# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 2 Related Works  
### Image Retrieval Methods for Cross-View Geo-Localization  
For geo-localization tasks, vision-based methods that utilize GPS-tagged satellite images to estimate the location of a set of ground-view or aerial-view images by searching for similar images through various retrieval algorithms have gained significant attention [18]. The feature-based method is first proposed to search for similar images in cross-view. [PERSON] and [PERSON] [19] extended the former approach by using SIFT feature descriptors [20], and sorted the images by RANSAC [21]. [PERSON] and [PERSON] [22] improved the Bag-of-Words (BOW) technique [23] by clustering the images for similar landmarks. [PERSON] et al. [24] pruned outliers and incorporated approximate feature matching using GMCP, further improving nearest-neighbor matching. [PERSON] et al. [25] adopted a BOW method with multiple descriptors. However, these methods encountered difficulties because the same landmarks in cross-view images most likely looked different, leading to feature point matching failure. Since the convolution operation can automatically extract features without designing feature descriptors manually, many CNN-based geo-localization algorithms have been proposed to overcome the inconsistency of cross-view features. CVM-Net [26] used the fully convolutional layers to extract image features and developed global descriptors for image retrieval using NetVLAD. In Ref. [26], the soft-margin triplet loss function was more effective than adopting the max-margin. The extension work of CVM-Net [27] proposed a  
Figure 1: The samples of UAV and satellite images geo-localization are shown. The image retrieval task is between a satellite image block and several drone view images. Our method achieved image retrieval mainly by obtaining the cross-view consistent attention information. Markov localization framework to realize geo-localization in the video stream. Inspired by the spatial localization of humans utilizing orientation information, [PERSON] and [PERSON] [28] designed a Siamese network to encode the orientation to reduce the variation of cross-view images. The effectiveness of combined geometric information in geo-localization is verified. [PERSON] and [PERSON] [29] proposed CNN-based image retrieval method combing with a semantically driven data augmentation technique. [PERSON] et al. [30] implemented the CVFT as network layers based on the CNN network, benefiting the comparison between cross-view images. The extension study of CVFT [31] realized an accurate 3-DoF localization of ground-view images. Although CNN-based methods can extract cross-view consistency information, visual disturbances such as long dynamic features (e.g., weather and vegetation) and transient objects (e.g., cars and pedestrians) widely exist in cross-view images, which limit the model performance. Therefore, a more flexible approach for image representation in cross-view is worth studying. In recent years, the proposed Vision Transformer (ViT) [16] can better learn the context information of images, which is conducive to the global consistency information extraction of images from different perspectives, outperforming the CNN-based methods. [PERSON] et al. [32] used semantic segmentation and generative adversarial networks to process satellite images and ViT to retrieve images. [PERSON] et al. [33] combined CNN and Transformer to extract the spatial configuration of visual feature layouts. [PERSON] et al. [34] applied regular polar transform to reduce the differences between ground and satellite images. CNN and Transformer structure were also used to extract image features. [PERSON] et al. [35] developed a layer-to-layer Transformer (L2 LTR) for geo-localization. The positional encoding of the Transformer is exploited, which benefited from aligning geometric configurations between cross-view images. However, the network structure combined CNN and Transformer, bringing large GPU memory consumption.