# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Ablation Study
#### 4.4.1 Ablation Study of Color Transfer  
In the ablation study of color transfer, the color transfer module is tested and the significant impacts of color transfer are verified (Table 5). Compared with the model without the color transfer, the model with color transfer has an 18.44% increase in R@1, which illustrates a crucial influence on improving the model accuracy. The rationale behind the result is that the image content (e.g., illumination, the appearance of buildings, and vegetation cover) in UAV and satellite pictures are different due to time lag, and the model may incorrectly learn this interference information. The color transfer technique reduces the difference between satellite and drone images for independent attributes of concern for cross-view matching, allowing the model to learn key information. Therefore, the model can neglect the influence of irrelevant information to achieve higher accuracy. Figure 7 demonstrates an example of the attention map of the last layer. The result illustrates that the transformer encoder can perform better for learning correct image information after color transfer.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Training** & **Inference** & **Inference Time** & **AP** \\  & **GPU Memoery** & **GPU Memoery** & **Per-Image** & \\ \hline Ours with & & & & \\ Resnet-50 & 9911 MB & 6867 MB & **0.261** & 70.64 \\ Ours with & & & & \\ ViT-S & 15,707 MB & 5351 MB & 0.315 & 84.50 \\ Ours with & & & & \\ DeiT-S & **8747 MB** & **3551 MB** & 0.310 & **93.31** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The computation cost experiment for different network models. (Bold numbers mean the best results).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Drone to Satellite**} & \multicolumn{5}{c}{**Satellite to Drone**} \\ \cline{2-9}  & **R@1** & **R@5** & **R@10** & **AP** & **R@1** & **R@5** & **R@10** & **AP** \\ \hline Contrastive Loss [13; 58] & 52.39 & - & - & 57.44 & 63.91 & - & - & 52.24 \\ Triplet Loss (M = 0.3) [13; 50] & 55.18 & - & - & 59.97 & 63.62 & - & - & 53.85 \\ Triplet Loss (M = 0.5) [13; 50] & 53.58 & - & - & 58.60 & 64.48 & - & - & 53.15 \\ CVM-Net [13; 26] & 53.21 & - & - & 58.03 & 65.62 & - & - & 54.47 \\ Instance Loss [13] & 58.23 & - & - & 62.91 & 74.47 & - & - & 59.54 \\ LCM [38] & 66.65 & 84.93 & 90.02 & 70.82 & 79.89 & 87.34 & 90.03 & 65.38 \\ RK-Net [11] & 77.60 & - & - & 80.55 & 86.59 & - & - & 75.96 \\ LPN [9] (256 \(\times\) 256) & 75.93 & - & - & 80.80 & 86.45 & - & - & 74.79 \\ LPN [9] (512 \(\times\) 512) & 77.71 & - & - & 80.80 & 90.30 & - & - & 78.78 \\ PCL [10] (512 \(\times\) 512) & 83.27 & 90.32 & 95.56 & 87.32 & 91.78 & 93.35 & 96.45 & 82.18 \\ FSRA [15] (256 \(\times\) 256) & 82.25 & - & - & 84.82 & 87.87 & - & - & 81.53 \\ FSRA [15] (512 \(\times\) 512) & 85.50 & - & - & 87.53 & 89.73 & - & - & 84.94 \\ \hline Ours (256 \(\times\) 256) & 91.57 & **99.29** & **99.68** & 93.31 & **94.44** & **97.00** & **97.86** & 91.55 \\ Ours (512 \(\times\) 512) & **92.17** & 98.91 & 99.40 & **93.66** & **94.44** & 96.43 & 97.43 & **92.17** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparisons between the proposed method and the existing open-source [11] and non-open-source [9; 10; 13; 15; 38] methods at different image resolutions on the public benchmark University-1652. (Bold numbers mean the best results).