HyperSL: A Spectral Foundation Model for Hyperspectral Image Interpretation
Weili Kong, Baisen Liu, Xiaojun Bi, Changdong Yu, Xinyao Li, Yushi Chen · IEEE Transactions on Geoscience and Remote Sensing · 2025
This work has delivered a novelty foundational model for hyperspectral remote sensing images. Current approaches for hyperspectral data interpretation often require specialized models that are specifically tailored to individual datasets or tasks. In some specific tasks, the availability of hyperspectral data is often limited, posing significant challenges to training due to data insufficiency. Furthermore, the diverse structure of hyperspectral data often complicates the transfer of knowledge from other available datasets, severely limiting cross-scenario capabilities. To bridge this gap, we introduce a highly adaptable foundational model with strong transferability, capable of processing all forms of hyperspectral data across diverse spectral bands and ranges. Compared to previous methods, our approach: 1) standardizes all types spectral vectors into a common token format, enabling a single model for multi-source hyperspectral data; 2) aligns spectral features across different ranges by embedding wavelength information into position encoding; 3) has been pre-trained on over 300 million spectral instances worldwide, ensuring broad generalization; 4) transfers learned knowledge effortlessly to downstream tasks and new datasets without architectural modifications or training from scratch. Experimental results demonstrate that, compared to other mainstream methods, our approach achieves state-of-the-art classification performance across various datasets with different spectral characteristics in both supervised and unsupervised learning settings, while also delivering impressive results in change detection tasks. The source code and the pretrained weights are available at https://github.com/kkweil/HyperSL.