LujianYao/HSSDataset

Hyperspectral and Multispectral Smoke Segmentation Datasets

Overview

Smoke segmentation is critical for wildfire management and industrial safety applications. Traditional visible-light-based methods face limitations due to insufficient spectral information, particularly struggling with cloud interference and semi-transparent smoke regions.

Figure 1: Motivation for hyperspectral smoke segmentation. The upper part shows challenging smoke scenarios with cloud interference and semi-transparent regions in the visible light band. The lower part plots the spectral distribution of marked points, where yellow shaded regions highlight key discriminatory band ranges.

To address these challenges, we introduce:

1. HSSDataset: The first hyperspectral smoke segmentation dataset with 25 spectral bands
2. MSSDataset: A multispectral dataset with RGB-infrared paired images

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HSSDataset: Hyperspectral Smoke Segmentation Dataset

Dataset Overview

HSSDataset is the first hyperspectral smoke segmentation dataset, built from an extensive collection of over 18,000 hyperspectral video frames captured across 20 real-world smoke scenarios. From this large-scale raw data collection, we carefully selected and annotated 1,007 high-quality samples under diverse challenging conditions.

Hyperspectral Camera System

Figure 2: XIMEA MQ022HG-IM-SM5X5-NIR hyperspectral camera specifications and 25-band mosaic filter design.

We employ a XIMEA MQ022HG-IM-SM5X5-NIR hyperspectral camera with the following specifications:

• Spectral Bands: 25 bands spanning 600-974nm
• Wavelengths (nm): 600, 616, 632, 647, 664, 680, 696, 712, 728, 744, 760, 776, 792, 808, 824, 840, 856, 872, 888, 894, 910, 926, 942, 958, 974
• Filter Design: Specialized 5×5 mosaic filter array deployed on the sensor surface
• Technology: Frame-style hyperspectral acquisition based on mosaic coating

Key Advantages:

• Simultaneous balance of spectral and spatial resolution
• Rapid acquisition of both spectral and spatial information
• High integration density
• Ability to capture unique "spectral fingerprints" of smoke

Data Collection

Figure 3: The challenging scenarios of HSSDataset.

Data collection was conducted across 20 real-world industrial emission scenarios, capturing over 18,000 hyperspectral video frames. The collection targeted diverse smoke conditions, including:

Scene-based Challenging Scenarios:

• High Exposure Environments: Bright lighting conditions with overexposed regions (214 samples)
• Low Visibility Conditions: Poor lighting and atmospheric conditions (118 samples)
• Complex Backgrounds: Industrial environments with cluttered backgrounds (411 samples)
• Cloud Interference: Scenes with cloud-smoke confusion scenarios (264 samples)

Smoke-based Challenging Scenarios:

• Early-stage Minimal Smoke: Small smoke plumes in initial emission phases (268 samples)
• Semi-transparent Regions: Varying smoke opacity and transparency (258 samples)
• Complex-shaped Smoke: Irregular smoke patterns with unclear boundaries (481 samples)

Annotation Protocol: Many-to-One Annotations

Figure 4: Many-to-One annotations for hyperspectral smoke segmentation.

To ensure annotation reliability and capture the inherent uncertainty in smoke boundary delineation, we employ a rigorous Many-to-One annotations protocol:

Process:

1. Sampling Strategy: Systematically sample every 18th frame from the 18,000+ collected frames
2. Band-Averaged Image Generation: Generate grayscale images by computing arithmetic mean across all 25 spectral bands
3. Multiple Annotators: Each frame receives three independent ground truth masks from three different expert annotators
4. Ground Truth Definition: Final masks generated through majority voting - each pixel classified as smoke if at least two-thirds of annotators label it as smoke

Inter-Annotator Agreement:

• Unanimous agreement (3/3): 52.07% of annotated smoke pixels
• Majority agreement (2/3): 14.14% of annotated smoke pixels
• Single annotator (1/3): 33.79% (excluded from final ground truth)

Quality Control:

Special emphasis placed on challenging regions including:

