Robust Feature Matching of Multi-Illumination Lunar Orbiter Images Based on Crater Neighborhood Structure
Lunar orbiter image matching is a critical process for achieving high-precision lunar mapping, positioning, and navigation. However, with the Moon’s weak-texture surface and rugged terrain, lunar orbiter images generally suffer from inconsistent lighting conditions and exhibit varying degrees of non...
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| Päätekijät: | , , , , , , |
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| Aineistotyyppi: | Artikkeli |
| Kieli: | englanti |
| Julkaistu: |
MDPI AG
2025-07-01
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| Sarja: | Remote Sensing |
| Aiheet: | |
| Linkit: | https://www.mdpi.com/2072-4292/17/13/2302 |
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| Yhteenveto: | Lunar orbiter image matching is a critical process for achieving high-precision lunar mapping, positioning, and navigation. However, with the Moon’s weak-texture surface and rugged terrain, lunar orbiter images generally suffer from inconsistent lighting conditions and exhibit varying degrees of non-linear intensity distortion, which pose significant challenges to image traditional matching. This paper presents a robust feature matching method based on crater neighborhood structure, which is particularly robust to changes in illumination. The method integrates deep-learning based crater detection, Crater Neighborhood Structure features (CNSFs) construction, CNSF similarity-based matching, and outlier removal. To evaluate the effectiveness of the proposed method, we created an evaluation dataset, comprising Multi-illumination Lunar Orbiter Images (MiLOIs) from different latitudes (a total of 321 image pairs). And comparative experiments have been conducted using the proposed method and state-of-the-art image matching methods. The experimental results indicate that the proposed approach exhibits greater robustness and accuracy against variations in illumination. |
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| ISSN: | 2072-4292 |