HyperFM: An Efficient Hyperspectral Foundation Model with Spectral Grouping

TL;DR

HyperFM is a new, efficient hyperspectral foundation model that uses spectral grouping and attention mechanisms to improve performance on atmospheric retrieval tasks, while reducing computational costs.

Contribution

It introduces HyperFM, a parameter-efficient hyperspectral foundation model with spectral grouping and hybrid decomposition, and releases a large-scale hyperspectral dataset from the PACE mission.

Findings

HyperFM outperforms existing hyperspectral models on four atmospheric cloud property tasks.

HyperFM achieves better spectral spatial relationship capture with fewer parameters.

The HyperFM250K dataset includes diverse scenes for further hyperspectral research.

Abstract

The NASA PACE mission provides unprecedented hyperspectral observations of ocean color, aerosols, and clouds, offering new insights into how these components interact and influence Earth's climate and air quality. Its Ocean Color Instrument measures light across hundreds of finely spaced wavelength bands, enabling detailed characterization of features such as phytoplankton composition, aerosol properties, and cloud microphysics. However, hyperspectral data of this scale is large, complex, and difficult to label, requiring specialized processing and analysis techniques. Existing foundation models, which have transformed computer vision and natural language processing, are generally trained on standard RGB imagery and therefore struggle to interpret the continuous spectral signatures captured by PACE. While recent advances have introduced hyperspectral foundation models, they are typically trained on cloud-free observations and often remain limited to single-sensor datasets due to spectral inconsistencies across instruments. Moreover, existing models tend to be parameter-heavy and computationally expensive, limiting scalability and adoption in operational settings. To address these challenges, we introduce HyperFM, a parameter-efficient hyperspectral foundation model that leverages intra-group and inter-group spectral attention along with hybrid parameter decomposition to better capture spectral spatial relationships while reducing computational cost. HyperFM demonstrates consistent performance improvements over existing hyperspectral foundation models and task-specific state-of-the-art methods across four benchmark downstream atmospheric cloud property retrieval tasks. To support further research, we additionally release HyperFM250K, a large-scale hyperspectral dataset from the PACE mission that includes both clear and cloudy scenes.