Multispectral to Hyperspectral using Pretrained Foundational model

Multispectral to Hyperspectral using Pretrained Foundational model Ruben Gonzalez* 1, Conrad M Albrecht 1, Nassim Ait Ali Braham 1, Devyani Lambhate* 2, Joao Lucas de Sousa Almeida 2, Paolo Fraccaro 2, Benedikt Blumenstiel 2, Thomas Brunschwiler 2, and Ranjini Bangalore 2 1 Remote Sensing Technology Institute, German Aerospace Center (DLR), Germany 2 IBM Research Labs, India, U.K., Zurich, Brazil February 28, 2025 Abstract Hyperspectral imaging provides detailed spectral information, offering significant potential for monitoring greenhouse gases like CH 4 and NO 2. 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 reconstructs 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. 1 Introduction Satellite images are being used to create detailed maps of Earth's surface.