Hyperspectral Identification for Collaborative Tracking

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$100,000.00
Award Year:
2006
Program:
SBIR
Phase:
Phase I
Contract:
FA8650-06-M-1060
Agency Tracking Number:
F061-218-3046
Solicitation Year:
2006
Solicitation Topic Code:
AF06-218
Solicitation Number:
2006.1
Small Business Information
GITAM TECHNOLOGIES INC
9782 Country Creek Way, Dayton, OH, 45458
Hubzone Owned:
N
Socially and Economically Disadvantaged:
Y
Woman Owned:
N
Duns:
134123475
Principal Investigator:
William McCormick
Senior Research Scientist
(937) 439-0345
william.mccormick@wright.edu
Business Contact:
Arnab Shaw
President and Technical Lead
(937) 885-9767
gitam13@yahoo.com
Research Institution:
n/a
Abstract
Gitam Technologies Inc. (GTI) proposes to develop novel HSI/MSI algorithms for detection, recognition and tracking of dismounts, vehicles and other man-made objects (Fortifications, Ordnance, weapons, IEDs, etc.). Preliminary work on dismount/vehicle detection using PCA with VIS-HSI data indicated improved performance when compared to traditional EO processing. In this work, we will extend the work to incorporate NIR-HSI bands. For the dismount case, a major focus will be in the phenomenology analysis, and exploitation of NIR band facial/skin data for face recognition, as well as for dismount tracking in darkness using NIR-HSI sensors. Preliminary results on moving target tracking using change detection over successive HSI bands have been promising. In this project, we shall consider Active Contour Tracking of dismounts and vehicles using Level Sets where the contours are represented by energy-minimizing splines known as Snakes. Genetic algorithm will be used to harness large quantity of high-quality spatio-spectral HSI data for automated feature extraction. In addition to traditional HSI algorithms, such as PCA, LDA and ICA, we will also study the feasibility of a new information theoretical Spectral Information Measure algorithm, which is specially suited for exploiting spectral variability, similarity, and discrimination from hyperspectral images.

* information listed above is at the time of submission.

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