| | --- |
| | license: cc-by-4.0 |
| | language: |
| | - en |
| | tags: |
| | - super-resolution |
| | - hyperspectral |
| | - remote_sensing |
| | size_categories: |
| | - 1K<n<10K |
| | --- |
| | # GEWDiff Training & Evaluation Dataset |
| |
|
| | ## 📘 Overview |
| |
|
| | The **GEWDiff Training & Evaluation Dataset** is derived from the EnMAP Champion and MDAS hyperspectral datasets. |
| | It is designed for image enhancement, super-resolution, restoration, and generative remote sensing tasks. |
| | The dataset includes Low-Quality (LQ) low-resolution images, corresponding Ground-Truth (GT) high-resolution images, and optional structure information such as **masks** and **edges** (partially provided; remaining components can be automatically generated using the accompanying GitHub scripts). |
| |
|
| | All data have been preprocessed, spatially tiled, spectrally unified, and harmonized through **nearest-neighbor approximation of the spectral response functions (SRF)**. |
| |
|
| | --- |
| |
|
| | ## 📂 Dataset Structure |
| |
|
| | ### **1. Training Set** |
| | - **LQ images**: low-quality / low-resolution observations |
| | - **GT images**: high-quality ground-truth targets |
| | - **Mask (partial)**: missing parts can be generated with included scripts |
| | - **Edge (partial)**: missing parts can be generated with included scripts |
| | Used for model training across various reconstruction and generative tasks. |
| |
|
| | --- |
| |
|
| | ### **2. Validation Set (val)** |
| | - Same structure as the training set |
| | - Paired LQ–GT samples for model validation and tuning |
| |
|
| | --- |
| |
|
| | ### **3. Test Sets (with ground truth)** |
| | Includes several subsets: |
| |
|
| | - **MDAS1** |
| | - **MDAS2** |
| | - **WDC** |
| |
|
| | These subsets contain paired LQ–GT data and are suitable for quantitative evaluations. |
| |
|
| | --- |
| |
|
| |
|
| |
|
| | ## 📐 Preprocessing Details |
| |
|
| | The dataset originates from **EnMAP Champion** and **MDAS hyperspectral** sources. |
| | All data have undergone: |
| |
|
| | - Spatial tiling |
| | - Spectral band unification |
| | - **Spectral response harmonization using nearest-neighbor approximation** |
| | - Conversion into LQ/GT pairs suitable for super-resolution, enhancement, and generative modeling tasks |
| |
|
| | --- |
| |
|
| | ## 🔧 Additional Resources |
| |
|
| | Mask and edge maps—when not provided—can be generated automatically using the scripts available in the linked GitHub repository. |
| | These structural cues enable models to leverage both texture and geometric information. |
| |
|
| | --- |
| |
|
| | ## 📑 Citation |
| |
|
| | If you use this dataset in your research or applications, please cite **our paper** (arXiv](https://arxiv.org/abs/2511.07103)): |
| |
|
| | ```bibtex |
| | @misc{wang2025gewdiffgeometricenhancedwaveletbased, |
| | title={GEWDiff: Geometric Enhanced Wavelet-based Diffusion Model for Hyperspectral Image Super-resolution}, |
| | author={Sirui Wang and Jiang He and Natàlia Blasco Andreo and Xiao Xiang Zhu}, |
| | year={2025}, |
| | eprint={2511.07103}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.CV}, |
| | url={https://arxiv.org/abs/2511.07103}, |
| | } |
| | |
| | |
