Report
Data-Driven Invertible Neural Surrogates of Atmospheric Transmission
| العنوان: | Data-Driven Invertible Neural Surrogates of Atmospheric Transmission |
|---|---|
| المؤلفون: | Koch, James, Forland, Brenda, Bernacki, Bruce, Doster, Timothy, Emerson, Tegan |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Physics - Atmospheric and Oceanic Physics |
| الوصف: | 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. Comment: Manuscript accepted for presentation and publication at the 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.19605 |
| رقم الانضمام: | edsarx.2404.19605 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |