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Peering into lunar permanently shadowed regions with deep learning
V T Bickel1, B Moseley2, I Lopez-Francos3
1Max Planck Institute for Solar System Research, Göttingen, Germany. bickel@mps.mpg.de.
Nature Communications
|September 24, 2021
Summary
New image processing reveals small-scale features in lunar permanently shadowed regions (PSRs). This method aids future exploration by improving understanding of PSR geomorphology, though it did not detect surface frost or near-surface ice.
Area of Science:
- Lunar science
- Planetary geology
- Remote sensing
Background:
- Lunar permanently shadowed regions (PSRs) are critical for sustainable exploration due to expected water-ice deposits.
- Current orbital imagery lacks the resolution to detail small-scale geomorphology and ice distribution within PSRs.
Purpose of the Study:
- To develop and validate a novel post-processing method for enhancing Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) images of PSRs.
- To improve the characterization of geomorphological features within PSRs for future exploration missions.
Main Methods:
- Post-processing of LRO NAC images using a new technique.
- Analysis of enhanced imagery to identify geomorphological features.
Main Results:
- The developed method successfully revealed previously unobserved geomorphological features, including boulders and craters as small as 3 meters.
- No evidence of surface frost or near-surface ice was detected in the post-processed images.
Conclusions:
- The enhanced imagery significantly aids in planning lunar surface exploration missions by reducing uncertainties in target selection and traverse planning.
- The method improves the understanding of PSR geomorphology, despite not confirming the presence of accessible water ice at the tested scales.
