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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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2302 |
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| author | Bin Xie Bin Liu Kaichang Di Wai-Chung Liu Yuke Kou Yutong Jia Yifan Zhang |
| author_facet | Bin Xie Bin Liu Kaichang Di Wai-Chung Liu Yuke Kou Yutong Jia Yifan Zhang |
| author_sort | Bin Xie |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cbff3fc93a3a40b7adf65108768243b0 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-cbff3fc93a3a40b7adf65108768243b02025-08-20T03:16:42ZengMDPI AGRemote Sensing2072-42922025-07-011713230210.3390/rs17132302Robust Feature Matching of Multi-Illumination Lunar Orbiter Images Based on Crater Neighborhood StructureBin Xie0Bin Liu1Kaichang Di2Wai-Chung Liu3Yuke Kou4Yutong Jia5Yifan Zhang6Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaLunar 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.https://www.mdpi.com/2072-4292/17/13/2302image matchinglunar orbiter imagesmulti-illuminationcraterneighborhood structure |
| spellingShingle | Bin Xie Bin Liu Kaichang Di Wai-Chung Liu Yuke Kou Yutong Jia Yifan Zhang Robust Feature Matching of Multi-Illumination Lunar Orbiter Images Based on Crater Neighborhood Structure Remote Sensing image matching lunar orbiter images multi-illumination crater neighborhood structure |
| title | Robust Feature Matching of Multi-Illumination Lunar Orbiter Images Based on Crater Neighborhood Structure |
| title_full | Robust Feature Matching of Multi-Illumination Lunar Orbiter Images Based on Crater Neighborhood Structure |
| title_fullStr | Robust Feature Matching of Multi-Illumination Lunar Orbiter Images Based on Crater Neighborhood Structure |
| title_full_unstemmed | Robust Feature Matching of Multi-Illumination Lunar Orbiter Images Based on Crater Neighborhood Structure |
| title_short | Robust Feature Matching of Multi-Illumination Lunar Orbiter Images Based on Crater Neighborhood Structure |
| title_sort | robust feature matching of multi illumination lunar orbiter images based on crater neighborhood structure |
| topic | image matching lunar orbiter images multi-illumination crater neighborhood structure |
| url | https://www.mdpi.com/2072-4292/17/13/2302 |
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