• Early-stage minimal smoke
• Semi-transparent regions
• Blurred boundaries
• Cloud interference areas

Dataset Structure

HSS_VOC/
├── npy_multichannel/          # Hyperspectral data (25 bands)
│   ├── 10_frame_0001.npy
│   ├── 10_frame_0019.npy
│   └── ...
├── Mask/                      # Final ground truth masks 
│   ├── 10_frame_0001.png      # Majority voting results
│   ├── 10_frame_0019.png
│   └── ...
├── Annotation_1/              # Annotator 1's masks 
│   └── *.png
├── Annotation_2/              # Annotator 2's masks 
│   └── *.png
├── Annotation_3/              # Annotator 3's masks 
│   └── *.png
└── split/                     # Dataset split files
    ├── train.txt              # Training set sample list
    ├── val.txt                # Validation set sample list
    └── test.txt               # Test set sample list

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MSSDataset: Multispectral Smoke Segmentation Dataset

Dataset Overview

Figure 5: Visible and infrared frame pairs in MSSDataset.

To validate the generalizability of our method beyond hyperspectral data, we constructed a multispectral smoke segmentation dataset (MSSDataset) derived from the FLAME2 dataset.

Dataset Specifications

• Source: FLAME2 dataset
• Spectral Channels: 4 channels (RGB + Infrared)
• Annotated Samples: 200 carefully selected samples
• Scenarios: Wildland fire smoke under various environmental conditions
• Format: RGB-IR paired multispectral cubes

Key Features

• Enhanced infrared visualization for better smoke detection
• Diverse wildfire scenarios
• Distinct smoke features suitable for annotation
• Complementary to HSSDataset for cross-modality validation

Dataset Structure

FLAME2_VOC/
├── npy_multichannel/          # Multispectral data (4 channels: RGB+IR)
│   └── *.npy
├── Mask/                      # Ground truth masks
│   └── *.png
└── split/                     # Dataset split files
    ├── train.txt              # Training set sample list 
    ├── val.txt                # Validation set sample list 
    └── test.txt               # Test set sample list

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Data Format

Hyperspectral Data (HSSDataset)

Each .npy file contains a hyperspectral image cube:

• Shape: (H, W, 25) where H and W are spatial dimensions
• Data Type: float32 or uint16
• Channels: 25 spectral bands (600-974nm)
• Organization: Channels ordered by wavelength

Example loading code:

import numpy as np

# Load hyperspectral data
hsi_data = np.load('10_frame_0001.npy')  # Shape: (H, W, 25)

# Access specific spectral band
band_600nm = hsi_data[:, :, 0]  # First band (600nm)
band_974nm = hsi_data[:, :, 24]  # Last band (974nm)

# Generate band-averaged image (for visualization)
band_avg = np.mean(hsi_data, axis=2)

Multispectral Data (MSSDataset)

Each .npy file contains a multispectral image:

• Shape: (H, W, 4) where H and W are spatial dimensions
• Data Type: float32
• Channels: 4 channels (RGB + IR)
• Channel Order: [R, G, B, IR]

Example loading code:

import numpy as np

# Load multispectral data
msi_data = np.load('sample_001.npy')  # Shape: (H, W, 4)

# Access RGB channels
rgb = msi_data[:, :, :3]

# Access infrared channel
ir = msi_data[:, :, 3]

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📝 Citation

If you use these datasets in your research, please cite:

@article{yao2026hyperspectral,
  title={Hyperspectral Smoke Segmentation via Mixture of Prototypes},
  author={Yao, Lujian and Zhao, Haitao and Kong, Xianghai and Xu, Yuhan}
  year={2026},
  journal={arXiv preprint arXiv:2602.10858},
}

Dataset on Hugging Face: https://huggingface.co/datasets/LujianYao/HSSDataset

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License

This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).For more details, see the full license at: https://creativecommons.org/licenses/by-nc-sa/4.0/

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Acknowledgments

• FLAME2 dataset authors for providing the source data for MSSDataset