Parallel evolution of image processing tools for multispectral imagery

Neal R. Harvey; Steven P. Brumby; Simon J. Perkins; Reid B. Porter; James Theiler; A. Cody Young; John J. Szymanski; Jeffrey J. Bloch

doi:10.1117/12.406611

Parallel evolution of image processing tools for multispectral imagery

Neal R. Harvey, Steven P. Brumby, Simon J. Perkins, Reid B. Porter, James Theiler, A. Cody Young, John J. Szymanski, Jeffrey J. Bloch

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Scopus citations

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.

Original language	English (US)
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Publisher	Society of Photo-Optical Instrumentation Engineers
Pages	72-82
Number of pages	11
Volume	4132
DOIs	https://doi.org/10.1117/12.406611
State	Published - 2000
Externally published	Yes
Event	Imaging Spectrometry VI - San Diego, USA Duration: Jul 31 2000 → Aug 2 2000

Other

Other	Imaging Spectrometry VI
City	San Diego, USA
Period	7/31/00 → 8/2/00

ASJC Scopus subject areas

Electrical and Electronic Engineering
Condensed Matter Physics

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10.1117/12.406611

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Harvey, N. R., Brumby, S. P., Perkins, S. J., Porter, R. B., Theiler, J., Young, A. C., Szymanski, J. J., & Bloch, J. J. (2000). Parallel evolution of image processing tools for multispectral imagery. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 4132, pp. 72-82). Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.406611

Parallel evolution of image processing tools for multispectral imagery. / Harvey, Neal R.; Brumby, Steven P.; Perkins, Simon J. et al.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 4132 Society of Photo-Optical Instrumentation Engineers, 2000. p. 72-82.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Harvey, NR, Brumby, SP, Perkins, SJ, Porter, RB, Theiler, J, Young, AC, Szymanski, JJ & Bloch, JJ 2000, Parallel evolution of image processing tools for multispectral imagery. in Proceedings of SPIE - The International Society for Optical Engineering. vol. 4132, Society of Photo-Optical Instrumentation Engineers, pp. 72-82, Imaging Spectrometry VI, San Diego, USA, 7/31/00. https://doi.org/10.1117/12.406611

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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.",

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AB - 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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Parallel evolution of image processing tools for multispectral imagery

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