Atmospheric correction using diffusion models and MODTRAN for constrained training

David Stelter; Ethan Brewer; Robert Sundberg

doi:10.1117/12.3012882

7 June 2024 Atmospheric correction using diffusion models and MODTRAN for constrained training

David Stelter, Ethan Brewer, Robert Sundberg

Proceedings Volume 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX; 130310C (2024) https://doi.org/10.1117/12.3012882
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Diffusion models are a promising generative artificial intelligence (AI) technique for denoising and synthetic data generation. A de-noising diffusion model is applied to the atmospheric correction problem. In place of true noise, the model is trained based on predictions from the physics-based atmospheric radiative transfer tool, MODTRAN, to constrain the training environment. In this paper, we present results from a trained diffusion-based neural network model applied to hyperspectral image data and assess performance compared to conventional empirical atmospheric correction algorithms.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

David Stelter, Ethan Brewer, and Robert Sundberg "Atmospheric correction using diffusion models and MODTRAN for constrained training", Proc. SPIE 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, 130310C (7 June 2024); https://doi.org/10.1117/12.3012882

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