标题： GeoCrossBench：遥感中的跨波段泛化 显示英文标题

标题： GeoCrossBench: Cross-Band Generalization for Remote Sensing

摘要： 随着遥感卫星的数量和多样性随时间增长，而大多数标记数据来自较旧的卫星。 当地球观测的基础模型规模扩大时，(再)训练以支持新卫星的成本也相应增加，因此模型对新卫星的泛化能力变得越来越重要。 在本工作中，我们引入了GeoCrossBench，这是流行的GeoBench基准的一个扩展，具有新的评估协议：它测试分布内性能；对没有波段重叠的卫星的泛化能力；以及相对于训练集具有额外波段的卫星的泛化能力。 我们还开发了ChannelViT的一个自监督扩展版本ChiViT，以提高其跨卫星性能。 首先，我们表明，即使是最先进的遥感基础模型（DOFA、TerraFM），在分布内设置中也没有超越通用模型如DINOv3的表现。 其次，当泛化到没有波段重叠的新卫星时，所有模型的性能都会下降2-4倍，而ChiViT显著优于第二名DINOv3。 第三，当在测试时提供额外波段时，所有测试模型的性能平均下降5-25%。 最后，我们展示了仅使用所有波段的oracle标签对这些模型的最后一个线性层进行微调，可以在所有卫星上获得相对一致的性能，这表明该基准远未达到饱和。 我们公开发布代码和数据集，以鼓励开发具有更强跨卫星泛化能力的更未来可靠的遥感模型。 显示英文摘要

摘要： The number and diversity of remote sensing satellites grows over time, while the vast majority of labeled data comes from older satellites. As the foundation models for Earth observation scale up, the cost of (re-)training to support new satellites grows too, so the generalization capabilities of the models towards new satellites become increasingly important. In this work we introduce GeoCrossBench, an extension of the popular GeoBench benchmark with a new evaluation protocol: it tests the in-distribution performance; generalization to satellites with no band overlap; and generalization to satellites with additional bands with respect to the training set. We also develop a self-supervised extension of ChannelViT, ChiViT, to improve its cross-satellite performance. First, we show that even the best foundation models for remote sensing (DOFA, TerraFM) do not outperform general purpose models like DINOv3 in the in-distribution setting. Second, when generalizing to new satellites with no band overlap, all models suffer 2-4x drop in performance, and ChiViT significantly outperforms the runner-up DINOv3. Third, the performance of all tested models drops on average by 5-25\% when given additional bands during test time. Finally, we show that fine-tuning just the last linear layer of these models using oracle labels from all bands can get relatively consistent performance across all satellites, highlighting that the benchmark is far from being saturated. We publicly release the code and the datasets to encourage the development of more future-proof remote sensing models with stronger cross-satellite generalization.