Presentation + Paper
13 June 2023 A neural differential equation formulation for modeling atmospheric effects in hyperspectral images
James Koch, Brenda Forland, Timothy Doster, Tegan Emerson
Author Affiliations +
Abstract
Atmospheric correction is the process for removing atmospheric effects from spectral data; a necessary step for recovering salient spectral properties. The complex interactions between the atmosphere and light are dominated by absorbance and scattering physics. Existing methods for modeling atmospheric interactions typically rely on deep knowledge of relevant environmental conditions and high-fidelity numerical simulations of the governing physics in order to obtain accurate estimates of these effects. Additionally, existing approaches often require a subject matter expert for pre/post-processing of the data. Model-based approaches for removing atmospheric effects struggle in situations where such domain expertise is not available, and require significant human effort and computational power even when that expertise is available. In contrast, we propose a data-driven approach the uses Neural Differential Equations (NDEs) to accurately learn the interactions between electromagnetic radiation and the atmospheric without access to location specific environmental information. Once trained, the NDE can be applied bi-directionally; to apply or remove atmospheric effects. We demonstrate the effectiveness and utility of these techniques on an example multi-spectral scene.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James Koch, Brenda Forland, Timothy Doster, and Tegan Emerson "A neural differential equation formulation for modeling atmospheric effects in hyperspectral images", Proc. SPIE 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX , 125190E (13 June 2023); https://doi.org/10.1117/12.2663958
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KEYWORDS
Atmospheric modeling

Atmospheric corrections

Atmospheric physics

Machine learning

Modeling

Physics

Differential equations

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