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Proceedings Paper

Spectral gating in hyperspectral-augmented target tracking
Author(s): Neil A. Soliman; Michael J. Mendenhall; Juan R. Vasquez
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Paper Abstract

Hyperspectral images provide scientists and engineers with the capability of precise material identification in remote sensing applications. One can leverage this data for precise track identification (ID) and incorporate the high-confidence ID in the tracking process. Our previous work demonstrates that hyperspectral-aided tracking outperforms kinematic-only tracking where multiple ambiguous situations exist. We develop a novel gating concept for hyperspectral measurements, similar in concept to the gating of the Mahalanobis distance computed from the Kalman residuals. Our spectral gating definition is based on the distance between the spectral distribution of the class ID of a track and the spectral distribution of the class ID resulting from the classification of a measurement. We further incorporate the distance between each class distribution (in spectral space) in the track association portion of our hyperspectral-aided tracker. Since functional forms of the joint probability distribution function do not exist, similarity measures such as the Kullback-Leibler divergence or Bhattacharyya distance cannot be used. Instead, we compute all pair-wise distances between all samples of the two classes and then summarize these distances in a meaningful way. This article presents our novel spectral gating approach and its use in track association. It further explores different similarity measures and their effect on spectral gating and track association.

Paper Details

Date Published: 16 April 2008
PDF: 12 pages
Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 696907 (16 April 2008); doi: 10.1117/12.777590
Show Author Affiliations
Neil A. Soliman, Air Force Institute of Technology (United States)
Michael J. Mendenhall, Air Force Institute of Technology (United States)
Juan R. Vasquez, Numerica Corp. (United States)

Published in SPIE Proceedings Vol. 6969:
Signal and Data Processing of Small Targets 2008
Oliver E. Drummond, Editor(s)

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