Visual Localization Based on Remote Sensing Scene Matching with Siamese Feature Aggregation Network

This paper presents a new framework with a siamese feature aggregation network (SFANet) for visual localization based on remote sensing scene matching. Specifically, the presented framework predicts the location of a query image by finding the matching remote sensing images with geographical information. We employ the fully convolutional networks (FCNs) and a siamese network of NetVLAD to aggregate local features and learn the global representations for images from different sources. A new soft margin loss function is established for the network. Geographic coordinates of the query images are obtained by calculating the similarity with satellite images. We also collect a multi-scale dataset that contains 136959 images from 45653 locations. Various experiments are carried out on it. Experimental results show the effectivity of the proposed method.