期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 19, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2022.3159571
关键词
Correspondences; neighbor context; outlier removal; pose estimation
类别
资金
- National Natural Science Foundation of China [62072223]
- Natural Science Foundation of Fujian Province [2020J01131199]
Finding reliable correspondences between two images is crucial in remote-sensing image registration. We propose a simple yet effective method to collect abundant local information by establishing a local neighborhood structure, extracting and aggregating neighborhood context, and thereby improving the registration performance.
Finding reliable correspondences between two images is a fundamental problem in remote-sensing image registration. In the face of sparse and unordered correspondences, the previous learning-based works often focus on global information and ignore valuable local information. To gather rich local information, we propose a simple and effective approach, the principle of which is to establish a local neighborhood structure for putative correspondences and then extract and aggregate neighborhood context. Specifically, we design a residual block with an innovative normalization operation and then we construct a sub-net, by introducing a competition mechanism in the local neighborhood to enhance the expression ability of features. Finally, we propose a learning-based network, to improve the performance of outlier rejection by extracting neighborhood context and global context. Extensive experiments on relative pose estimation demonstrate that the proposed network surpasses current state-of-the-art approaches on both challenging indoor and outdoor datasets.
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