一个能适配任意光谱通道和空间尺度的高光谱图像融合框架。
Hyperspectral Image Fusion with Spectral-Band and Fusion-Scale Agnosticism
- 提出马特里什卡核，让模型可处理任意数量光谱通道。
- 基于隐式神经表示，实现任意分辨率重建。
- 单模型跨传感器、跨尺度泛化，适合未来高光谱基础模型研究。
当前多光谱与高光谱图像融合（MS/HS fusion）的深度学习模型通常针对固定光谱波段和空间尺度设计，限制了其在不同传感器间的迁移能力。为此，我们提出SSA框架，具备光谱波段与融合尺度的无感特性。具体而言，引入马特里什卡核（Matryoshka Kernel），使单一模型可自适应任意数量的光谱通道；同时，基于隐式神经表示（INR）骨干网络，将高光谱信号建模为连续函数，支持任意空间分辨率的重建。二者结合使得单一模型在未见过的传感器和空间尺度上均表现优异。大量实验表明，该模型在保持顶尖性能的同时展现出良好泛化能力，为未来高光谱基础模型的发展铺平道路。
原文摘要 · Abstract (English)
Current deep learning models for Multispectral and Hyperspectral Image Fusion (MS/HS fusion) are typically designed for fixed spectral bands and spatial scales, which limits their transferability across diverse sensors. To address this, we propose SSA, a universal framework for MS/HS fusion with spectral-band and fusion-scale agnosticism. Specifically, we introduce Matryoshka Kernel (MK), a novel operator that enables a single model to adapt to arbitrary numbers of spectral channels. Meanwhile, we build SSA upon an Implicit Neural Representation (INR) backbone that models the HS signal as a continuous function, enabling reconstruction at arbitrary spatial resolutions. Together, these two forms of agnosticism enable a single MS/HS fusion model that generalizes effectively to unseen sensors and spatial scales. Extensive experiments demonstrate that our single model achieves state-of-the-art performance while generalizing well to unseen sensors and scales, paving the way toward future HS foundation models.
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