  The geo-localization and navigation technology of unmanned aerial vehicles
(UAVs) in denied environments is currently a prominent research area. Prior
approaches mainly employed a two-stream network with non-shared weights to
extract features from UAV and satellite images separately, followed by related
modeling to obtain the response map. However, the two-stream network extracts
UAV and satellite features independently. This approach significantly affects
the efficiency of feature extraction and increases the computational load. To
address these issues, we propose a novel coarse-to-fine one-stream network
(OS-FPI). Our approach allows information exchange between UAV and satellite
features during early image feature extraction. To improve the model's
performance, the framework retains feature maps generated at different stages
of the feature extraction process for the feature fusion network, and
establishes additional connections between UAV and satellite feature maps in
the feature fusion network. Additionally, the framework introduces offset
prediction to further refine and optimize the model's prediction results based
on the classification tasks. Our proposed model, boasts a similar inference
speed to FPI while significantly reducing the number of parameters. It can
achieve better performance with fewer parameters under the same conditions.
Moreover, it achieves state-of-the-art performance on the UL14 dataset.
Compared to previous models, our model achieved a significant 10.92-point
improvement on the RDS metric, reaching 76.25. Furthermore, its performance in
meter-level localization accuracy is impressive, with 182.62% improvement in
3-meter accuracy, 164.17% improvement in 5-meter accuracy, and 137.43%
improvement in 10-meter accuracy.
