# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 3 Proposed Method
### Color Transfer  
UAV and satellite image data are often collected at different times, even in different years, which brings great difficulties for image retrieval. Moreover, weather and illumination condition influence the integral color. As a result, the time lag between satellite and UAV images leads to inconsistent scene content. Meanwhile, UAVs and satellites usually equip different sensors. They have different imaging methods and image characteristics, which can also cause color differences between drone and satellite images. Therefore, the model introduces a color transfer module to reduce the differences between satellite and drone images due to other interference reasons. Thus, the model can more accurately extract consistent cross-view feature information. Inspired by existing studies [47; 48; 49], a color-space-based color transfer is introduced to assimilate drone and satellite images. One satellite image corresponds to multiple drone images in one scene. If the color distribution of the drone image is used as a reference, the satellite image has to be transferred numerous times according to different color distributions. Therefore, the color distribution of the satellite images is the reference for the UAV images.  
The original UAV and satellite images are stored in RGB color space. RGB color space is based on three basic colors, R(Red), G(Green), and B(Blue), which are superimposed to produce a rich and wide range of colors. However, customary RGB color space is not orthogonal, and the values in the R, G, and B channels are not independent. As a result, the values of the three channels, assumed to be normally distributed, cannot be transferred independently through the mean and standard deviation of each channel. Therefore, the RGB color space is transferred to the orthogonal \(\ell\alpha\beta\) color space for each image.  
Orthogonal linear transformation is used to transform the red, green, and blue signals perceived by the human visual system into three unrelated color components. It consists of a luminance component \(\ell\), and two chrominance components \(\alpha\) and \(\beta\). The satellite image is regarded as the source image \(S^{k},k=(\ell,\alpha,\beta)\), and the UAV image is regarded as the target image \(T^{k},k=(\ell,\alpha,\beta)\). Then, the standard deviation and mean value of the satellite image \(\sigma_{s}^{k},\hat{S}^{k},k=(\ell,\alpha,\beta)\) and the UAV image \(\sigma_{t}^{k},\hat{T}^{k}k=(\ell,\alpha,\beta)\) are calculated on every channel in the \(\ell\alpha\beta\) color space. The ratio of standard deviation between the drone image and satellite image determines the amplitude of color variation. Therefore, for a transferred UAV image \(I^{k},k=(\ell,\alpha,\beta)\), the formula of color transfer is presented as follows:  
\[I_{k}=\frac{\sigma_{t}^{k}}{\sigma_{s}^{k}}(S^{k}-\hat{S}^{k})+\hat{T}^{k},k=( \ell,\alpha,\beta) \tag{2}\]Finally, we transfer the \(\ell\alpha\beta\) drone image to RGB color space for visualization. The samples of color transfer are shown in Figure 3. The color transfer technique is used to remap the brightness of the reference image, as shown in Equation (2). Compared with histogram matching, the color correction technique can prevent color distortion and has stronger adaptability to different images. The disadvantage of this technology is that it does not have a noticeable effect on images with a rich color content. The proposed method transfers image color before training to avoid occupying computing resources during network training. In the testing stage, the color transfer formula is consistent with the color transfer formula in the training stage. Although the real-time satellite images in the testing stage are unknown during the training stage, the UAV images in the testing stage will be transferred to the same color distribution as historical satellite images, thereby avoiding data leakage and unfair comparison.