Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2603.06690 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912979886276608 |
|---|---|
| author | Leonardi, Julia Anna Jakubik, Johannes Fraccaro, Paolo Brovelli, Maria Antonia |
| author_facet | Leonardi, Julia Anna Jakubik, Johannes Fraccaro, Paolo Brovelli, Maria Antonia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06690 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spectral Gaps and Spatial Priors: Studying Hyperspectral Downstream Adaptation Using TerraMind Leonardi, Julia Anna Jakubik, Johannes Fraccaro, Paolo Brovelli, Maria Antonia Computer Vision and Pattern Recognition 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. |
| title | Spectral Gaps and Spatial Priors: Studying Hyperspectral Downstream Adaptation Using TerraMind |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.06690 |