While the application of UAV images in target detection and tracking has been widely studied amid the popularization of UAV aerial photography technology, there has been relatively few researches on the matching between UAV images and satellite images. Due to their different imaging mechanisms and viewpoints, there are large-scale differences between UAV images and satellite images. Effective matching between UAV images and satellite images are difficult to realize using existing image matching methods. In order to solve this problem, this paper proposes a method for matching UAV images with large-scale differences from satellite images. This method registers the direction and scale of UAV images using the longitude and latitude coordinate of satellite images and the camera pose information of UAV images. Using the position of the UAV images, the satellite images are then roughly matched to obtain the satellite sub-images including the UAV images matching area. The CNN features of the registered UAV images and satellite sub-images are then extracted using neural networks, and the precise matching between UAV images and satellite images is realized based on CNN features. Simulation results show that the proposed method can effectively match large-scale UAV images with satellite images. The comparison in terms of matching accuracy between this matching method and existing image matching algorithms show the effectiveness and superiority of this matching algorithm.