README.md

# RRealHyperPDID: Remote Sensing Real-World Hyperspectral Paired Dehazing Image Dataset

A real-world paired dataset for hyperspectral and RGB image dehazing, containing aligned hazy and clear image pairs in both RGB and hyperspectral formats. 
Designed for benchmarking dehazing methods under authentic real-world atmospheric conditions.

**Official Dataset for HyperHazeOff:** Hyperspectral Remote Sensing Image Dehazing Benchmark [MDPI](https://www.mdpi.com/2313-433X/11/12/422) [Preprints.org](https://www.preprints.org/manuscript/202510.1565).

**Official Code:** [GitHub](https://github.com/iitpvisionlab/hyperhazeoff).

## Dataset Versions

The dataset includes three versions of data and annotations for downstream task quality assessment:

### RGB/
- **CSNC/** — RGB images synthesized using narrowband spectral reconstruction (`.png` format)
- **CSSO/** — RGB images synthesized via CIE XYZ → sRGB transformation using the standard observer (`.png` format)

*Note: CSNC and CSSO contain the same RGB data with different color synthesis methods*

### HSI/
Hyperspectral images (`.npy` format)
Each hyperspectral image has size 256×256 pixels with 182 spectral bands.

Additionally, wavelength information for the hyperspectral bands is provided in the file *hsi/wavelengths_realhyper.npy.npy*, supporting spectral analysis across the dataset.

### MARKUP/
Field delineation annotations (`.json` format)

## Dataset Structure
```
RRealHyperPDID/
├── RGB/
│   ├── CSNC/
│   │   ├── S01C00/
│   │   │   ├── f160621t01p00r17_clean.png
│   │   │   └── f170607t01p00r12_hazed.png
│   │   ├── ...
│   │   └── S17C04/
│   │       ├── *_clean.png
│   │       └── *_hazed.png
│   └── CSSO/
│       ├── S01C00/
│       │   ├── f160621t01p00r17_clean.png
│       │   └── f170607t01p00r12_hazed.png
│       ├── ...
│       └── S17C04/
│           ├── *_clean.png
│           └── *_hazed.png
├── HSI/
│   ├── S01C00/
│   │   ├── f160621t01p00r17_clean.npy
│   │   └── f170607t01p00r12_hazed.npy
│   ├── ...
│   └── S17C04/
│       ├── *_clean.npy
│       └── *_hazed.npy
└── MARKUP/
    ├── places.txt
    ├── S03C00/
    │   ├── f210324t01p00r11_hazed.json
    │   └── f210402t01p00r09_clean.json
    ├── ...
    └── S16C11/
        ├── *_hazed.json
        └── *_clean.json

```

## Changelog

### v1.1 (2025-11-17)
- Updated HSI.zip, RGB.zip, Markup.zip with improved image alignment
- Image pairs are now better registered/aligned for more accurate correspondence

### v1.0
- Initial release

## Data Source

Data derived from AVIRIS imagery.  
Courtesy NASA/JPL-Caltech.

## Citation

```
@Article{jimaging11120422,
AUTHOR = {Nikonorov, Artem and Sidorchuk, Dmitry and Odinets, Nikita and Volkov, Vladislav and Sarycheva, Anastasia and Dudenko, Ekaterina and Zhidkov, Mikhail and Nikolaev, Dmitry},
TITLE = {HyperHazeOff: Hyperspectral Remote Sensing Image Dehazing Benchmark},
JOURNAL = {Journal of Imaging},
VOLUME = {11},
YEAR = {2025},
NUMBER = {12},
ARTICLE-NUMBER = {422},
URL = {https://www.mdpi.com/2313-433X/11/12/422},
ISSN = {2313-433X},
ABSTRACT = {Hyperspectral remote sensing images (HSIs) provide invaluable information for environmental and agricultural monitoring, yet they are often degraded by atmospheric haze, which distorts spatial and spectral content and hinders downstream analysis. Progress in hyperspectral dehazing has been limited by the absence of paired real-haze benchmarks; most prior studies rely on synthetic haze or unpaired data, restricting fair evaluation and generalization. We present HyperHazeOff, the first comprehensive benchmark for hyperspectral dehazing that unifies data, tasks, and evaluation protocols. It comprises (i) RRealHyperPDID, 110 scenes with paired real-haze and haze-free HSIs (plus RGB images), and (ii) RSyntHyperPDID, 2616 paired samples generated using a physically grounded haze formation model. The benchmark also provides agricultural field delineation and land classification annotations for downstream task quality assessment, standardized train/validation/test splits, preprocessing pipelines, baseline implementations, pretrained weights, and evaluation tools. Across six state-of-the-art methods (three RGB-based and three HSI-specific), we find that hyperspectral models trained on the widely used HyperDehazing dataset fail to generalize to real haze, while training on RSyntHyperPDID enables significant real-haze restoration by AACNet. HyperHazeOff establishes reproducible baselines and is openly available to advance research in hyperspectral dehazing.},
DOI = {10.3390/jimaging11120422}
}
```

## License

This dataset is released under license: [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/)