Hyperspectral Vision Transformers for Greenhouse Gas Estimations from Space

• URL: http://arxiv.org/abs/2504.16851v1
• Date: Wed, 23 Apr 2025 16:19:42 GMT
• Title: Hyperspectral Vision Transformers for Greenhouse Gas Estimations from Space
• Authors: Ruben Gonzalez Avilés, Linus Scheibenreif, Nassim Ait Ali Braham, Benedikt Blumenstiel, Thomas Brunschwiler, Ranjini Guruprasad, Damian Borth, Conrad Albrecht, Paolo Fraccaro, Devyani Lambhate, Johannes Jakubik,
• Abstract summary: This study proposes a spectral transformer model that synthesizes hyperspectral data from multispectral inputs.<n>The model is pre-trained via a band-wise masked auto-encoder and subsequently fine-tuned on aligned multispectral-hyperspectral image pairs.<n>The resulting synthetic hyperspectral data retain the spatial and temporal benefits of multispectral imagery and improve GHG prediction accuracy.
• Score: 6.559887989252469
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral imaging provides detailed spectral information and holds significant potential for monitoring of greenhouse gases (GHGs). However, its application is constrained by limited spatial coverage and infrequent revisit times. In contrast, multispectral imaging offers broader spatial and temporal coverage but often lacks the spectral detail that can enhance GHG detection. To address these challenges, this study proposes a spectral transformer model that synthesizes hyperspectral data from multispectral inputs. The model is pre-trained via a band-wise masked autoencoder and subsequently fine-tuned on spatio-temporally aligned multispectral-hyperspectral image pairs. The resulting synthetic hyperspectral data retain the spatial and temporal benefits of multispectral imagery and improve GHG prediction accuracy relative to using multispectral data alone. This approach effectively bridges the trade-off between spectral resolution and coverage, highlighting its potential to advance atmospheric monitoring by combining the strengths of hyperspectral and multispectral systems with self-supervised deep learning.

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