TL;DR for operators The paper changes the unit of output in spectral super-resolution. Instead of training a model only to recover a predetermined vector of hyperspectral bands, it learns a mapping that can be queried by spatial position and wavelength. Its proposed SSRON model leads five reported baselines on the held-out benchmark, reducing MAE by about 21% and RMSE by about 20% relative to UNO, the strongest aggregate baseline. ...
TL;DR for operators A standoff LWIR sensor is not looking through a clean window. It is negotiating with air. The paper Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging proposes a lightweight Set-Transformer model for estimating three atmospheric compensation products from passive long-wave infrared hyperspectral measurements: range-specific transmittance, range-specific atmospheric path radiance, and a shared downwelling radiance spectrum.1 The operating idea is simple enough to be useful: instead of trusting one radiance measurement and asking a neural network to perform spectral divination, collect measurements from multiple standoff ranges and let their differences constrain the atmospheric inverse problem. ...