Data-Driven Invertible Neural Surrogates of Atmospheric Transmission
CoRR(2024)
摘要
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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