Cross-view geo-localization (CVGL) between drone and satellite imagery remains challenging due to severe viewpoint gaps and the presence of hard negatives, which are visually similar but geographically mismatched samples. Existing mining or reweighting strategies often use static weighting, which is sensitive to distribution shifts and prone to overemphasizing difficult samples too early, leading to noisy gradients and unstable convergence. In this paper, we present a Dual-level Progressive Hardness-aware Reweighting (DPHR) strategy. At the sample level, a Ratio-based Difficulty-Aware (RDA) module evaluates relative difficulty and assigns fine-grained weights to negatives. At the batch level, a Progressive Adaptive Loss Weighting (PALW) mechanism exploits a training-progress signal to attenuate noisy gradients during early optimization and progressively enhance hard-negative mining as training matures. Experiments on the University-1652 and SUES-200 benchmarks demonstrate the effectiveness and robustness of the proposed DPHR, achieving consistent improvements over state-of-the-art methods.

翻译：无人机与卫星图像之间的跨视角地理定位（CVGL）仍然面临严峻挑战，这主要源于显著的视角差异以及困难负样本的存在——这些样本视觉上相似但地理位置不匹配。现有的挖掘或重加权策略通常采用静态加权方式，对分布偏移敏感，且容易过早过度强调困难样本，导致梯度噪声大和收敛不稳定。本文提出一种双层级渐进式难度感知重加权（DPHR）策略。在样本层级，基于比率的难度感知（RDA）模块评估相对难度并为负样本分配细粒度权重。在批次层级，渐进式自适应损失加权（PALW）机制利用训练进度信号，在优化早期抑制噪声梯度，并随着训练成熟逐步增强困难负样本的挖掘。在University-1652和SUES-200基准数据集上的实验验证了所提DPHR方法的有效性和鲁棒性，相比现有先进方法取得了持续的性能提升。