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Deep learning driven interpretation of Chang'E-4 Lunar Penetrating Radar

Roncoroni, G.
•
Forte, E.
•
Santin, I.
altro
Pipan, M.
2024
  • journal article

Periodico
ICARUS
Abstract
We reprocessed Chang’E-4 Lunar Penetrating Radar data collected until 27th March 2023 with a total length of about 1440 m adding >400 m to the longest profile published so far. For data interpretation, we exploited a new Deep Learning-based algorithm to automatically extract reflectors from a processed radar dataset. The results are in terms of horizon probability and have been interpreted by integrating signal attribute analysis with orbital imagery. The approach provides more objective results by minimizing the subjectivity of data interpretation, allowing to link radar reflectors to their geological context and surface structures. For the first time, we imaged dipping layers and at least twenty shallow buried craterform structures within the regolith using Lunar Penetrating Radar data. We further recognized four deeper structures similar to craters, and identified a crater rim crossed by the rover path and visible in satellite imagery.
DOI
10.1016/j.icarus.2024.116219
WOS
WOS:001280586700001
Archivio
https://hdl.handle.net/11368/3097378
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85199268494
https://www.sciencedirect.com/science/article/pii/S0019103524002793
Diritti
open access
license:creative commons
license uri:http://creativecommons.org/licenses/by/4.0/
FVG url
https://arts.units.it/bitstream/11368/3097378/1/Deep learning driven interpretation of Chang_E-4 Lunar Penetrating Radar_LR.pdf
Soggetti
  • Deep learning

  • LPR data

  • Interpretation

  • Attribute analysi

  • Data integration

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