Topological Data Analysis and Wide Area Detection of Chemical and Biological Contamination MP 39-10

Period of Performance: 09/27/2010 - 03/31/2011

$100K

Phase 1 STTR

Recipient Firm

Metron, Inc.
1818 Library Street Suite 600
Reston, VA 20190
Firm POC
Principal Investigator

Research Institution

Stanford University
3160 Porter Drive, Suite 100
Palo Alto, CA 94304
Institution POC

Abstract

Metron, Inc. and Stanford University propose to design, develop, test and demonstrate topological data analytic algorithms to analyze hyperspectral imagery. We propose to adapt the topological data analytic techniques, including Stanford s successful Mapper algorithm, to the hyperspectral imagery domain. Using these algorithms we will identify topological and geometric features and properties indicative of chemical and biological contamination. We will leverage Metron s expertise in Bayesian filters to produce preliminary detection and classification results using the extracted features and properties.