Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
Summary
A lightweight set-based deep learning framework using a transformer is presented for atmospheric compensation in passive long-wave infrared hyperspectral imaging, jointly estimating transmittance, atmospheric path radiance, and downwelling spectrum from multi-range radiance measurements.
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Paper page - Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
Source: https://huggingface.co/papers/2606.08324
Abstract
A lightweight deep learning framework is presented for atmospheric compensation in passive long-wave infrared hyperspectral imaging, enabling joint estimation of transmittance, atmospheric path radiance, and downwelling spectrum from multi-range radiance measurements.
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 lightweightset-based deep learning frameworkthat takes multiple radiance measurements, collected at different standoff ranges, as input and jointly estimates transmittance,atmospheric path radiance, and a shareddownwelling spectrum. We analyze the learned representation with asparse autoencoderand observe that several latent features do activate on geographically coherent subsets of the test data despite the absence of location supervision. Experiments on a MODTRAN generated standoff LWIR dataset demonstrate low spectral distortion across all estimated products. The dataset and code is publicly available at: https://factral.co/SAE-LWIR/
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