Open AccessHeterogeneous Image Template Matching Based on Region Proposal Network
Author(s) -
Sha Yue,
Wang Shengzhe,
Yuyong Cui,
Wei Guan,
Xinyi Gao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012093
Subject(s) - matching (statistics) , computer science , template matching , artificial intelligence , residual , image (mathematics) , pattern recognition (psychology) , fusion , channel (broadcasting) , computer vision , algorithm , mathematics , computer network , linguistics , statistics , philosophy
Heterogeneous template matching is important to disaster relief. The method of traditional heterogeneous template matching in real-time performance is so weak and usually has a lower accuracy than deep learning method. A heterogeneous template matching method based on region proposal network is proposed in this paper. Specifically, deep features between the optical images and SAR images are extracted by using residual network, and MRPN, a suitable region proposal network for matching work, is proposed to complete precise positioning of the template region. Finally, the channel fusion is performed on the matching results to get a better result and confidence. It is shown that the real-time performance and the accuracy performance have been greatly improved, which is reaching 97%.