Diffusion Geometry Based Nonlinear Methods for Hyperspectral Change Detection

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$749,273.00
Award Year:
2010
Program:
STTR
Phase:
Phase II
Contract:
FA9550-10-C-0134
Award Id:
90233
Agency Tracking Number:
F08B-T24-0189
Solicitation Year:
2008
Solicitation Topic Code:
AF08-BT24
Solicitation Number:
2008.B
Small Business Information
19 Whitney Avenue, New Haven, CT, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
030077908
Principal Investigator:
AndreasCoppi
Vice President
(203) 285-8617
coppi@plainsight.com
Business Contact:
AndreasCoppi
Vice President
(203) 285-8617
coppi@plainsight.com
Research Institute:
Yale University
Ella Sandor
Department of Mathematics
PO box 208283
New Haven, CT, 06520-
(203) 432-4180

Abstract
We propose a suite of nonlinear image processing algorithms derived from the analysis of the underlying diffusion geometry of a collection of hyperspectral images of interest. These tools enable the comparison of spatio-spectral features of hyperspectral images acquired under different conditions, for the purposes of target detection, change detection and anomaly assessment. This methodology also automatically extracts independent components of the spectrum and builds an empirical model of the constituents of the scene. It is precisely through this model that the most efficient target search and change detection can be performed. We will integrate these tools into an existing hyperspectral image toolbox, and validate the methods on Air Force data as well as that from our proprietary hyperspectral acquisition hardware. BENEFIT: The eventual development of a commercial suite of efficient hyperspectral image processing algorithms deployable in on- and off-line applications including image acquisition systems

* information listed above is at the time of submission.

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