---
license: apache-2.0
task_categories:
  - image-classification
  - zero-shot-image-classification

tags:
  - hyperspectral imaging
  - remote-sensing
  - land-use
  - land-cover
  - HSI
  - earth-observation
  - deep-learning
  - benchmark
  - computer-vision
pretty_name: HSI Datasets Collection
size_categories:
  - 10G<n<100G
language:
  - en

---

# 🛰️ HSI Datasets Collection

A comprehensive collection of **24 publicly available Hyperspectral Image (HSI) datasets** curated for research in hyperspectral image classification, land use/land cover (LULC) mapping, and remote sensing deep learning benchmarks.

> **Total Size:** ~20.1 GB  
> **License:** Apache 2.0  
> **Maintained by:** [Tanishq Rachamalla](https://huggingface.co/Tanishq165), [Aryan Das](https://huggingface.co/aryadomain)  
> **HuggingFace Dataset:** [https://huggingface.co/datasets/Tanishq165/HSI_Datasets](https://huggingface.co/datasets/Tanishq165/HSI_Datasets)

---

## 📖 Dataset Description

Hyperspectral imaging captures data across hundreds of narrow spectral bands, enabling fine-grained material and land cover discrimination beyond what RGB or multispectral sensors can achieve. This repository consolidates 24 widely-used HSI benchmark datasets in a single place, covering a variety of sensors (AVIRIS, ROSIS, Hyperion, CRISM), geographic regions (USA, Europe, Asia, Mars), and scene types (urban, agricultural, coastal, planetary).

All datasets are stored in `.mat` format (MATLAB-compatible), making them directly loadable with `scipy.io` in Python.

---

## 📦 Included Datasets

All datasets are hosted on the **HuggingFace Hub**: [`Tanishq165/HSI_Datasets`](https://huggingface.co/datasets/Tanishq165/HSI_Datasets)

| # | Dataset | Sensor | Scene Type | Region |
|---|---------|--------|------------|--------|
| 1 | **Augsburg** | DAS Specim | Urban | Augsburg, Germany |
| 2 | **Berlin** | HyMap | Urban | Berlin, Germany |
| 3 | **Botswana** | NASA EO-1 Hyperion | Wetland/Vegetation | Okavango Delta, Africa |
| 4 | **Chikusei** | Headwall Photonics | Agricultural/Urban | Chikusei, Japan |
| 5 | **Dioni** | AVIRIS-NG | Mixed | Dioni, Greece |
| 6 | **Holden** | MRO CRISM | Planetary | Mars |
| 7 | **Houston 2013** | ITRES CASI-1500 | Urban | Houston, TX, USA |
| 8 | **Houston 2018** | AVIRIS-NG | Urban | Houston, TX, USA |
| 9 | **Indian Pines** | NASA AVIRIS | Agriculture/Forest | Indiana, USA |
| 10 | **KSC** | NASA AVIRIS | Wetland/Vegetation | Florida, USA |
| 11 | **Loukia** | AVIRIS-NG | Mixed | Loukia, Greece |
| 12 | **Muufl** | ITRES CASI-1500 | Urban/Vegetation | Mississippi, USA |
| 13 | **NiliFossae** | MRO CRISM | Planetary | Mars |
| 14 | **Pavia Center** | ROSIS | Urban | Pavia, Italy |
| 15 | **Pavia University** | ROSIS | Urban | Pavia, Italy |
| 16 | **Pingan** | — | Urban/Rural | China |
| 17 | **Qingyun** | — | Agricultural | China |
| 18 | **Salinas** | NASA AVIRIS | Agriculture | Salinas Valley, USA |
| 19 | **Tangdoaowan** | — | Coastal | China |
| 20 | **Trento** | AISA Eagle | Rural | Trento, Italy |
| 21 | **Utopia** | MRO CRISM | Planetary | Mars |
| 22 | **WHU-Hi-HanChuan** | Headwall Nano | Agricultural | HanChuan, China |
| 23 | **WHU-Hi-HongHu** | Headwall Nano | Agricultural | HongHu, China |
| 24 | **WHU-Hi-LongKou** | Headwall Nano | Agricultural | LongKou, China |

