用遥感训练的高光谱模型直接迁移到近距感知，效果更好且不丢光谱信息。
Cross-Domain Transfer of Hyperspectral Foundation Models
- 直接复用遥感训练的高光谱基础模型，跳过跨模态转换
- 在小样本下性能优于传统域内训练，接近跨模态方法
- 适合数据少、需保留光谱特征的近距感知任务
高光谱图像语义分割通常依赖域内训练，但真实场景中数据有限，制约模型表现。现有方法通过跨模态技术连接RGB与高光谱图像，利用视觉基础模型，但或丢失光谱信息，或增加架构复杂度。本文提出跨域迁移：直接复用遥感领域预训练的高光谱基础模型，应用于近距感知任务。该方法无需跨模态桥接，既保留光谱信息，又保持结构简单。基于HS3-Bench基准，系统评估并比较了域内训练、同模态训练、跨模态迁移和跨域迁移策略。结果表明，跨域迁移显著优于域内与同模态训练，在数据稀缺条件下仍保持强性能，缩小了与跨模态方法的差距。本工作推动了高光谱语义分割在多样化场景中的有效应用。
原文摘要 · Abstract (English)
Hyperspectral imaging (HSI) semantic segmentation typically relies on in-domain training, but limited data availability often restricts model performance in real-world applications. Current approaches to leverage foundation models in proximal sensing use cross-modality techniques, bridging RGB and HSI to exploit vision foundation models. However, these methods either discard spectral information or introduce architectural complexity. We propose cross-domain transfer as an alternative, reusing HSI foundation models - originally trained in remote sensing - for proximal sensing applications. By eliminating the need to bridge modality gaps, our approach preserves spectral information while maintaining a simple architecture. Using the HS3-Bench benchmark, we systematically evaluate and compare conventional in-domain, in-modality training, cross-modality transfer and cross-domain transfer strategies. Our results demonstrate that cross-domain transfer achieves large performance improvements over in-domain, in-modality training, reduces the performance gap to cross-modality approaches and maintains strong performance in limited data settings. Thus, this work advances more effective HSI semantic segmentation in diverse applications.
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