Geospatial Foundation Models (GFMs) typically lack native support for Hyperspectral Imaging (HSI) due to the complexity and sheer size of high-dimensional spectral data. This study investigates the adaptability of TerraMind, a multimodal GFM, to address HSI downstream tasks \emph{without} HSI-specific pretraining. Therefore, we implement and compare two channel adaptation strategies: Naive Band Selection and physics-aware Spectral Response Function (SRF) grouping. Overall, our results indicate a general superiority of deep learning models with native support of HSI data. Our experiments also demonstrate the ability of TerraMind to adapt to HSI downstream tasks through band selection with moderate performance decline. Therefore, the findings of this research establish a critical baseline for HSI integration, motivating the need for native spectral tokenization in future multimodal model architectures.
@article{arxiv.2603.06690,
title = {Spectral Gaps and Spatial Priors: Studying Hyperspectral Downstream Adaptation Using TerraMind},
author = {Julia Anna Leonardi and Johannes Jakubik and Paolo Fraccaro and Maria Antonia Brovelli},
journal= {arXiv preprint arXiv:2603.06690},
year = {2026}
}
Comments
Accepted to ICLR 2026 Machine Learning for Remote Sensing (ML4RS) Workshop