---

## 🗂️ Repository Structure

### HuggingFace Hub Structure

```
HSI_Datasets/
├── Augsburg/
├── Berlin/
├── Botswana/
├── Chikusei/
├── Dioni/
├── Holden/
├── Houston13/
├── Houston18/
├── Indian_Pines/
├── KSC/
├── Loukia/
├── Muufl/
├── NiliFossae/
├── Pavia_Center/
├── Pavia_University/
├── Pingan/
├── Qingyun/
├── Salinas/
├── Tangdoaowan/
├── Trento/
├── Utopia/
├── WHU-Hi-HanChuan/
├── WHU-Hi-HongHu/
└── WHU-Hi-LongKou/
```
Each dataset folder contains:
```
DatasetName/
├── data.mat        # Hyperspectral cube: (H × W × Bands)
└── gt.mat          # Ground truth label map: (H × W)
```

---

## 🚀 Loading the Data

### Install dependencies

```bash
pip install huggingface_hub scipy numpy h5py scikit-learn pyyaml matplotlib
```

### Load a single dataset (via project loader)

```python
from utils.data_loader import DatasetLoader
import numpy as np

loader = DatasetLoader(use_cache=True)

# Auto-downloads from HuggingFace Hub on first use
hsi_cube, labels = loader.load_dataset("Indian_Pines")

print(f"HSI Cube shape : {hsi_cube.shape}")
print(f"Labels shape   : {labels.shape}")
print(f"Num classes    : {len(np.unique(labels)) - 1}")  # excluding background
```

### Download all datasets

```python
from huggingface_hub import snapshot_download

local_path = snapshot_download(
    repo_id="Tanishq165/HSI_Datasets",
    repo_type="dataset"
)
print(f"Downloaded to: {local_path}")
```

---

## 📐 Data Format

| Property | Details |
|---|---|
| **File Format** | `.mat` (MATLAB / scipy compatible) |
| **HSI Cube Shape** | `(Height × Width × Spectral Bands)` |
| **Ground Truth Shape** | `(Height × Width)` — integer class labels |
| **Background Class** | Label `0` represents unlabeled/background pixels |
| **Value Range** | Reflectance values (varies per dataset; typically float32 or uint16) |

---

## 🔬 Applications & Use Cases

- **Hyperspectral Image Classification (HSI)** — pixel-wise or patch-based
- **Land Use / Land Cover (LULC) Mapping**
- **Benchmarking deep learning architectures** — CNNs, Vision Transformers (ViT), Mamba/SSMs, Graph Neural Networks
- **Transfer learning & domain adaptation** across HSI scenes
- **Multi-sensor data fusion** (e.g., HSI + LiDAR for Trento/Augsburg)
- **Planetary surface analysis** — NiliFossae, Utopia and Holden(Mars CRISM data)
- **Spectral unmixing and abundance estimation**

---

## 📊 Dataset Specifications

Complete reference for all 24 datasets:

