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Proceedings Paper

Evolving a spatio-spectral network on reconfigurable computers for multispectral feature identification
Author(s): Reid B. Porter; Maya Gokhale; Neal R. Harvey; Simon J. Perkins; Aaron Cody Young
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Paper Abstract

Feature identification attempts to find algorithms that can consistently separate a feature of interest from the background in the presence of noise and uncertain conditions. This paper describes the development of a high-throughput, reconfigurable computer based, feature identification system known as POOKA. POOKA is based on a novel spatio-spectral network, which can be optimized with an evolutionary algorithm on a problem-by-problem basis. The reconfigurable computer provides speed up in two places: 1) in the training environment to accelerate the computationally intensive search for new feature identification algorithms, and 2) in the application of trained networks to accelerate content based search in large multi-spectral image databases. The network is applied to several broad area features relevant to scene classification. The results are compared to those found with traditional remote sensing techniques as well as an advanced software system known as GENIE. The hardware efficiency and performance gains compared to software are also reported.

Paper Details

Date Published: 17 January 2002
PDF: 12 pages
Proc. SPIE 4480, Imaging Spectrometry VII, (17 January 2002); doi: 10.1117/12.453330
Show Author Affiliations
Reid B. Porter, Los Alamos National Lab. (United States)
Maya Gokhale, Los Alamos National Lab. (United States)
Neal R. Harvey, Los Alamos National Lab. (United States)
Simon J. Perkins, Los Alamos National Lab. (United States)
Aaron Cody Young, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 4480:
Imaging Spectrometry VII
Michael R. Descour; Sylvia S. Shen, Editor(s)

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