A Coarse-to-Fine Feature Match Network Using Transformers for Remote Sensing Image Registration | |
Liang Chenbin; Dong Yunyun; Changjun Zhao; Zengguo Sun | |
发表期刊 | Remote Sensing |
2023-06 | |
页码 | 3243 |
摘要 | Feature matching is a core step in multi-source remote sensing image registration ap-proaches based on feature. However, for existing methods, whether traditional classical SIFT algo-rithm or deep learning-based methods, they essentially rely on generating descriptors from local regions of feature points, which can lead to low matching success rates due to various challenges, including gray-scale changes, content changes, local similarity, and occlusions between images. Inspired by the human approach of finding rough corresponding regions globally and then carefully comparing local regions, and the excellent global attention property of transformers, the proposed feature matching network adopts a coarse-to-fine matching strategy that utilizes both global and local information between images to predict corresponding feature points. Importantly, the network has great flexibility of matching corresponding points for any feature points and can be effectively trained without strong supervised signals of corresponding feature points and only require the true geometric transformation between images. The qualitative experiment illustrate the effectiveness of the proposed network by matching feature points extracted by SIFT or sampled uniformly. In the quantitative experiments, we used feature points extracted by SIFT, SuperPoint, and LoFTR as the keypoints to be matched. We then calculated the mean match success ratio (MSR) and mean repro-jection error (MRE) of each method at different thresholds in the test dataset. Additionally, boxplot graphs were plotted to visualize the distributions. By comparing the MSR and MRE values as well as their distributions with other methods, we can conclude that the proposed method consistently outperforms the comparison methods in terms of MSR at different thresholds. Moreover, the MSR of the proposed method remains within a reasonable range compared to the MRE of other methods. |
收录类别 | SCI |
WOS记录号 | WOS:001028415300001 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52131 |
专题 | 复杂系统管理与控制国家重点实验室_影像分析与机器视觉 |
推荐引用方式 GB/T 7714 | Liang Chenbin,Dong Yunyun,Changjun Zhao,et al. A Coarse-to-Fine Feature Match Network Using Transformers for Remote Sensing Image Registration[J]. Remote Sensing,2023:3243. |
APA | Liang Chenbin,Dong Yunyun,Changjun Zhao,&Zengguo Sun.(2023).A Coarse-to-Fine Feature Match Network Using Transformers for Remote Sensing Image Registration.Remote Sensing,3243. |
MLA | Liang Chenbin,et al."A Coarse-to-Fine Feature Match Network Using Transformers for Remote Sensing Image Registration".Remote Sensing (2023):3243. |
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