Within research on the cross-view geolocation of UAVs, differences in image sources and interference from similar scenes pose huge challenges. Inspired by multimodal machine learning, in this paper, we design a single-stream pyramid transformer network (SSPT). The backbone of the model uses the self-attention mechanism to enrich its own internal features in the early stage and uses the cross-attention mechanism in the later stage to refine and interact with different features to eliminate irrelevant interference. In addition, in the post-processing part of the model, a header module is designed for upsampling to generate heat maps, and a Gaussian weight window is designed to assign label weights to make the model converge better. Together, these methods improve the positioning accuracy of UAV images in satellite images. Finally, we also use style transfer technology to simulate various environmental changes in order to expand the experimental data, further proving the environmental adaptability and robustness of the method. The final experimental results show that our method yields significant performance improvement: The relative distance score (RDS) of the SSPT-384 model on the benchmark UL14 dataset is significantly improved from 76.25% to 84.40%, while the meter-level accuracy (MA) of 3 m, 5 m, and 20 m is increased by 12%, 12%, and 10%, respectively. For the SSPT-256 model, the RDS has been increased to 82.21%, and the meter-level accuracy (MA) of 3 m, 5 m, and 20 m has increased by 5%, 5%, and 7%, respectively. It still shows strong robustness on the extended thermal infrared (TIR), nighttime, and rainy day datasets.

UAVs typically rely on satellite navigation for positioning, yet this method proves ineffective in instances where the signal is inadequate or communication is disrupted. Visually based positioning technology has emerged as a reliable alternative. In this paper, we propose a novel end-to-end network, OBTPN. In the initial phase of the model, we optimized the distribution of attention within the primary network, aiming to achieve a balance between self-attention and cross-attention. Subsequently, we devised a feature fusion head, which enhanced the model’s capacity to process multi-scale information. OBTPN was successfully deployed on an NVIDIA Jetson TX2 onboard computer. This paper also proposes a high-altitude complex environment dataset, Crossview9, which addresses a research gap in the field of high-altitude visual navigation. The performance of the model on this dataset is also evaluated. Additionally, the dataset was processed to simulate a low-quality image environment to assess the model’s resilience in challenging weather conditions. The experimental results demonstrate that OBTPN_256 attains an accuracy of 84.55% on the RDS metric, thereby reaching the state-of-the-art (SOTA) level of the UL14 dataset. On the Crossview9 dataset, OBTPN_256 achieves an RDS score of 79.76%, also reaching the SOTA level. Most notably, the model’s high accuracy in low-quality image environments further substantiates its robustness in complex environments.

Heterogeneous data from diverse sensor platforms can complement each other, providing more comprehensive and richer information for the analysis of complex scenarios. To effectively utilise this information for UAV geo-location tasks, accurate feature extraction and fusion methods are crucial. To address this challenge, we propose the Multi-Modal Geo Location Transformer Network (MMGLT), leveraging attention mechanisms to fuse cross-modal features. Unlike traditional dual modal fusion frameworks integrating only drone downward-view and satellite images, MMGLT innovatively uses a triple-input structure to process RGB images, LiDAR-derived auxiliary data, and satellite images. It also designs a tailored cross-attention mechanism to link visual semantics with remote sensing features f inely, enabling more robust multi-source feature learning. Subsequently, MMGLT employs a Global Attention Upsampling Head that refines features layer by layer via attention and adaptive up sampling; by combining dynamic feature re-allocation with multi scale pyramid fusion, it mitigates feature degradation and boosts representational power. With the aim of facilitating subsequent applications, we have successfully deployed the MMGLT model on the NVIDIA Jetson Orin NX compact computer. To evaluate the performance of MMGLT in the task, this paper constructs the CrossView-Van dataset. To the best of our knowledge, this is the first end-to-end geo-location dataset that integrates RGB images, high-precision remote sensing feature data, and satellite images. On this new dataset, MMGLT significantly outperforms existing single-modal methods, and achieves improvement in low-texture forest regions where traditional approaches underperform.

Drones traditionally rely on satellite signals for positioning and altitude. However, when in a special denial environment, satellite communication is interrupted, and the traditional positioning and height determination methods face challenges. We made a dataset at the height of 80–200 m and proposed a multi-scale input network. The positioning index RDS achieved 76.3 points, and the positioning accuracy within 20 m was 81.7%. This paper proposes a method to judge the height by image alone, without the support of other sensor data. One height judgment can be made per single image. Based on the UAV image–satellite image matching positioning technology, by calculating the actual area represented by the UAV image in real space, combined with the fixed parameters of the optical camera, the actual height of the UAV flight is calculated, which is 80–200 m, and the relative error rate of height is 18.1%.