无人机依赖全球导航卫星系统（GNSS）进行导航定位容易受到信号阻挡或干扰造成失效。视觉位置识别（VPR）通过将无人机捕获的视觉信息、预先构建的地图数据进行匹配实现地理定位，能够在GNSS信号拒止环境下提供可靠的定位信息，因此成为近年来的研究热点。传统VPR方法依赖预训练网络提取用于匹配、检索的全局特征，通常对视角、尺度、光照等视觉外观变化敏感，并且容易丢失细粒度信息。为此，提出了一种基于双分支特征聚合网络的无人机视觉地理位置识别方法，结合了预训练的视觉Transformer模型、状态空间模型以提取更加鲁棒的特征。具体来说，设计了一个集成了DINOv2、VMamba模型的双分支特征提取网络，通过结合ViT的全局语义理解、视觉状态空间模型的局部动态建模能力，实现更强的泛化、细节感知能力。此外，引入了一个受MLP-Mixer架构启发的高效特征融合框架，以增强多通道特征表示的性能。在同视角的ALTO数据集、跨视角的 VIGOR 数据集上进行的实验表明，所提出的方法在诸如返回前1、5个结果中的召回率指标上具有较高的准确性，且优于现有方法，无论是在同一视角还是跨视角的场景中，都能够更有效地识别出匹配图像。

UAVs’ reliance on Global Navigation Satellite Systems （GNSS） for navigation and positioning is prone to failure due to signal blockage or interference. Visual Place Recognition （VPR） enables geographic localization by matching the visual information captured by UAVs with pre-built map data， providing reliable positioning information in GNSS-denied environments， and thus become a research hotspot in recent years. Traditional VPR methods typically depend on pre-trained networks to extract global features for matching and retrieval， but they are sensitive to changes in visual appearance such as viewpoint， scale， and lighting， and are prone to losing fine-grained information. To address these issues， this paper proposes a UAV visual geo-localization method based on a dual-branch feature aggregation network that combines a pre-trained Vision Transformer model and a state-space model to extract more robust features. Specifically， a dual-branch feature extraction network integrating the DINOv2 and VMamba models is designed， which leverages the global semantic understanding of ViT and the local dynamic modeling capability of the visual state-space model to achieve stronger generalization and fine-grained perception. Additionally， the method introduces an efficient feature fusion framework inspired by the MLP-Mixer architecture to enhance the performance of multi-channel feature representation. Experiments conducted on the same-view ALTO dataset and the cross-view VIGOR dataset demonstrate that the proposed method achieves high accuracy in metrics such as R@1 and R@5， outperforming existing methods. This method is proved effective in identifying matching images in different scenarios.