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Attention-based neural network with Generalized Mean Pooling for cross-view geo-localization between UAV and satellite

Attention-based neural network with Generalized Mean Pooling for cross-view geo-localization... Cross-view geo-localization is finding images containing the same geographic target in multi-views. For example, given a query image from UAV view, a proposed matching model can find an exact image of the same location in a gallery collected by satellites. Using a UAV-view image to acquire the true-matched satellite-view image with a geo-tag, the current geographic location of the UAV can be easily localized based on flight records. However, due to the extreme change of viewpoints across platforms, traditional image processing methods have met difficulties matching multi-view images. This paper proposed advanced neural network-based approaches, which applied the attention mechanism to the feature learning process to improve the ability to learn essential features from the input image. A different pooling method was also implemented to increase the global descriptor. Our proposed models have significantly improved accuracy and have achieved competitive results on the University-1652 dataset. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Life and Robotics Springer Journals

Attention-based neural network with Generalized Mean Pooling for cross-view geo-localization between UAV and satellite

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References (56)

Publisher
Springer Journals
Copyright
Copyright © International Society of Artificial Life and Robotics (ISAROB) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1433-5298
eISSN
1614-7456
DOI
10.1007/s10015-023-00867-x
Publisher site
See Article on Publisher Site

Abstract

Cross-view geo-localization is finding images containing the same geographic target in multi-views. For example, given a query image from UAV view, a proposed matching model can find an exact image of the same location in a gallery collected by satellites. Using a UAV-view image to acquire the true-matched satellite-view image with a geo-tag, the current geographic location of the UAV can be easily localized based on flight records. However, due to the extreme change of viewpoints across platforms, traditional image processing methods have met difficulties matching multi-view images. This paper proposed advanced neural network-based approaches, which applied the attention mechanism to the feature learning process to improve the ability to learn essential features from the input image. A different pooling method was also implemented to increase the global descriptor. Our proposed models have significantly improved accuracy and have achieved competitive results on the University-1652 dataset.

Journal

Artificial Life and RoboticsSpringer Journals

Published: Aug 1, 2023

Keywords: Attention mechanism; Cross-view image matching; Generalized Mean Pooling; UAV

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