# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Experiment Results  
#### 4.3.1 Comparison with Other Methods  
The proposed method is compared with open-source [11] and non-open-source [9; 10; 13; 15; 38] methods at different image resolutions. The directions of Drone to Satellite and Satellite to drone are both tested. The color transfer module is applied in both training and testing stage. The evaluation metrics include R@1, R@5, R@10, and AP. The experimental results in Table 3 illustrate that our testing results in the image size of \(256\times 256\) achieve state-of-the-art results with a 6.07% improvement on R@1 and a 5.78% improvement on AP. In Figure 6, some results are shown. The top-\(k\) recall rate measures the rank position of positive samples among all reference samples. The positive effect of high-precision resolution images on the experiment results has been verified in previous studies [9; 10; 15]. Compared with the best CNN-based method PCL [10], the proposed method considers the global positioning through a DeiT structure. Compared with the Transformer-based method FSRA [15], which achieved the previous best performance, the proposed method only trained one image retrieval network and achieved a better result due to the designed loss function and color transfer module. In summary, the method in this paper can realize better results on smaller-resolution images than previous methods, which can further prove the effectiveness of our methods.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **R@1** & **AP** & **Inference** \\  & & & **Time** \\ \hline Ours with & & & \\ Euclid distance & **91.57** & **93.31** & 0.310 \\ Ours with & & & \\ cosine similarity & 86.60 & 88.78 & **0.278** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different similarity calculation methods. The image size is \(256\times 256\). (Bold numbers mean the best results).  
Figure 5: The influence of hyper-parameter \(\beta\).  
Although the results in \(256\times 256\) image resolution have achieved the SOTA results in a lower computation cost, the experiment of the image size in \(512\times 512\) is also tested. The results have a 6.67% improvement in R@1 and a 6.13% improvement in AP, outperforming the previous methods. However, doubling the length and width of the image leads to a four-fold increase in the usage of computing resources. Since the algorithm with a heavy computation consumption is unsuitable for the practical application of UAV, and the gap between \(256\times 256\) and \(512\times 512\) image resolution is acceptable, the \(256\times 256\) image resolution is adopted for the subsequent experiment. In addition, since the similarity calculation matrix of drone-to-satellite and satellite-to-drone is identical, the difference only existed in the calculation of image retrieval accuracy. As a result, only the drone-to-satellite results are calculated in the subsequent experiments to present the results more succinctly. The incorrect retrieval mainly occurs in the case of two buildings with similar shape features. The reason may be the influence of the self-attention mechanism, which mainly focuses on the building areas in the image for feature extraction.