SeGCN: A Semantic-Aware Graph Convolutional Network for UAV Geo-Localization

SeGCN: A Semantic-Aware Graph Convolutional Network for UAV Geo-Localization

Cross-view geo-localization via scene matching is crucial in unmanned aerial vehicle (UAV) systems in global navigation satellite system denial environment. However, images in the same scene may undergo geometric distortion and occlusion due to differences in capture viewpoint, time, and platform. T...

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Bibliographic Details
Main Authors: Xiangzeng Liu, Ziyao Wang, Yue Wu, Qiguang Miao
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Geo-localization
graph convolutional networks (GCNs)
sementic inference
transformer
unmanned aerial vehicle (UAV) navigation
Online Access:https://ieeexplore.ieee.org/document/10448528/

Internet

https://ieeexplore.ieee.org/document/10448528/

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