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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Main Authors: Bin Xie, Bin Liu, Kaichang Di, Wai-Chung Liu, Yuke Kou, Yutong Jia, Yifan Zhang
Format: Article
Language:English
Published: MDPI AG 2025-07-01
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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AT binliu robustfeaturematchingofmultiilluminationlunarorbiterimagesbasedoncraterneighborhoodstructure
AT kaichangdi robustfeaturematchingofmultiilluminationlunarorbiterimagesbasedoncraterneighborhoodstructure
AT waichungliu robustfeaturematchingofmultiilluminationlunarorbiterimagesbasedoncraterneighborhoodstructure
AT yukekou robustfeaturematchingofmultiilluminationlunarorbiterimagesbasedoncraterneighborhoodstructure
AT yutongjia robustfeaturematchingofmultiilluminationlunarorbiterimagesbasedoncraterneighborhoodstructure
AT yifanzhang robustfeaturematchingofmultiilluminationlunarorbiterimagesbasedoncraterneighborhoodstructure