酿造更强的特征：多光谱地球观测的双教师蒸馏
Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation
AI总结：
本文提出双教师对比蒸馏框架，用于多光谱地球观测，通过结合多光谱和光学教师，实现跨模态表示学习，提升多光谱和光学数据的性能。
摘要：
基础模型正在改变地球观测（EO），但EO传感器和模态的多样性使得单一通用模型不现实。多个专门的EO基础模型（EOFMs）可能会共存，这使得在模态之间高效的知识转移至关重要。大多数现有的EO预训练依赖于掩码图像建模，这强调局部重建但对全局语义结构控制有限。为了解决这个问题，我们提出了一种双教师对比蒸馏框架，用于多光谱图像，使学生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。我们的方法结合了多光谱教师和光学VFM教师，实现了连贯的跨模态表示学习。在多样化的光学和多光谱基准测试中，实验表明，我们的模型能够适应多光谱数据，而不会影响对仅光学输入的性能，实现了在两种设置中的最佳结果，平均在语义分割任务中提高了3.64个百分点，在变化检测任务中提高了1.2个百分点，在分类任务中提高了1.31个百分点。这表明，对比蒸馏为在异构EO数据源之间进行可扩展的表示学习提供了一种系统且高效的途径。项目页面：https://wolfilip.github.io/DEO/。
英文摘要：
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. Project page: \textcolor{magenta}{https://wolfilip.github.io/DEO/}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
