Image Matching for UAV Geolocation: Classical and Deep Learning Approaches

Fatih Baykal, Mehmet İrfan Gedik, Constantino Carlos Reyes-Aldasoro, Cefa Karabağ

Open source

DOI: 10.3390/jimaging11110409
Published: 2025-11-12
Container: Journal of Imaging
Publisher: MDPI AG
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3390/jimaging11110409,
  title = {Image Matching for UAV Geolocation: Classical and Deep Learning Approaches},
  author = {Fatih Baykal and Mehmet İrfan Gedik and Constantino Carlos Reyes-Aldasoro and Cefa Karabağ},
  year = {2025},
  journal = {Journal of Imaging},
  doi = {10.3390/jimaging11110409},
  url = {https://doi.org/10.3390/jimaging11110409}
}

RIS

TY  - JOUR
TI  - Image Matching for UAV Geolocation: Classical and Deep Learning Approaches
AU  - Fatih Baykal
AU  - Mehmet İrfan Gedik
AU  - Constantino Carlos Reyes-Aldasoro
AU  - Cefa Karabağ
PY  - 2025
JO  - Journal of Imaging
DO  - 10.3390/jimaging11110409
UR  - https://doi.org/10.3390/jimaging11110409
ER  -

APA

Baykal, F., Gedik, M. İ., Reyes-Aldasoro, C. C., & Karabağ, C. (2025). Image Matching for UAV Geolocation: Classical and Deep Learning Approaches. Journal of Imaging. https://doi.org/10.3390/jimaging11110409

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crossref · retrieved 2026-09-24T20:04:17.649Z