| # | Dataset | H × W | Bands | Classes | Labeled Samples | Sensor | Region |
|---|---------|-------|-------|---------|-----------------|--------|--------|
| 1 | **Augsburg** | 332 × 485 | 180 | 7 | 78,294 | DAS Specim | Germany |
| 2 | **Berlin** | 1723 × 476 | 244 | 8 | 464,671 | HyMap | Germany |
| 3 | **Botswana** | 1476 × 256 | 145 | 14 | 3,248 | EO-1 Hyperion | Africa |
| 4 | **Chikusei** | 2517 × 2335 | 128 | 19 | 77,592 | Headwall | Japan |
| 5 | **Dioni** | 250 × 1376 | 176 | 12 | 20,024 | AVIRIS-NG | Greece |
| 6 | **Holden** | 595 × 440 | 418 | 6 | 20,090 | CRISM | Mars |
| 7 | **Houston13** | 349 × 1905 | 144 | 15 | 15,029 | CASI-1500 | USA |
| 8 | **Houston18** | 1202 × 4768 | 48 | 20 | 150,029 | AVIRIS-NG | USA |
| 9 | **Indian_Pines** | 200 × 145 | 145 | 16 | 10,249 | AVIRIS | USA |
| 10 | **KSC** | 512 × 614 | 176 | 13 | 5,211 | AVIRIS | USA |
| 11 | **Loukia** | 249 × 945 | 176 | 14 | 13,503 | AVIRIS-NG | Greece |
| 12 | **Muufl** | 325 × 220 | 64 | 11 | 53,687 | CASI-1500 | USA |
| 13 | **NiliFossae** | 478 × 593 | 425 | 9 | 26,710 | CRISM | Mars |
| 14 | **Pavia Center** | 1096 × 715 | 102 | 9 | 148,152 | ROSIS | Italy |
| 15 | **Pavia University** | 610 × 340 | 103 | 9 | 42,776 | ROSIS | Italy |
| 16 | **Pingan** | 1230 × 1000 | 176 | 10 | 1,140,937 | — | China |
| 17 | **Qingyun** | 880 × 1360 | 176 | 6 | 954,893 | — | China |
| 18 | **Salinas** | 512 × 217 | 204 | 16 | 54,129 | AVIRIS | USA |
| 19 | **Tangdoaowan** | 1740 × 860 | 176 | 18 | 557,366 | — | China |
| 20 | **Trento** | 166 × 600 | 63 | 6 | 30,214 | AISA Eagle | Italy |
| 21 | **Utopia** | 478 × 595 | 432 | 9 | 17,338 | CRISM | Mars |
| 22 | **WHU-Hi-HanChuan** | 1217 × 303 | 274 | 16 | 257,530 | Headwall | China |
| 23 | **WHU-Hi-HongHu** | 940 × 475 | 270 | 22 | 386,693 | Headwall | China |
| 24 | **WHU-Hi-LongKou** | 550 × 400 | 270 | 9 | 204,542 | Headwall | China |

> **Note:** `H × W` = Height × Width (spatial dimensions). Labeled samples exclude background pixels (class 0).

---

## 🗂️ Dataset File Reference

Each folder contains `.mat` files. The loader auto-detects data vs GT by filename suffix:

| Suffix | Content |
|--------|---------|
| `_gt.mat` or `gt.mat` | Ground truth label map |
| `_data.mat` or `data.mat` | Hyperspectral cube |
| `_corrected.mat` | Corrected HSI cube (e.g. Indian Pines) |


---

## 🏷️ Class Names Reference

### Indian_Pines (16 classes)
1. Alfalfa | 2. Corn-notill | 3. Corn-mintill | 4. Corn | 5. Grass-pasture | 6. Grass-trees | 7. Grass-pasture-mowed | 8. Hay-windrowed | 9. Oats | 10. Soybean-notill | 11. Soybean-mintill | 12. Soybean-clean | 13. Wheat | 14. Woods | 15. Buildings-Grass-Trees-Drives | 16. Stone-Steel-Towers

### Pavia University (9 classes)
1. Asphalt | 2. Meadows | 3. Gravel | 4. Trees | 5. Painted metal sheets | 6. Bare Soil | 7. Bitumen | 8. Self-Blocking Bricks | 9. Shadows

### Pavia Center (9 classes)
1. Water | 2. Trees | 3. Asphalt | 4. Self-Blocking Bricks | 5. Bitumen | 6. Tiles | 7. Shadows | 8. Meadows | 9. Bare Soil

### Salinas (16 classes)
1. Brocoli_green_weeds_1 | 2. Brocoli_green_weeds_2 | 3. Fallow | 4. Fallow_rough_plow | 5. Fallow_smooth | 6. Stubble | 7. Celery | 8. Grapes_untrained | 9. Soil_vinyard_develop | 10. Corn_senesced_green_weeds | 11. Lettuce_romaine_4wk | 12. Lettuce_romaine_5wk | 13. Lettuce_romaine_6wk | 14. Lettuce_romaine_7wk | 15. Vinyard_untrained | 16. Vinyard_vertical_trellis

### KSC (13 classes)
1. Scrub | 2. Willow swamp | 3. CP hammock | 4. Slash pine | 5. Oak/Broadleaf | 6. Hardwood | 7. Swamp | 8. Graminoid marsh | 9. Spartina marsh | 10. Cattail marsh | 11. Salt marsh | 12. Mud flats | 13. Water

