Mr. Yuan Gao Profile

Yuan Gao

at Nanjing Univ of Science and Technology

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Proceedings Article | 24 November 2021 Paper

Mismatches removal in unmanned aerial vehicle image registration using density based method

Yunfei Ye, Zijuan Luo, Xuelian Yu, Kan Ren, Yuan Gao, Qian Chen

Proceedings Volume 12061, 120610P (2021) https://doi.org/10.1117/12.2604182

KEYWORDS: Unmanned aerial vehicles, Image registration, Distortion, Data modeling, Remote sensing, Motion models, Image processing

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For the problems of large viewpoint variation, heavy distortion and small overlap area in UAV images registration, this paper proposes a density analysis based method to remove mismatches in putative feature correspondences. Our method uses intra-cluster topological constraints for mismatch filtering, which is based on a density-based hierarchical clustering algorithm. Compared with other methods that perform mismatch filtering based on neighborhood topological relationship, our method is more robust to viewpoint changes both in horizontal and vertical directions. The algorithm in this paper uses a coarse-to-fine strategy, which starts with establishing putative feature correspondences based on local descriptors, such as SIFT, ORB, etc. After that we focus on removing outliers by clustering these feature points and verifying topology consistency of the clusters in different images. We view the feature point matching problem as a correspondence problem of the same visual model in two images, and clustering the feature points based on density can approximate the separation of multiple visual patterns. We tested our algorithm on a UAV image dataset which includes several pairs of images and their ground truth. These image pairs contain viewpoint changes in horizontal, vertical and their mixture which produce problems of low overlap, image distortion and severe outliers. Experiments demonstrate that our method significantly outperforms the state-of-the-art in terms of matching precision.

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