Direct Analysis of Hyperspectral Images (DAHi) ID: 2016-044
A revolutionary approach to hyperspectral image analysis using information content as a summary statistic.
Photo by starlineart - stock.adobe.com
Technology Overview
DAHi represents a significant leap in hyperspectral image analysis by employing Shannon's entropy to simplify the data from a three-dimensional hyperspectral data cube into a two-dimensional map, effectively retaining all chemical information. This method calculates an information content (IC) value for each spectrum, allowing for a more accurate inference of chemical variations.
Key Advantages
- Reduces data complexity without losing chemical information
- Employs a single characteristic number to process data, enhancing efficiency
- Overcomes limitations of traditional methods like PCA and MCR
- Enables more accurate identification of different material compositions
Problems Addressed
- Complexity and volume of data in hyperspectral images
- Limitations of traditional analysis methods in handling large datasets
- Difficulty in accurately identifying material compositions from hyperspectral data
Market Applications
- Enhanced analysis in instruments like EDS, TOF-SIMS, and RAMAN
- Material distribution analysis at micro to nanoscale resolutions
- Potential applications in fields requiring detailed chemical imaging
Additional Information
Technology ID: 2022-002
Sell Sheet: Download the Sell Sheet here
Market Analysis: Contact us for a more in-depth market report
Date Published: 28 March, 2025
Connect with the Tech Transfer to:
- Meet with the technology manager
- Receive additional information
- Request a marketing plan report