A Multi-Source Image Matching Network for UAV Visual Location

Visual localization is an important but challenging task for unmanned aerial vehicles (UAV). Matching real-time UAV orthophotos to pre-existing georeferenced satellite images is the key problem for this task. However, UAV and satellite images are inconsistent in image styles, perspectives, and times. In this paper, a new fully convolutional siamese network is proposed to extract similar features for multi-source images. The Squeeze-and-Excitation structure is integrated into the densely connected network to adapt to multi-scale features and the texture differences of different regions. Besides, a loss function with a progressive sampling strategy is utilized to mine the similarity of matching multi-source images and improve the description compactness among dimensions. Extensive experimental results with in-depth analysis are provided, which indicate that the proposed framework can significantly improve the matching performance of the learned descriptor.