Cross-view scene matching refers to the discovery of images of the same geographical target from different platforms （such as drones and satellites）. However， different image platforms lead to low accuracy of UAV （Unmanned Aerial Vehicle） positioning and navigation tasks， and the existing methods usually focus only on a single dimension of the image and ignore the multi-dimensional features of the image. To solve the above problems， GAMF （Global Attention and Multi-granularity feature Fusion） deep neural network was proposed to improve feature representation and feature distinguishability. Firstly， the images from the UAV perspective and the satellite perspective were combined， and the three branches were extended under the unified network architecture， the spatial location， channel and local features of the images from three dimensions were extracted. Then， by establishing the SGAM （Spatial Global relationship Attention Module） and CGAM （Channel Global Attention Module）， the spatial global relationship mechanism and channel attention mechanism were introduced to capture global information， so as to better carry out attention learning. Secondly， in order to fuse local perception features， a local division strategy was introduced to better improve the model’s ability to extract fine-grained features. Finally， the features of the three dimensions were combined as the final features to train the model. The test results on the public dataset University-1652 show that the AP （Average Precision） of the GAMF model on UAV visual positioning tasks reaches 87.41%， and the Recall （R@1） in UAV visual navigation tasks reaches 90.30%， which verifies that the GAMF model can effectively aggregate the multi-dimensional features of the image and improve the accuracy of UAV positioning and navigation tasks.