Metaspectral provides real-time hyperspectral data analysis through its Fusion platform, utilizing deep learning techniques to extract sub-pixel level information from any hyperspectral sensor. This technology enables users to gain actionable insights from ultra-high-resolution imaging while significantly reducing data labeling efforts and processing time.
Funding
Funding not disclosed
BCFounders
Product
Problem
Analyzing hyperspectral data to extract meaningful insights is computationally intensive and requires specialized expertise in both hyperspectral imaging and machine learning. Traditional methods struggle to process the vast amounts of data generated by hyperspectral sensors in real-time, hindering timely decision-making. Furthermore, labeling hyperspectral data for training AI models is a time-consuming and expensive process.
Solution
Metaspectral's Fusion platform provides real-time hyperspectral data analysis using deep learning techniques, enabling users to extract sub-pixel level information from any hyperspectral sensor. The platform employs novel data compression techniques to handle terabytes of data and purpose-built neural network architectures to extract detailed spectral and spatial information. By reducing the data labeling effort by up to 10x, Fusion accelerates the development and deployment of hyperspectral AI models. The platform supports real-time streaming and inference, allowing users to gain actionable insights from ultra-high-resolution imaging without complex programming or AI experience.
Target Audience
The primary target audience includes organizations in recycling, space exploration, agriculture, and defense that require real-time analysis of hyperspectral data for tasks such as object detection, classification, and anomaly detection.
Features
- Real-time hyperspectral data processing and analysis
- Deep learning models for sub-pixel level information extraction
- Compatible with any hyperspectral camera and sensor platform
- Up to 95% data compression for efficient streaming and storage
- Reduced data labeling requirements (up to 10x less)
- Spatial and spectral analysis with deep learning
- Cloud-based platform for data labeling, model training, and deployment
- MLOps agents for continuous AI model improvement
- Hardware-accelerated AI for real-time inference at the edge or in the cloud