Traditional supervised drone-view geo-localization (DVGL) methods heavily depend on paired training data and encounter difficulties in learning cross-view correlations from unpaired data. Moreover, when deployed in a new domain, these methods require obtaining the new paired data and subsequent retraining for model adaptation, which significantly increases computational overhead. Existing unsupervised methods have enabled to generate pseudo-labels based on cross-view similarity to infer the pairing relationships. However, geographical similarity and spatial continuity often cause visually analogous features at different geographical locations. The feature confusion compromises the reliability of pseudo-label generation, where incorrect pseudo-labels drive negative optimization. Given these challenges inherent in both supervised and unsupervised DVGL methods, we propose a novel cross-domain invariant knowledge transfer network (CDIKTNet) with limited supervision, whose architecture consists of a cross-domain invariance sub-network (CDIS) and a cross-domain transfer sub-network (CDTS). This architecture facilitates a closed-loop framework for invariance feature learning and knowledge transfer. The CDIS is designed to learn cross-view structural and spatial invariance from a small amount of paired data that serves as prior knowledge. It endows the shared feature space of unpaired data with similar implicit cross-view correlations at initialization, which alleviates feature confusion. Based on this, the CDTS employs dual-path contrastive learning to further optimize each subspace while preserving consistency in a shared feature space. Extensive experiments demonstrate that CDIKTNet achieves state-of-the-art performance under full supervision compared with those supervised methods, and further surpasses existing unsupervised methods in both few-shot and cross-domain initialization.

翻译：传统的监督式无人机视角地理定位方法严重依赖配对训练数据，且难以从非配对数据中学习跨视角关联。此外，当部署至新领域时，这些方法需要获取新的配对数据并进行模型重训练以适应新领域，这显著增加了计算开销。现有的无监督方法能够基于跨视角相似性生成伪标签以推断配对关系。然而，地理相似性与空间连续性常导致不同地理位置呈现视觉相似的特征。这种特征混淆会损害伪标签生成的可靠性，其中错误的伪标签将引发负向优化。鉴于监督式与无监督式DVGL方法均存在上述固有挑战，我们提出一种新型有限监督跨域不变知识迁移网络，其架构由跨域不变性子网络与跨域迁移子网络构成。该架构构建了一个用于不变性特征学习与知识迁移的闭环框架。CDIS旨在从少量作为先验知识的配对数据中学习跨视角结构不变性与空间不变性。它在初始化阶段为未配对数据的共享特征空间赋予相似的隐式跨视角关联，从而缓解特征混淆问题。在此基础上，CDTS采用双路径对比学习在保持共享特征空间一致性的同时进一步优化各子空间。大量实验表明，CDIKTNet在全监督条件下相比现有监督方法达到最优性能，并在少样本与跨域初始化场景中进一步超越现有无监督方法。