Summary of Data-driven Invertible Neural Surrogates Of Atmospheric Transmission, by James Koch et al.
Data-Driven Invertible Neural Surrogates of Atmospheric Transmission
by James Koch, Brenda Forland, Bruce Bernacki, Timothy Doster, Tegan Emerson
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Atmospheric and Oceanic Physics (physics.ao-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a framework for inferring atmospheric transmission profiles from spectral scenes. The method uses a lightweight simulator that is automatically tuned using autodifferentiation and differentiable programming to construct a surrogate atmospheric profile. This framework demonstrates its utility by performing atmospheric correction, recasting spectral data between modalities, and inferring atmospheric transmission profiles, including absorbing bands and their relative magnitudes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand the atmosphere better by creating a special tool that can predict what’s happening in the air from looking at pictures of the sky. This tool uses math to make a pretend version of the air, which helps fix mistakes in our measurements and gives us new ways to look at data. It even helps us figure out things like where the air absorbs certain kinds of light. |
