Abstract
Foundation models are transforming Earth Observation (EO), yet the diversity of EO sensors and modalities makes a single universal model unrealistic. Multiple specialized EO foundation models (EOFMs) will likely coexist, making efficient knowledge transfer across modalities essential. Most existing EO pretraining relies on masked image modeling, which emphasizes local reconstruction but provides limited control over global semantic structure. To address this, we propose a dual-teacher contrastive distillation framework for multispectral imagery that aligns the student's pretraining objective with the contrastive self-distillation paradigm of modern optical vision foundation models (VFMs). Our approach combines a multispectral teacher with an optical VFM teacher, enabling coherent cross-modal representation learning. Experiments across diverse optical and multispectral benchmarks show that our model adapts to multispectral data without compromising performance on optical-only inputs, achieving state-of-the-art results in both settings, with an average improvement of 3.64 percentage points in semantic segmentation, 1.2 in change detection, and 1.31 in classification tasks. This demonstrates that contrastive distillation provides a principled and efficient approach to scalable representation learning across heterogeneous EO data sources. Code: Coming soon.
Abstract (translated)
基础模型正在改变地球观测（EO），然而，各种不同的EO传感器和模式使得创建单一通用模型不切实际。多个专业的EO基础模型(EOFMs)可能会共存，因此在不同模态之间进行有效的知识转移至关重要。现有的大多数EO预训练依赖于掩码图像建模，这强调了局部重建但对全局语义结构的控制有限。为了解决这个问题，我们提出了一种双教师对比蒸馏框架，用于多光谱影像，使学生模型的预训练目标与现代光学视觉基础模型(VFMs)中的对比自蒸馏范式相一致。我们的方法结合了多光谱和光学VFM两个老师，从而能够进行连贯的跨模态表示学习。在各种各样的光学和多光谱基准测试上进行的实验表明，我们的模型在适应多光谱数据的同时，并不会牺牲对仅光学输入的表现，在语义分割、变化检测以及分类任务中分别取得了平均3.64个百分点、1.2个百分点和1.31个百分点的进步。这证明了对比蒸馏为跨异构EO数据源的可扩展表示学习提供了一个原理上正确且有效的方法。 代码：即将发布。
URL
https://arxiv.org/abs/2602.19863
