Diffusion Geometry Based Nonlinear Methods for Hyperspectral Change Detection

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9550-09-C-0189
Agency Tracking Number: F08B-T24-0189
Amount: $99,936.00
Phase: Phase I
Program: STTR
Awards Year: 2009
Solicitation Year: 2008
Solicitation Topic Code: AF08-BT24
Solicitation Number: 2008.B
Small Business Information
Plain Sight Systems Inc
19 Whitney Avenue, New Haven, CT, 06510
DUNS: 030077908
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Andreas Coppi
 Vice President
 (203) 285-8617
 coppi@plainsight.com
Business Contact
 Andreas Coppi
Title: Vice President
Phone: (203) 285-8617
Email: coppi@plainsight.com
Research Institution
 Yale University
 Ella Sandor
 Department of Mathematics
PO box 208283
New Haven, CT, 06520-
 (203) 432-4180
 Nonprofit college or university
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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