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Proceedings Paper

Persistent hyperspectral adaptive multi-modal feature-aided tracking
Author(s): Andrew C. Rice; Juan R. Vasquez; John Kerekes; Michael J. Mendenhall
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Paper Abstract

An architecture and implementation is presented regarding persistent, hyperspectral, adaptive, multi-modal, feature-aided tracking within the urban context. A novel remote-sensing imager has been designed which employs a micro-mirror array at the focal plane for per-pixel adaptation. A suite of end-to-end synthetic experiments have been conducted, which include high-fidelity moving-target urban vignettes, DIRSIG hyperspectral rendering, and full image-chain treatment of the prototype adaptive sensor. Corresponding algorithm development has focused on: motion segmentation, spectral feature modeling, classification, fused kinematic/spectral association, and adaptive sensor feedback/control.

Paper Details

Date Published: 27 April 2009
PDF: 12 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340M (27 April 2009); doi: 10.1117/12.818913
Show Author Affiliations
Andrew C. Rice, Numerica Corp. (United States)
Juan R. Vasquez, Numerica Corp. (United States)
John Kerekes, Rochester Institute of Technology (United States)
Michael J. Mendenhall, Numerica Corp. (United States)

Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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