Data-Driven Invertible Neural Surrogates of Atmospheric Transmission

• URL: http://arxiv.org/abs/2404.19605v1
• Date: Tue, 30 Apr 2024 14:55:57 GMT
• Title: Data-Driven Invertible Neural Surrogates of Atmospheric Transmission
• Authors: James Koch, Brenda Forland, Bruce Bernacki, Timothy Doster, Tegan Emerson,
• Abstract summary: We present a framework for inferring an atmospheric transmission profile from a spectral scene. A physics-based simulator is automatically tuned to construct a surrogate atmospheric profile to model the observed data.
• Score: 2.6938549839852524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a framework for inferring an atmospheric transmission profile from a spectral scene. This framework leverages a lightweight, physics-based simulator that is automatically tuned - by virtue of autodifferentiation and differentiable programming - to construct a surrogate atmospheric profile to model the observed data. We demonstrate utility of the methodology by (i) performing atmospheric correction, (ii) recasting spectral data between various modalities (e.g. radiance and reflectance at the surface and at the sensor), and (iii) inferring atmospheric transmission profiles, such as absorbing bands and their relative magnitudes.

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