A true hyperspectral image super-resolution dataset

Tools

Ulrichsen, Alexander and De Kerf, Thomas and Dunphy, R. David and Murray, Paul and Vanlanduit, S and Marshall, Stephen; (2025) A true hyperspectral image super-resolution dataset. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, USA, pp. 4421-4430. ISBN 979-8-3315-9994-2 (https://doi.org/10.1109/CVPRW67362.2025.00426)

[thumbnail of Ulrichsen_A_True_Hyperspectral_Image_Super-Resolution_Dataset_CVPRW_2025_paper]
Preview
 Text. Filename: Ulrichsen_A_True_Hyperspectral_Image_Super-Resolution_Dataset_CVPRW_2025_paper.pdf
Accepted Author Manuscript
License: Creative Commons Attribution 4.0 logo
Download (5MB)| Preview

Abstract

Hyperspectral imaging, crucial in remote sensing, provides extensive spectral information at the cost of lower spatial resolution compared to standard color images. Single-image super-resolution, reconstructing high-resolution images from low-resolution inputs, is particularly useful for enhancing hyperspectral images. Due to the unavailability of real low- and high-resolution image pairs, many hyperspectral image super-resolution methods resort to downsampling for training. This leads to suboptimal performance on real-world data due to inherent assumptions in the downsampling process. This paper introduces a novel dataset featuring actual low- and high-resolution hyperspectral image pairs, captured using different lenses and sensors. We train various super-resolution models on this dataset and compare their performance against models trained on artificially downsampled high-resolution images. Our findings reveal that models trained with real image pairs substantially outperform basic bicubic interpolation, whereas those trained with synthetically generated low-resolution images do not, highlighting the importance of using authentic high- and low-resolution images for training.

ORCID iDs

Ulrichsen, Alexander, De Kerf, Thomas, Dunphy, R. David ORCID logoORCID: https://orcid.org/0000-0003-3891-0432, Murray, Paul ORCID logoORCID: https://orcid.org/0000-0002-6980-9276, Vanlanduit, S and Marshall, Stephen ORCID logoORCID: https://orcid.org/0000-0001-7079-5628;
CORE (COnnecting REpositories)