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Proceedings Paper

Parallel evolution of image processing tools for multispectral imagery
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Paper Abstract

We describe the implementation and performance of a parallel, hybrid evolutionary-algorithm-based system, which optimizes image processing tools for feature-finding tasks in multi-spectral imagery (MSI) data sets. Our system uses an integrated spatio-spectral approach and is capable of combining suitably-registered data from different sensors. We investigate the speed-up obtained by parallelization of the evolutionary process via multiple processors (a workstation cluster) and develop a model for prediction of run-times for different numbers of processors. We demonstrate our system on Landsat Thematic Mapper MSI , covering the recent Cerro Grande fire at Los Alamos, NM, USA.

Paper Details

Date Published: 15 November 2000
PDF: 11 pages
Proc. SPIE 4132, Imaging Spectrometry VI, (15 November 2000); doi: 10.1117/12.406611
Show Author Affiliations
Neal R. Harvey, Los Alamos National Lab. (United States)
Steven P. Brumby, Los Alamos National Lab. (United States)
Simon J. Perkins, Los Alamos National Lab. (United States)
Reid B. Porter, Los Alamos National Lab. (United States)
James P. Theiler, Los Alamos National Lab. (United States)
Aaron Cody Young, Los Alamos National Lab. (United States)
John J. Szymanski, Los Alamos National Lab. (United States)
Jeffrey J. Bloch, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 4132:
Imaging Spectrometry VI
Michael R. Descour; Sylvia S. Shen, Editor(s)

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