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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.19605 |
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| _version_ | 1866917654547136512 |
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| author | Koch, James Forland, Brenda Bernacki, Bruce Doster, Timothy Emerson, Tegan |
| author_facet | Koch, James Forland, Brenda Bernacki, Bruce Doster, Timothy Emerson, Tegan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19605 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Invertible Neural Surrogates of Atmospheric Transmission Koch, James Forland, Brenda Bernacki, Bruce Doster, Timothy Emerson, Tegan Machine Learning Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics 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. |
| title | Data-Driven Invertible Neural Surrogates of Atmospheric Transmission |
| topic | Machine Learning Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2404.19605 |