# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention  
[PERSON]  
1 State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310012, China; [EMAIL_ADDRESS] (Z.C. ); [EMAIL_ADDRESS] (P.Z. ); 1 [EMAIL_ADDRESS] (X.W. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Y.Z.) [PERSON]  
1 State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310012, China; [EMAIL_ADDRESS] (Z.C. ); [EMAIL_ADDRESS] (P.Z. ); 1 [EMAIL_ADDRESS] (X.W. ); [EMAIL_ADDRESS] (Y.L. ); zhuangyu@  
Image retrieval methods based on deep learning frameworks are widely used in geolocalization. CNN-based methods for UAV and satellite cross-view image retrieval [9; 10; 11; 12; 13] have received wide concern, as these methods extract the invariant information of images from different perspectives through convolution operation. However, the interference features (e.g., buildings with similar colors or shapes) of UAV and satellite images may be incorrectly extracted, as CNN tends to extract local features, which reduces image retrieval accuracy. Moreover, the methods mentioned above rely on the manual geometric segmentation of the image, encountering shortcomings in generalization. To overcome these disadvantages, some Vision-Transformer-based methods [14; 15] have been proposed, for the image context information is used in ViT [16] to extract meaningful information with global consistency. However, these methods require multiple stages of network training because the cross-view information is difficult to learn. For example, the method developed in Ref. [14] includes the entire image training stage and cropped image training stage, and the method developed in Ref. [15] includes the classifier training stage and retrieval training stage. The multiple training stages increase the complexity and time consumption of model training. Although many image retrieval methods have been proposed, UAV and satellite cross-view geo-localization still faces many challenges, mainly due to the specificity of image data in this problem. The most important issue is the inconsistent view angles that increase the difficulty of image retrieval. The drone view is tilted to the ground, while the satellite view is directly above. The difference in perspective will bring about the inconsistency of the size and direction of the image content, as well as the occlusion of buildings and other problems. The time gap between drone and satellite images can cause geo-localization failures since public satellite images are updated infrequently. The scene contents in drone and satellite images (e.g., vegetation, vehicles, and building appearance) have time-variant characteristics. Additionally, these scenes are affected by weather and season. Different visual sensor characteristics can also cause inconsistent color information in satellite and drone images in the same area. As a result, the inconsistency of image contents and color also increases the difficulty of retrieving images. Moreover, the number of drone and satellite images is generally disproportionate, as drone images have a variety of perspectives. The data imbalance will bring difficulties in network training. The unbalanced distribution of samples causes the model to focus mainly on samples with a higher proportion, making it difficult to learn the distribution of the minority classes of images. A cross-view geo-localization method for satellites and UAVs that utilizes image transformation techniques and Vision Transformer architecture is proposed in this paper. The geo-localization task of UAV and satellite images geo-localization is shown in Figure 1. The image retrieval between a satellite image block and several drone view images needs to be solved in order to accomplish geo-localization. This paper first presents a single-stage training method for this problem, which is inspired by the structure of DeiT [17].