15 November 2000 Parallel evolution of image processing tools for multispectral imagery
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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.
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Neal R. Harvey, Steven P. Brumby, Simon J. Perkins, Reid B. Porter, James P. Theiler, Aaron Cody Young, John J. Szymanski, Jeffrey J. Bloch, "Parallel evolution of image processing tools for multispectral imagery", Proc. SPIE 4132, Imaging Spectrometry VI, (15 November 2000); doi: 10.1117/12.406611; https://doi.org/10.1117/12.406611

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