Automated Feature Extraction from Hyperspectral Imagery

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNC06CB48C
Agency Tracking Number: 054506
Amount: $70,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2006
Solitcitation Year: 2005
Solitcitation Topic Code: S7.01
Solitcitation Number: N/A
Small Business Information
Visual Learning Systems, Inc.
1280 S. 3rd Street West, #2, Missoula, MT, 59801-2391
Duns: 150373442
Hubzone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Stuart Blundell
 Principal Investigator
 (406) 829-1384
 sblundell@vls-inc.com
Business Contact
 David Opitz
Title: Business Official
Phone: (406) 829-1384
Email: opitz@vls-inc.com
Research Institution
N/A
Abstract
In response to NASA Topic S7.01, Visual Learning Systems, Inc. (VLS) will develop a novel hyperspectral plug-in toolkit for its award winning Feature AnalystREG software that will (a) leverage VLS' proven algorithms to provide a new, simple, and long-awaited approach to materials classification from hyperspectral imagery (HSI), and (b) improve state-of-the-art Feature Analyst's automated feature extraction (AFE) capabilities by effectively incorporating detailed spectral information into its extraction process. HSI techniques, such as spectral end-member classification, can provide effective materials classification; however, current methods are slow (or manual), cumbersome, complex for analysts, and are limited to materials classification only. Feature Analyst, on the other hand has a simple workflow of (a) an analyst providing a few examples (e.g., pixels of a certain material) and (b) an advanced software agent classifying the rest of the imagery based on the examples. This simple yet powerful approach will be used as a new paradigm for materials classification. In addition, Feature Analyst uses, along with spectral information, feature characteristics such as spatial association, size, shape, texture, pattern, and shadow in its generic AFE process. Incorporating the best spectral classifier techniques with the best AFE approach promises to greatly increase the usefulness and applicability of HSI

* information listed above is at the time of submission.

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