Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
Passive long-wave infrared (LWIR) hyperspectral imaging under a standoff geometry depends on atmospheric absorption and emission, as well as reflected radiance, thus making atmospheric compensation essential to get knowledge of a target of interest. Despite its importance, this compensation has been largely overlooked due to its practical and modeling difficulty. In this paper, we present a lightweight set-based deep learning framework that takes multiple radiance measurements, collected at different standoff ranges, as input and jointly estimates transmittance, atmospheric path radiance, and a shared downwelling spectrum. We analyze the learned representation with a sparse autoencoder an...
| Authors | Fabian Perez, Nicolas Quintero, Jeferson Acevedo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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- SemilleroCV/SAE-LWIRDataset · cited by
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- SemilleroCV/SAE-LWIRDataset
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