MSM: a scaling-based feature matching algorithm for images with large-scale differences

Feature matching represents a fundamental and critical problem in various tasks, including 3D reconstruction, simultaneous localization and mapping (SLAM) and preprocessing for remote sensing images. However, existing methods frequently fail to produce high-quality results on images with large-scale...

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Published inInternational journal of digital earth Vol. 18; no. 1
Main Authors Ge, Qifeng, Du, Xiaoping, Xu, Chen, Xu, Jianhao, Yan, Zhenzhen, Fan, Xiangtao
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 31.12.2025
Taylor & Francis Ltd
Taylor & Francis Group
Subjects
Algorithms
Datasets
Feature matching
Image quality
Image reconstruction
local feature
Matching
multi-scale dataset
multi-scale feature matching
Remote sensing
Satellite imagery
Simultaneous localization and mapping
Online AccessGet full text
ISSN1753-8947
1753-8955
1753-8955
DOI10.1080/17538947.2025.2543562

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Abstract Feature matching represents a fundamental and critical problem in various tasks, including 3D reconstruction, simultaneous localization and mapping (SLAM) and preprocessing for remote sensing images. However, existing methods frequently fail to produce high-quality results on images with large-scale differences. In light of limitations, this study proposed a feature matching algorithm based on scaling, MSM (Multi-Scale Matching), which enhances feature matching performance in images with large-scale variations. This algorithm extract feature points across multiple scales, identifying them as scale-invariant keypoints. In addition, it combines descriptors from different scales to construct composite descriptors, to improve the robustness and accuracy of the matching process. For a more comprehensive evaluation of the MSM, we have meticulously devised a scale difference index (SDI) and constructed a multi-scale dataset (MSD) by SDI. The results of feature matching experiments demonstrated that the MSM algorithm outperforms current state-of-the-art methods regarding generality and effectiveness in several benchmark tests. This study presents a novel approach to feature matching in the context of large-scale differences, which can fulfill the requirements of feature matching for large-scale differences in UAV images, satellite images, etc. The MSD is accessible at https://github.com/KevenGe/MSD-Datasets .
AbstractList Feature matching represents a fundamental and critical problem in various tasks, including 3D reconstruction, simultaneous localization and mapping (SLAM) and preprocessing for remote sensing images. However, existing methods frequently fail to produce high-quality results on images with large-scale differences. In light of limitations, this study proposed a feature matching algorithm based on scaling, MSM (Multi-Scale Matching), which enhances feature matching performance in images with large-scale variations. This algorithm extract feature points across multiple scales, identifying them as scale-invariant keypoints. In addition, it combines descriptors from different scales to construct composite descriptors, to improve the robustness and accuracy of the matching process. For a more comprehensive evaluation of the MSM, we have meticulously devised a scale difference index (SDI) and constructed a multi-scale dataset (MSD) by SDI. The results of feature matching experiments demonstrated that the MSM algorithm outperforms current state-of-the-art methods regarding generality and effectiveness in several benchmark tests. This study presents a novel approach to feature matching in the context of large-scale differences, which can fulfill the requirements of feature matching for large-scale differences in UAV images, satellite images, etc. The MSD is accessible at https://github.com/KevenGe/MSD-Datasets.
Feature matching represents a fundamental and critical problem in various tasks, including 3D reconstruction, simultaneous localization and mapping (SLAM) and preprocessing for remote sensing images. However, existing methods frequently fail to produce high-quality results on images with large-scale differences. In light of limitations, this study proposed a feature matching algorithm based on scaling, MSM (Multi-Scale Matching), which enhances feature matching performance in images with large-scale variations. This algorithm extract feature points across multiple scales, identifying them as scale-invariant keypoints. In addition, it combines descriptors from different scales to construct composite descriptors, to improve the robustness and accuracy of the matching process. For a more comprehensive evaluation of the MSM, we have meticulously devised a scale difference index (SDI) and constructed a multi-scale dataset (MSD) by SDI. The results of feature matching experiments demonstrated that the MSM algorithm outperforms current state-of-the-art methods regarding generality and effectiveness in several benchmark tests. This study presents a novel approach to feature matching in the context of large-scale differences, which can fulfill the requirements of feature matching for large-scale differences in UAV images, satellite images, etc. The MSD is accessible at https://github.com/KevenGe/MSD-Datasets .
Author Fan, Xiangtao
Du, Xiaoping
Yan, Zhenzhen
Xu, Jianhao
Ge, Qifeng
Xu, Chen
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SubjectTerms Algorithms
Datasets
Feature matching
Image quality
Image reconstruction
local feature
Matching
multi-scale dataset
multi-scale feature matching
Remote sensing
Satellite imagery
Simultaneous localization and mapping
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Title MSM: a scaling-based feature matching algorithm for images with large-scale differences
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