# 3D Positioning of Drones through Images
## 2 Related Work
### Transformer  
Currently, deep learning is thriving in image processing, with applications in image classification, object detection, semantic segmentation, and more. Within deep learning, there is a distinction between traditional convolutional neural networks and transformer-based architectures. Traditional CNNs, such as CNN [14], AlexNet [15] and VGG [16], use convolutional layers, pooling layers, and fully connected layers to extract features from images and perform classification or regression tasks. As transformers developed in natural language processing, researchers introduced them to image processing.  
The ViT model, proposed by [PERSON] et al. [17] in 2020 for image classification, divides images into a series of patches, serializes these patches, and inputs them into a Transformer for processing, with final classification through fully connected layers. The DeiT model, proposed by [PERSON] et al. [18] in 2020 for image classification, achieves efficient classification on small-scale datasets through pre-training on large-scale image datasets and knowledge distillation to improve generalization.  
[PERSON] et al. [19] proposed the PVT structure in 2021, combining traditional pyramid features with Transformer advantages to capture feature information at different scales and levels. The PVT model introduces multiple parallel pyramid modules, each composed of multiple levels with a Transformer module at each level. These levels have different resolutions and receptive fields, capturing feature information at various scales through self-attention mechanisms. PCPVT, an improved version of PVT for image classification tasks, incorporates cross-layer connections in each level's context feature fusion module to merge context features from previous levels with current level features. This cross-layer connection design gradually introduces higher-level context information and improves feature representation through feature fusion.  
Image retrieval methods for cross-view geolocation [20] require strict alignment between query images and database images, which is challenging in specific situations. [PERSON] [21] applied this structure to the field of drone image matching and subsequently [PERSON] [6] proposed an FPI method, a new end-to-end approach for direct point searching, using a dual-stream network without shared weights for UAV and satellite image views. A response map is then generated through relationship modeling, with the point of maximum response being the model's predicted current location of the UAV image.  
In OS-FPI [6], the authors proposed a single-stream network. Feature extraction for both UAV and satellite images uses the same backbone. At different stages of feature extraction, the interactivity of Transformers is utilized to establish information exchange between UAV and satellite features. Its characteristics include: (1) Using the Transformer mechanism to establish connections between UAV and satellite images, enabling information exchange and improving accuracy. (2) Retaining feature maps of different sizes at each stage to establish more interactions and connections between subsequent features.