F3-Net: Multiview Scene Matching for Drone-Based Geo-Localization
Bo Sun, Ganchao Liu, Yuan Yuan
Abstract
Scene matching involves establishing a mapping relationship between heterogeneous images, which is crucial for drone visual geo-localization. However, it poses a significant challenge for multi-view images such as those captured by drones and satellites. To address this issue, this paper proposes an end-to-end geo-localization framework named F3-Net for calculating the similarity of multi-source and multi-view images. The key contributions of F3-Net are as follows: 1) The Split and Fusion (SF) module is designed to fully exploit the features through the global self-attention mechanism. 2) To improve the multi-view semantic features, a Target Feature Enhancement (TFE) module is introduced, based on the principle of invariance target semantic consistency. 3) After multi-view feature learning, a Feature Alignment and Unity (FAU) module with Earth Mover distance is used to calculate the similarity of non-aligned features. F3-Net fully exploits the multi-source image feature correspondence and multi-view image semantic consistency. Different from the traditional siamese network, the features of multi-view images are regarded as probability distribution, so F3-Net can quantify and eliminate the feature differences of multi-view images in the learning process. Experiments show that F3-Net can effectively overcome multi-view changes and achieve high accuracy on University-1652 dataset.