### Botswana (14 classes)
1. Water | 2. Hippo grass | 3. Floodplain grasses 1 | 4. Floodplain grasses 2 | 5. Reeds | 6. Riparian | 7. Firescar | 8. Island interior | 9. Acacia woodlands | 10. Acacia shrublands | 11. Acacia grasslands | 12. Short mopane | 13. Mixed mopane | 14. Exposed soils

### Houston13 (15 classes)
> 2013 IEEE GRSS Data Fusion Contest | 2.5 m/pixel | 380–1050 nm
1. Healthy Grass | 2. Stressed Grass | 3. Synthetic Grass | 4. Trees | 5. Soil | 6. Water | 7. Residential | 8. Commercial | 9. Road | 10. Highway | 11. Railway | 12. Parking Lot 1 | 13. Parking Lot 2 | 14. Tennis Court | 15. Running Track

### Houston18 (20 classes)
> 2018 IEEE GRSS Data Fusion Contest | 1 m/pixel | 380–1050 nm | Area: UH campus + surroundings
1. Healthy Grass | 2. Stressed Grass | 3. Artificial Turf | 4. Evergreen Trees | 5. Deciduous Trees | 6. Bare Earth | 7. Water | 8. Residential Buildings | 9. Non-residential Buildings | 10. Roads | 11. Sidewalks | 12. Crosswalks | 13. Major Thoroughfares | 14. Highways | 15. Railways | 16. Paved Parking Lots | 17. Unpaved Parking Lots | 18. Cars | 19. Trains | 20. Stadium Seats

### Trento (6 classes)
1. Apples | 2. Buildings | 3. Ground | 4. Woods | 5. Vineyard | 6. Roads

### Muufl (11 classes)
1. Trees | 2. Mostly grass | 3. Mixed ground surface | 4. Dirt and sand | 5. Road | 6. Water | 7. Buildings shadow | 8. Buildings | 9. Sidewalk | 10. Yellow curb | 11. Cloth panels

### NiliFossae (9 classes) — Mars
1. Fe-Olivine | 2. Epidote | 3. Chlorite | 4. Bassanite | 5. Illite/Muscovite | 6. Mg-Carbonate | 7. Plagioclase | 8. Prehnite | 9. Serpentine

### Utopia (9 classes) — Mars
1. Analcime | 2. Bassanite | 3. High-Ca Pyroxene | 4. Illite/Muscovite | 5. Low-Ca Pyroxene | 6. Mg-Smectite | 7. Monohydrated sulfate | 8. Plagioclase | 9. Prehnite

### Holden (6 classes) — Mars
1. Analcime | 2. Plagioclase | 3. Prehnite | 4. High-Ca Pyroxene | 5. Serpentine | 6. Margarite

### WHU-Hi-HongHu (22 classes)
1. Red roof | 2. Road | 3. Bare soil | 4. Cotton | 5. Cotton firewood | 6. Rape | 7. Chinese cabbage | 8. Pakchoi | 9. Cabbage | 10. Tuber mustard | 11. Brassica parachinensis | 12. Brassica chinensis | 13. Small Brassica chinensis | 14. Lactuca sativa | 15. Celtuce | 16. Film covered lettuce | 17. Romaine lettuce | 18. Carrot | 19. White radish | 20. Garlic sprout | 21. Broad bean | 22. Tree

### WHU-Hi-HanChuan (16 classes)
1. Strawberry | 2. Cowpea | 3. Soybean | 4. Sorghum | 5. Water spinach | 6. Watermelon | 7. Greens | 8. Trees | 9. Grass | 10. Red roof | 11. Gray roof | 12. Plastic | 13. Bare soil | 14. Road | 15. Bright object | 16. Water

### WHU-Hi-LongKou (9 classes)
1. Corn | 2. Cotton | 3. Sesame | 4. Broad-leaf soybean | 5. Narrow-leaf soybean | 6. Rice | 7. Water | 8. Roads and houses | 9. Mixed weed

### Dioni (12 classes)
1. Dense Urban Fabric | 2. Mineral Extraction Sites | 3. Non Irrigated Arable Land | 4. Fruit Trees | 5. Olive Groves | 6. Coniferous Forest | 7. Dense Sclerophyllous Vegetation | 8. Sparce Sclerophyllous Vegetation | 9. Sparsely Vegetated Areas | 10. Rocks and Sand | 11. Water | 12. Coastal Water

