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 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.