PLGeo: A Patch-level Framework to Overcome Orientation Discrepancies in Cross-view Geo-localization

摘要

Cross-view geo-localization(CVGL) aims to determine the location of a ground-view image by referencing geo-tagged satellite-view images. Existing methods assume known ground-view image orientation-an unrealistic constraint in real-world scenarios where cameras have arbitrary orientations and limited fields of view (FOV).Unknown orientation cross-view geo-localization (UOCVGL) better reflects real-world applications but introduces severe feature alignment challenges due to misalignments in both viewpoints and orientations, significantly degrading localization accuracy. To address this challenge, we propose PLGeo, a novel method for UOCVGL, which includes two key components: (1) a Patch-wise Similarity Enhancement Component, which computes patch-level similarities between corresponding patches and refines alignments using learned attention weights, improving accuracy and mitigating issues caused by varying orientations and reduced FOV in ground-view images; and (2) an Attention-guided Patch Matching Component, which refines intra-domain feature matching within the same view by emphasizing stronger correspondences and suppressing weaker ones. We comprehensively evaluate PLGeo on several benchmark datasets under different settings, including unknown orientation, limited FOVs, robust datasets, the UAV dataset, north-aligned setting, and few-shot scenarios. Experimental results demonstrate that PLGeo consistently outperforms state-of-the-art methods, exhibiting remarkable robustness and generalization ability even in challenging real-world conditions. Code is available at https://github.com/1203ll/PLGeo.