allowing us to see inside them with high resolution for the first time. [ABSTRACT FROM AUTHOR]
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Peering into lunar permanently shadowed regions with deep learning.
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Autor/in / Beteiligte Person: | Bickel, V. T. ; Moseley, B. ; Lopez-Francos, I. ; Shirley, M. |
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Zeitschrift: | Nature Communications, Jg. 12 (2021-09-23), Heft 1, S. 1-12 |
Veröffentlichung: | 2021 |
Medientyp: | academicJournal |
ISSN: | 2041-1723 (print) |
DOI: | 10.1038/s41467-021-25882-z |
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