Object-aware graph matching network for cross-domain remote sensing image localization

Cross-domain and cross-modal remote sensing image geo-localization remains challenging due to large appearance discrepancies and unstable semantic correspondence across heterogeneous sensors and platforms. Existing methods mainly rely on scene-level or global representations, which often struggle to achieve reliable alignment in complex environments, especially under severe modality gaps such as infrared-to-visible matching. To facilitate research in this setting, this study introduces IRVL328, a new infrared-visible remote sensing localization dataset designed to reflect challenging cross-modal variations. Meanwhile, this study proposes an object-aware graph matching framework that integrates object detection with dual-graph neural reasoning, where salient structural regions are represented as graph nodes and both inter-image correspondences and intra-image relations are jointly modeled; a training-only node alignment strategy is further introduced to enhance supervision without increasing inference complexity. Experimental results show that the proposed method achieves competitive performance on SUES-200 and strong performance on IRVL328 and DenseUAV, particularly in challenging cross-modal settings.