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