Abstract: Cross-view geo-localization between UAV and satellite images has emerged as a promising alternative to GNSS-INS,particularly in environments where satellite signals are weak or obstructed.However,significant visual discrepancies caused by differences in viewpoint,illumination,and resolution pose considerable challenges for image matching.To address this issue,a novel method called P2VSSM(Pyramid Pooling Visual State Space Model) is proposed.By integrating a pyramid pooling self-attention mechanism into the Mamba architecture,the model enhances feature extraction capabilities for cross-view images.The proposed PPSA module aggregates multi-scale contextual information,improving both semantic abstraction and global modeling.Additionally,the InfoNCE loss is introduced to replace the traditional triplet loss,thereby avoiding the heavy burden of hard negative mining and significantly improving the diversity of negative samples and the stability of contrastive learning during training.Experimental results on two public UAV-satellite datasets,University-1652 and SUES-200,demonstrate that the proposed method achieves state-of-the-art performance in both UAV-to-satellite and satellite-to-UAV retrieval tasks.Extensive ablation studies further confirm the effectiveness and robustness of the proposed approach.

Key words: Cross-view geo-localization, UAV-satellite matching, Mamba, Pyramid pooling, Representation learning