### Loukia (14 classes)
1. Dense Urban Fabric | 2. Mineral Extraction Sites | 3. Non Irrigated Arable Land | 4. Fruit Trees | 5. Olive Groves | 6. Broad Leaved Forest | 7. Coniferous Forest | 8. Mixed Forest | 9. Dense Sclerophyllous Vegetation | 10. Sparce Sclerophyllous Vegetation | 11. Sparsely Vegetated Areas | 12. Rocks and Sand | 13. Water | 14. Coastal Water

### Berlin (8 classes)
1. Forest | 2. Residential | 3. Industrial | 4. Low Plants | 5. Soil | 6. Allotment | 7. Commercial | 8. Water

### Augsburg (7 classes)
1. Forest | 2. Residential Area | 3. Industrial Area | 4. Low Plants | 5. Allotment | 6. Commercial Area | 7. Water

### Pingan (10 classes)
1. Seawater | 2. Road | 3. Trees | 4. Floating pier | 5. Brick houses | 6. Steel houses | 7. Ship | 8. Car | 9. Concrete building | 10. Grass

### Qingyun (6 classes)
1. Trees | 2. Car | 3. Asphalt road | 4. Concrete building | 5. Water | 6. Grass

### Tangdoaowan (18 classes)
1. Rubber track | 2. Asphalt | 3. Grassland | 4. Ligustrum vicaryi | 5. Bare soil | 6. Populus | 7. Flagging | 8. Boardwalk | 9. Bulrush | 10. Coniferous pine | 11. Buxus sinica | 12. Ulmus pumila L | 13. Sandy | 14. Roof shadows | 15. Gravel road | 16. Spiraea | 17. Photinia serrulata | 18. Seawater

### Chikusei (19 classes)
1. Water | 2. Bare soil (farmland) | 3. Bare soil (park) | 4. Bare soil (roadside) | 5. Pavement (asphalt) | 6. Pavement (brick) | 7. Farmland (paddy) | 8. Farmland (other) | 9. Greenhouse | 10. Grass (park) | 11. Grass (roadside) | 12. Tree (park) | 13. Tree (roadside) | 14. Forest | 15. Building (low-rise) | 16. Building (high-rise) | 17. Building (factory) | 18. Power line | 19. Swimming pool

---

## ⚠️ Data Loading Notes

- **Auto-transpose:** The loader automatically detects and transposes data to `(H, W, Bands)` if needed.
- **Background class:** Class `0` is always background/unlabeled and excluded from training.
- **v7.3 MAT files:** Some datasets use HDF5-based `.mat` format. The loader falls back to `h5py` automatically.
- **Class imbalance:** Many datasets have highly imbalanced classes (e.g. Indian Pines, Houston18). Consider weighted loss or balanced sampling.

---

## 📄 Citation

If you use this dataset collection in your research, please cite:

```bibtex
@misc{tanishq2026hsidatasets,
  author       = {Tanishq Rachamalla, Aryan Das},
  title        = {HSI Datasets Collection},
  year         = {2026},
  publisher    = {HuggingFace},
  howpublished = {\url{https://huggingface.co/datasets/Tanishq165/HSI_Datasets}}
}
```

Please also cite the **original dataset papers** for each specific dataset you use in your work.

---

## 🤝 Acknowledgements

These datasets are sourced from publicly available repositories and the broader remote sensing research community. Full credit goes to the original dataset creators including:

- **NASA JPL / JPL AVIRIS** — Indian Pines, Salinas, KSC, Botswana
- **Wuhan University** — WHU-Hi series (HanChuan, HongHu, LongKou)
- **IEEE GRSS Data Fusion Contest** — Houston 2013, Houston 2018
- **University of Pavia** — Pavia Center, Pavia University
- **NASA MRO CRISM team** — NiliFossae, Utopia, Holden (Mars datasets)
- **DLR / HyMap** — Berlin, Augsburg
- **University of Southern Mississippi** — Muufl

---

## 📬 Contact

For questions, issues, or collaboration, feel free to open a discussion on the [HuggingFace dataset page](https://huggingface.co/datasets/Tanishq165/HSI_Datasets/discussions).