Hyperspectral Identification for Collaborative Tracking

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$750,000.00
Award Year:
2008
Program:
SBIR
Phase:
Phase II
Contract:
FA8650-08-C-1320
Agency Tracking Number:
F061-218-3046
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
GITAM TECHNOLOGIES, INC.
9782 Country Creek Way, Dayton, OH, 45458
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
134123475
Principal Investigator:
William McCormick
Senior Scientist
(937) 885-9767
william.mccormick@wright.edu
Business Contact:
Arnab Shaw
President and Tech Lead
(937) 885-9767
gitam13@yahoo.com
Research Institution:
n/a
Abstract
The primary goal of this effort is to develop advanced Hyperspectral Image (HSI) data and algorithms for early detection of plant degradation due to Chemical/Biological agents. During Phase I, proof-of-concept demonstrated that HSI algorithms are capable of detecting de-greening in arabidopsis plants infused with covert de-greening circuits. In Phase II, the major objectives are: (1) Extend genetic engineering towards more operational viability, i.e., subject larger and mature plants to a wider range of chemical and biological agents, (2) Develop advanced Detection/Classification algorithms: multiple-hypothesis detect/ID for multiple plant specimens affected by different chem-bio agents, signature-based temporal change/anomaly detection, kernelization of linear algorithms to account for nonlinearities, ICA-based unmixing of HSI data, genetic algorithm for automated feature extraction, (3) HSI aided Triage resource management for distinguishing live and deceased dismounts in urban calamity region using HSI thermal-IR bands, and (4) Generation of and experimentation with synthetic remote sensing data and analytical prediction models: Incorporate healthy/de-greened plant and human skin spectral reflectance/emissivity signatures within FASSP/DIRSIG modeling environment, add atmospheric/illumination/sensor effects to generate synthetic electro-optical imagery that an airborne sensor might observe from a distance, apply appropriate detect/ID algorithms on the synthetic images, and perform model based sensitivity analysis to explore detection bounds.

* information listed above is at the time of submission.

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