Multispectral to Hyperspectral using Pretrained Foundational model

Hyperspectral imaging provides detailed spectral information, offering significant potential for monitoring greenhouse gases like CH4 and NO2. However, its application is constrained by limited spatial coverage and infrequent revisit times. In contrast, multispectral imaging delivers broader spatial and temporal coverage but lacks the spectral granularity required for precise GHG detection. To address these challenges, this study proposes Spectral and Spatial-Spectral transformer models that reconstruct hyperspectral data from multispectral inputs. The models in this paper are pretrained on EnMAP and EMIT datasets and fine-tuned on spatio-temporally aligned (Sentinel-2, EnMAP) and (HLS-S30, EMIT) image pairs respectively. Our model has the potential to enhance atmospheric monitoring by combining the strengths of hyperspectral and multispectral imaging systems.

@article{gonzalez2025_2502.19451,
  title={ Multispectral to Hyperspectral using Pretrained Foundational model },
  author={ Ruben Gonzalez and Conrad M Albrecht and Nassim Ait Ali Braham and Devyani Lambhate and Joao Lucas de Sousa Almeida and Paolo Fraccaro and Benedikt Blumenstiel and Thomas Brunschwiler and Ranjini Bangalore },
  journal={arXiv preprint arXiv:2502.19451},
  year={ 2025 }
}