Hyperspectral Images for Material Classification

This dataset is a rich resource for material classification using hyperspectral imaging, specifically designed for applications in manufacturing, robotics, AR/VR, and more. It includes multi-spectral data collected using a ChromaFlash LED device and a PiCamera 3, capturing the spectral responses of 10 material samples under varied lighting and angles.

Key Features

✅ Diverse Material Samples: 10 samples across 5 categories:

• Plastic: White, Blue
• Paper: White, Yellow
• Cloth: White, Turquoise
• Cardboard: White, Brown
• Ceramic: White, Green

✅ Advanced Imaging Setup:

• Device: ChromaFlash LED with 11 spectral bands (380–780nm)
• Camera: PiCamera 3 (640×480 resolution, fixed exposure of 10,000 μs, ISO ~87)
• Controlled parameters: Disabled auto white balance and noise reduction for accurate data capture.

✅ Multiple Angles for Robust Data:

• Five positions: Perpendicular, +30°/-30° horizontal, +30°/-30° vertical angles, at a distance of 1.5 feet.

✅ Sophisticated Data Processing:

• Black correction and bilinear demosaicing for clarity.
• Object isolation and global normalization to ensure consistency.
• Feature extraction: 20×20 patches with 5×5 overlap for detailed analysis.

Performance Metrics

• Colored Materials: 86% classification accuracy.
• White Materials: 75% classification accuracy.

Applications

• Quality Control in Manufacturing: Detect and classify materials efficiently.
• Medical Devices: Enhance material recognition for health technologies.
• AR/VR Texture Rendering: Build lifelike simulations with accurate material data.
• Robotics: Improve material recognition for autonomous systems.