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October 11, 2025Information Technology And ControlOpen Access

Cross-View Image Geo-localization Based on Attention Weight Masks

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Authors

MZMa ZhuPLA Information Engineering UniversitySSSiyue SunPLA Information Engineering UniversityJXJingqian XuAnyang Institute of Technology

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Implication

The method shows enhanced localization accuracy in ground images, integrating attention weight masks and feature fusion.

Key Points

  • The proposed method significantly improves the localization accuracy of ground images with limited field of view.
  • Experimental results on benchmark datasets indicate enhancements in image matching process using attention mechanisms.
  • Integrating Coordinate Attention into ResNet18 allows effective alignment between ground and satellite images.
  • Multi-scale feature fusion enhances the representativeness of image descriptors across different levels.

Cite This Study

Zhu et al. (2025) studied this question.

synapsesocial.com/papers/68e9b2e4ba7d64b6fc13315dhttps://doi.org/10.5755/j01.itc.54.3.41802
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Satellite-Drone Image Cross-View Geolocalization Method Based on Multi-Scale Information and Dual-Channel Attention Mechanism2024 · 13 citations
  2. 2Balancing Precision and Efficiency: Cross-View Geo-Localization with Efficient State Space Models2026
  3. 3ConGeo: Robust Cross-view Geo-localization across Ground View Variations2024
  4. 4Cross-View Geo-Localization via 3D Gaussian Splatting-Based Novel View Synthesis2025
  5. 5Cross-view geo-localization: a survey2024 · 1 citations