遥感场景分类正日益依赖于在大规模地球观测数据上预训练的基础模型。此外，传递式推理（transductive inference）——即利用整个未标记查询集的整体统计结构——天然契合遥感处理流程，因为在该流程中，大尺寸遥感影像通常被切分为图像块并以批处理方式联合推理。本文提出 LC-TIM（Locally Consistent Transductive Information Maximization），该方法在当前最优的面向少样本 CLIP 的传递式信息最大化（TIM++）目标函数基础上，引入一个局部一致性正则项，强制每个查询样本与其在特征空间中的 $κ$ 个最近邻样本的预测结果保持一致。该正则项以单一乘性因子形式嵌入闭式 $q$-更新中，仅带来可忽略的计算开销。我们进一步提出一种多源扩展方案，融合多个遥感基础模型生成的亲和图（affinity graph），从而进一步提升分类精度。为评估所提方法，我们构建了首个面向传递式少样本遥感场景分类的全面、开源基准，涵盖十个多样化数据集、两种遥感视觉-语言模型以及多种少样本设定，对 LP++、TransCLIP、TIM++ 和 LC-TIM 进行系统评测。实验表明，传递式方法始终优于零样本基线；LC-TIM 达到当前最优分类精度，尤其在低样本量（low-shot）情形下增益最为显著——此时邻域线索最具判别性。代码已开源：https://github.com/elkhouryk/LC-TIM

Remote sensing scene classification is increasingly relying on foundation models pre-trained on large-scale Earth-observation data. Moreover, transductive inference, which exploits the collective statistical structure of the entire unlabeled query set, appears to naturally match remote sensing pipelines where large images are routinely split into patches and inferred as a batch. In this work, we introduce LC-TIM (Locally Consistent Transductive Information Maximization), which extends the state-of-the-art Transductive Information Maximization for Few-Shot CLIP (TIM++) objective with a local consistency regularizer that enforces prediction agreement between each query sample and its $κ$ nearest feature-space neighbors. The regularizer enters as a single multiplicative factor in the closed-form $q$-update, adding negligible computational overhead. We further propose a multi-source extension that fuses the affinity graph from multiple remote sensing foundation model, further boosting classification accuracy. To assess these methods, we establish the first comprehensive, open-source benchmark for transductive few-shot RS scene classification, evaluating LP++, TransCLIP, TIM++, and LC-TIM across ten diverse datasets, two remote sensing vision-language models, and across various few-shot settings. Our experiments show that transductive methods consistently outperform zero-shot baselines, and that LC-TIM achieves state-of-the-art accuracy, with the largest gains in the low-shot regime where neighborhood cues are most informative. Code is publicly available at: https://github.com/elkhouryk/LC-TIM