Multispectral to Hyperspectral using Pretrained Foundational model

• URL: http://arxiv.org/abs/2502.19451v1
• Date: Wed, 26 Feb 2025 06:18:40 GMT
• Title: Multispectral to Hyperspectral using Pretrained Foundational model
• Authors: Ruben Gonzalez, Conrad M Albrecht, Nassim Ait Ali Braham, Devyani Lambhate, Joao Lucas de Sousa Almeida, Paolo Fraccaro, Benedikt Blumenstiel, Thomas Brunschwiler, Ranjini Bangalore,
• Abstract summary: Hyperspectral imaging provides detailed spectral information, offering significant potential for monitoring greenhouse gases like CH4 and NO2.<n>Its application is constrained by limited coverage and infrequent revisit times.<n>In contrast, multispectral imaging delivers spatial and temporal coverage but lacks the spectral granularity required for precise detection.
• Score: 1.3516162688685323
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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