Without Paired Labeled Data: An End-to-End Self-Supervised Paradigm for UAV-View Geo-Localization

• URL: http://arxiv.org/abs/2502.11381v2
• Date: Tue, 01 Apr 2025 03:44:00 GMT
• Title: Without Paired Labeled Data: An End-to-End Self-Supervised Paradigm for UAV-View Geo-Localization
• Authors: Zhongwei Chen, Zhao-Xu Yang, Hai-Jun Rong,
• Abstract summary: UAV-View Geo-Localization (UVGL) aims to achieve accurate localization of unmanned aerial vehicles (UAVs) by retrieving the most relevant GPS-tagged satellite images.<n>Existing methods heavily rely on pre-paired UAV-satellite images for supervised learning.<n>We propose an end-to-end self-supervised UVGL method to overcome these limitations.
• Score: 2.733505168507872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: UAV-View Geo-Localization (UVGL) aims to achieve accurate localization of unmanned aerial vehicles (UAVs) by retrieving the most relevant GPS-tagged satellite images. However, existing methods heavily rely on pre-paired UAV-satellite images for supervised learning. Such dependency not only incurs high annotation costs but also severely limits scalability and practical deployment in open-world UVGL scenarios. To address these limitations, we propose an end-to-end self-supervised UVGL method. Our method leverages a shallow backbone network to extract initial features, employs clustering to generate pseudo labels, and adopts a dual-path contrastive learning architecture to learn discriminative intra-view representations. Furthermore, our method incorporates two core modules, the dynamic hierarchical memory learning module and the information consistency evolution learning module. The dynamic hierarchical memory learning module combines short-term and long-term memory to enhance intra-view feature consistency and discriminability. Meanwhile, the information consistency evolution learning module leverages a neighborhood-driven dynamic constraint mechanism to systematically capture implicit cross-view semantic correlations, thereby improving cross-view feature alignment. To further stabilize and strengthen the self-supervised training process, a pseudo-label enhancement strategy is introduced, which refines the quality of pseudo supervision. Our method ultimately constructs a unified cross-view feature representation space under self-supervised settings. Extensive experiments on three public benchmark datasets demonstrate that the proposed method consistently outperforms existing self-supervised methods and even surpasses several state-of-the-art supervised methods. Our code is available at https://github.com/ISChenawei/DMNIL.

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