HADGEO: Image Based 3-DoF Cross-View Geo-Localization with Hard Sample Mining

Image based 3 Degrees-of-Freedom (DoF) cross-view geo-localization aims to estimate the position and orientation of a camera on the ground by matching the captured ground image with geo-tagged aerial images. However, most existing methods do not sufficiently exploit the difference between positive and negative samples for feature extraction, resulting in low localization accuracy. In this paper, we propose a novel method called HADGEO for accurate 3-DoF cross-view geo-localization. Specifically, we design a double-siamese structure with All Learnable Fully Convolutional Networks (ALFCN) to separately extract features from the aerial and ground images. To tap full potential of our network, we define a new weighted soft-margin triplet loss by integrating the Hard Sample Mining (HSM) strategy. This loss increases the training difficulty, forcing the network to be more discriminative for orientation-aware features. A series of experiments demonstrate that our method outperforms existing methods and achieves state-of-the-art performance on orientation unknown and Field-of-View (FoV) limited conditions, further improving the accuracy of 3-DoF geo-localization.