Foundation models (FMs) are revolutionizing the analysis and understanding of
remote sensing (RS) scenes, including aerial RGB, multispectral, and SAR
images. However, hyperspectral images (HSIs), which are rich in spectral
information, have not seen much application of FMs, with existing methods often
restricted to specific tasks and lacking generality. To fill this gap, we
introduce HyperSIGMA, a vision transformer-based foundation model for HSI
interpretation, scalable to over a billion parameters. To tackle the spectral
and spatial redundancy challenges in HSIs, we introduce a novel sparse sampling
attention (SSA) mechanism, which effectively promotes the learning of diverse
contextual features and serves as the basic block of HyperSIGMA. HyperSIGMA
integrates spatial and spectral features using a specially designed spectral
enhancement module. In addition, we construct a large-scale hyperspectral
dataset, HyperGlobal-450K, for pre-training, which contains about 450K
hyperspectral images, significantly surpassing existing datasets in scale.
Extensive experiments on various high-level and low-level HSI tasks demonstrate
HyperSIGMA's versatility and superior representational capability compared to
current state-of-the-art methods. Moreover, HyperSIGMA shows significant
advantages in scalability, robustness, cross-modal transferring capability, and
real-world applicability.

对于富含光谱信息的高光谱图像（HSI），我们引入了基于 Vision Transformer 的基础模型 HyperSIGMA，通过一种新颖的稀疏采样注意力机制 (SSA) 有效地解决了光谱和空间冗余的挑战，并使用特别设计的光谱增强模块，将空间和光谱特征进行整合。 HyperSIGMA 在各种高级和低级 HSI 任务上的广泛实验证明了其多功能性和优越的代表能力，同时显示出在可扩展性、健壮性、跨模态传输能力和实际应用性方面的显著优势。