SAFL-Geo: Structure-Aware Feature Learning with Fusion Loss for Infrared-Visible Geo-Localization

Abstract

Cross-modal Visual Geo-localization often aims to retrieve a satellite visible-light image of the same geographic lo cation from a large-scale database using an infrared image cap tured by an unmanned aerial vehicle (UAV), thereby achieving precise localization. This capability is crucial for autonomous drone localization and navigation in low-light conditions such as nighttime or smoky environments. However, research in this field is still in its nascent stage, with existing methods being few in number and limited in precision. To address these issues, this paper proposes a structure-aware and fusion-loss constrained cross-modal geo-localization network (SAFL-Geo), which enhances the accuracy of cross-modal image retrieval. Specifically, we design a structure-aware module embedded into the network backbone, substantially enhancing the model’s abil ity to perceive and extract cross-modally consistent structural features (such as road and building contours). Furthermore, we propose a feature enhancement and aggregation module that projects the refined multi-modal representations into a unified embedding space, effectively reducing the cross-modal representation gap while preserving discriminative semantic structures. Finally, we propose a fusion loss constraint strategy that constructs intermediate fused features as a “bridge” to constrain the distribution distances between infrared and fused features, as well as between visible and fused features, thereby indirectly mitigating the modality gap. Extensive experiments on the Boson datasets show that our SAFL-Geo achieves superior state-of-the-art performance.