Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging

• URL: http://arxiv.org/abs/2606.08324v1
• Date: Sat, 06 Jun 2026 20:25:30 GMT
• Title: Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
• Abstract summary: We present a lightweight set-based deep learning framework that takes multiple radiance measurements and estimates transmittance, atmospheric path radiance, and a shared downwelling spectrum.<n>We analyze the learned representation with a sparse autoencoder and observe that several latent features do activate on geographically coherent subsets of the test data.
• Score: 6.066442015301664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 and 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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