Abstract
The primary objective of cross-view UAV geolocalization is to identify the exact spatial coordinates of drone-captured imagery by aligning it with extensive, geo-referenced satellite databases. Current approaches typically extract features independently from each perspective and rely on basic heuristics to compute similarity, thereby failing to explicitly capture the essential interactions between different views. To address this limitation, we introduce a novel, plug-and-play ranking architecture designed to explicitly perform joint relational modeling for improved UAV-to-satellite image matching. By harnessing the capabilities of a Large Vision-Language Model (LVLM), our framework effectively learns the deep visual-semantic correlations linking UAV and satellite imagery. Furthermore, we present a novel relational-aware loss function to optimize the training phase. By employing soft labels, this loss provides fine-grained supervision that avoids overly penalizing near-positive matches, ultimately boosting both the model's discriminative power and training stability. Comprehensive evaluations across various baseline architectures and standard benchmarks reveal that the proposed method substantially boosts the retrieval accuracy of existing models, yielding superior performance even under highly demanding conditions.
Abstract (translated)
跨视角无人机地理定位的主要目标是通过将其与广泛的、地理参考的卫星数据库对齐，来识别由无人机捕获影像的确切空间坐标。当前的方法通常是从每个视角独立提取特征，并依赖于基本启发式方法计算相似度，从而未能明确捕捉不同视图之间的关键交互作用。为了解决这一限制，我们引入了一种新颖的即插即用排名架构，该架构旨在进行联合关系建模，以改善无人机与卫星图像匹配的效果。通过利用大型视觉-语言模型（LVLM）的能力，我们的框架能够有效学习连接无人机和卫星影像之间的深度视觉语义关联。 此外，我们提出了一种新型的关系感知损失函数，用于优化训练阶段。通过使用软标签，这种损失可以提供细粒度的监督，并且避免过度惩罚接近正匹配的情况，从而最终增强模型的判别能力和训练稳定性。在各种基线架构和标准基准测试中的全面评估表明，所提出的方法显著提高了现有模型的检索准确性，在高度苛刻条件下也能表现出色。
URL
https://arxiv.org/abs/2603.08063
