酿造更强特征：面向多光谱对地观测的双教师蒸馏
Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation
将现代光学视觉基础模型的对比自蒸馏范式迁移到多光谱遥感预训练，通过双教师（多光谱教师+光学VFM教师）对齐全局语义结构，为跨模态表征学习提供了可复用的蒸馏框架。
原文依据 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
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基础模型正在变革对地观测（EO），然而EO传感器与模态的多样性使得单一通用模型并不现实。多个专用EO基础模型（EOFMs）很可能共存，因此跨模态的高效知识迁移至关重要。现有大多数EO预训练依赖掩码图像建模，该方法强调局部重建，但对全局语义结构的控制有限。为解决这一问题，我们提出一种面向多光谱图像的双教师对比蒸馏框架，使学生模型的预训练目标与现代光学视觉基础模型（VFMs）的对比自蒸馏范式对齐。我们的方法结合一个多光谱教师和一个光学VFM教师，实现连贯的跨模态表征学习。在多种光学与多光谱基准上的实验表明，我们的模型能够适应多光谱数据，同时不损害仅光学输入上的性能，在两种设定下均取得最优结果，语义分割平均提升3.64个百分点，变化检测提升1.2，分类提升1.31。这表明对比蒸馏为异构EO数据源的可扩展表征学习提供了一种有原则且高效的方法。
英文原文摘要 1348 字符
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 average improvements of 3.64 percentage points in semantic segmentation, 1.2 in change detection, and 1.31 in classification. This demonstrates that contrastive distillation provides a principled and efficient approach to scalable representation learning across heterogeneous EO data sources.