Super-Resolving Unseen Hyperspectral Sensors at Any Scale via Spatial Operators

2026-09-30

AI Summary

This study addresses the challenges of cross-sensor generalization and arbitrary-scale reconstruction in hyperspectral super-resolution, where existing methods require additional training for novel scenarios. We propose OmniHSR, an all-band shared operator prediction paradigm that achieves spatial-spectral decoupling by resampling inputs via cross-spectral mapping, predicting Gaussian-supported local operators, and applying them across all bands through continuous operator field reconstruction. This formulation enables zero-shot transfer to unseen sensors and arbitrary scales with minimal parameters. Extensive experiments demonstrate that OmniHSR outperforms baselines on seven datasets and achieves state-of-the-art zero-shot performance on six unseen datasets. Furthermore, it yields a 0.55 dB PSNR improvement across twelve upscaling factors while accelerating inference by 36×.

Abstract

Achieving cross-sensor generalization and arbitrary-scale reconstruction with a single model remains challenging in hyperspectral super-resolution (HSR). Although recent methods support arbitrary-scale reconstruction, applying them to new sensors or scales beyond the training range often requires additional data and computation to maintain reconstruction quality. To address these challenges, we propose OmniHSR, which predicts band-shared spatial operators rather than spectral values. Cross-Spectral Mapping (CSM) resamples inputs with any number of bands to fixed reference positions and predicts local operators with Gaussian supports. Continuous Operator-Field Reconstruction (COFR) composes these operators into a continuous field and applies them to all original bands for arbitrary-scale reconstruction. Experiments demonstrate that operator prediction outperforms direct spectral-value prediction on all seven datasets. Trained solely on ARAD with only 0.538M parameters, OmniHSR outperforms all directly transferred baselines on six unseen datasets without target-domain training data or adaptation. Across twelve upsampling factors from $\times2$ to $\times48$, it improves average PSNR on Pavia U and Chikusei by 0.55 dB over the strongest baseline. It also surpasses baselines trained from scratch or adapted on the target sensor and achieves up to $36\times$ faster inference. Our code will be publicly released soon.

Problem

• Hyperspectral Super-Resolution
• Cross-Sensor Generalization
• Arbitrary-Scale Reconstruction
• Unseen Sensors

Innovation

• Hyperspectral Super-Resolution
• Spatial Operators
• Arbitrary-Scale Reconstruction
• Cross-Sensor Generalization
• Continuous Operator-Field