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dc.contributor.author
Zhong, Jiageng
dc.contributor.author
Yan, Jianguo
dc.contributor.author
Li, Ming
dc.contributor.author
Barriot, Jean-Pierre
dc.date.accessioned
2023-11-13T13:16:58Z
dc.date.available
2023-11-10T04:41:03Z
dc.date.available
2023-11-13T13:16:58Z
dc.date.issued
2023-12
dc.identifier.issn
0924-2716
dc.identifier.other
10.1016/j.isprsjprs.2023.10.021
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/641230
dc.description.abstract
Topographic reconstruction of the lunar surface or other planets is important to engineering applications and scientific research in a planetary exploration mission. The typical methods of terrain reconstruction are usually based on photogrammetry techniques. Structure-from-Motion (SfM) is one of the most effective and commonly used photogrammetric technologies that estimate three-dimensional structures from two-dimensional image sequences. To find correspondences and align photos, SfM approaches require invariant features, such as Scale Invariant Feature Transform (SIFT). The hand-crafted features, however, seriously degrade performance due to weak texture and low light conditions causing image matching failure, which will directly affect the accuracy and robustness of reconstruction. Robust local descriptors based on deep learning outperform hand-crafted descriptors since convolutional neural networks are more robust than hand-engineered representations. Therefore, a novel and robust deep learning-based local feature extraction method is proposed, comprising two branch networks integrated with attention mechanisms for generating reliable keypoints and descriptors, respectively. Furthermore, a 3D terrain surface reconstruction workflow is constructed by combining it with the modern advanced image matching method and SfM system. The effectiveness of the proposed method and the workflow were verified in experiments using Panoramic Camera (PCAM) images acquired from three waypoints explored by the Yutu-2 lunar rover during the Chang’e-4 mission. We also illustrate how our approach supports other applications, such as creating panoramic mosaics of surface imagery. This provides a new and powerful method for planetary terrain reconstruction at a high spatial resolution that can meet the requirements for rover navigation and positioning, as well as geological analysis of the Moon and other planets.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Local feature
en_US
dc.subject
Deep learning
en_US
dc.subject
Image matching
en_US
dc.subject
Yutu-2 rover
en_US
dc.subject
3D reconstruction
en_US
dc.subject
Structure-from-Motion
en_US
dc.title
A deep learning-based local feature extraction method for improved image matching and surface reconstruction from Yutu-2 PCAM images on the Moon
en_US
dc.type
Journal Article
dc.date.published
2023-11-02
ethz.journal.title
ISPRS Journal of Photogrammetry and Remote Sensing
ethz.journal.volume
206
en_US
ethz.journal.abbreviated
ISPRS j. photogramm. remote sens.
ethz.pages.start
16
en_US
ethz.pages.end
29
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.status
published
en_US
ethz.date.deposited
2023-11-10T04:41:04Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2023-11-13T13:16:59Z
ethz.rosetta.lastUpdated
2024-02-03T06:26:49Z
ethz.rosetta.versionExported
true
ethz.COinS
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