arXiv:2511.17895v1 Announce Type: cross 
Abstract: Spectral super-resolution (SSR) aims to reconstruct hyperspectral images (HSIs) from multispectral observations, with broad applications in remote sensing. Data-driven methods are widely used, but they often overlook physical principles, leading to unrealistic spectra, particularly in atmosphere-affected bands. To address this challenge, we propose the Spectral Super-Resolution Neural Operator (SSRNO), which incorporates atmospheric radiative transfer (ART) prior into the data-driven procedure, yielding more physically consistent predictions. The proposed SSRNO framework consists of three stages: upsampling, reconstruction, and refinement. In the upsampling stage, we leverage prior information to expand the input multispectral image, producing a physically plausible hyperspectral estimate. Subsequently, we utilize a neural operator in the reconstruction stage to learn a continuous mapping across the spectral domain. Finally, the refinement stage imposes a hard constraint on the output HSI to eliminate color distortion. The upsampling and refinement stages are implemented via the proposed guidance matrix projection (GMP) method, and the reconstruction neural operator adopts U-shaped spectral-aware convolution (SAC) layers to capture multi-scale features. Moreover, we theoretically demonstrate the optimality of the GMP method. With the neural operator and ART priors, SSRNO also achieves continuous spectral reconstruction and zero-shot extrapolation. Various experiments validate the effectiveness and generalization ability of the proposed approach.

تم تقديم مشغل الاستعادة الطيفية الفائق (SSRNO) لتحسين إعادة بناء الصور الطيفية الفائقة من الملاحظات متعددة الطيف، مما يعالج قيود الطرق المعتمدة على البيانات التي غالبًا ما تتجاهل المبادئ الفيزيائية، خاصة في النطاقات المتأثرة بالغلاف الجوي. يتضمن هذا الإطار المبتكر سابقة نقل الإشعاع الجوي، مما يضمن توقعات أكثر دقة وتوافقًا مع الفيزياء.

Se ha presentado el Spectral Super-Resolution Neural Operator (SSRNO) para mejorar la reconstrucción de imágenes hiperespectrales a partir de observaciones multispectrales, abordando las limitaciones de los métodos basados en datos que a menudo ignoran principios físicos, especialmente en bandas afectadas por la atmósfera. Este marco innovador incorpora un antecedente de transferencia radiativa atmosférica, asegurando predicciones más precisas y físicamente consistentes.

Le Spectral Super-Resolution Neural Operator (SSRNO) a été introduit pour améliorer la reconstruction d'images hyperspectrales à partir d'observations multispectrales, en répondant aux limites des méthodes basées sur les données qui négligent souvent les principes physiques, en particulier dans les bandes affectées par l'atmosphère. Ce cadre innovant intègre un antécédent de transfert radiatif atmosphérique, garantissant des prédictions plus précises et physiquement cohérentes.

The Spectral Super-Resolution Neural Operator (SSRNO) has been introduced to enhance the reconstruction of hyperspectral images from multispectral observations, addressing the limitations of existing data-driven methods that often neglect physical principles, particularly in atmosphere-affected bands. This innovative framework incorporates atmospheric radiative transfer prior, ensuring more accurate and physically consistent predictions.

Spectral Super-Resolution Neural Operator with Atmospheric Radiative Transfer Prior

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