Diffusion learning for atmospheric correction

David Stelter; Robert Sundberg

doi:10.1117/12.2677771

20 October 2023 Diffusion learning for atmospheric correction

David Stelter, Robert Sundberg

Author Affiliations +

Proceedings Volume 12688, Imaging Spectrometry XXVI: Applications, Sensors, and Processing; 1268802 (2023) https://doi.org/10.1117/12.2677771
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Diffusion learning is a generative technique commonly applied to create new images or audio directly from sampled noise. The machine learning approach works by applying degrading signals, such as noise, continuously and learning the denoising process with a neural network. In place of noise, other operations can be performed, such as the addition of atmosphere effects using a physics-based radiative transport code. In this paper, we explore coupling the MODTRAN software to a diffusion learning framework. The goal is to apply atmosphere systematically for a variety of reflective surfaces and use diffusion learning to train models for atmospheric correction. To achieve this, we generate a scoped dataset containing randomized Lambertian surfaces with differing solar illumination and surface angles.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

David Stelter and Robert Sundberg "Diffusion learning for atmospheric correction", Proc. SPIE 12688, Imaging Spectrometry XXVI: Applications, Sensors, and Processing, 1268802 (20 October 2023); https://doi.org/10.1117/12.2677771

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