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13 June 2024 An enhanced feature matching multi-temporal port remote sensing image registration network E-SuperGlue
Xinyu He, Shuyi Feng, Wenbo Shao, Hengxiang He, Liming Wu
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Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131800H (2024) https://doi.org/10.1117/12.3033758
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China
Abstract
Multi-temporal collaborative analysis of port scenes can enhance the representation ability of image scenes, and image registration is required before multi-temporal analysis. In this paper, an image registration network E-SuperGlue with enhanced feature matching is proposed to solve the problems such as the difficulty of extracting feature points and matching feature descriptors for port multi-temporal image registration. Our network takes SuperGlue network as the basic framework. Firstly, Focus is introduced into the feature extraction network to increase the number to increase the number and detection rate of feature points. Secondly, LFPE module is added to feature matching network coding module to improve the information efficiency of feature descriptor coding. Finally, an improved multi-layer sensing structure E-MLP is added to the feature matching network to improve the utilization rate of channel information.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Xinyu He, Shuyi Feng, Wenbo Shao, Hengxiang He, and Liming Wu "An enhanced feature matching multi-temporal port remote sensing image registration network E-SuperGlue", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131800H (13 June 2024); https://doi.org/10.1117/12.3033758
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KEYWORDS
Image registration
Remote sensing
Feature extraction
Convolution
Neural networks
Deep learning
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