Feature extraction from multiple data sources using genetic programming

John J. Szymanski; Steven P. Brumby; Paul Pope; Damian Eads; Diana Esch-Mosher; Mark Galassi; Neal R. Harvey; Hersey D W McCulloch; Simon J. Perkins; Reid Porter; James Theiler; A. Cody Young; Jeffrey J. Bloch; Nancy David

doi:10.1117/12.478765

Feature extraction from multiple data sources using genetic programming

John J. Szymanski, Steven P. Brumby, Paul Pope, Damian Eads, Diana Esch-Mosher, Mark Galassi, Neal R. Harvey, Hersey D W McCulloch, Simon J. Perkins, Reid Porter, James Theiler, A. Cody Young, Jeffrey J. Bloch, Nancy David

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

10 Scopus citations

Abstract

Feature extraction from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. We use the GENetic Imagery Exploitation (Genie) software for this purpose, which produces image-processing software that inherently combines spatial and spectral processing. Genie is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. Genie then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land cover features including towns, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Original language	English (US)
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Editors	S.S. Shen, P.E. Lewis
Pages	338-345
Number of pages	8
Volume	4725
DOIs	https://doi.org/10.1117/12.478765
State	Published - 2002
Externally published	Yes
Event	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII - Orlando, FL, United States Duration: Apr 1 2002 → Apr 4 2002

Other

Other	Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII
Country/Territory	United States
City	Orlando, FL
Period	4/1/02 → 4/4/02

Keywords

Evolutionary computation
Feature extraction
Image processing
Multispectral analysis

ASJC Scopus subject areas

Electrical and Electronic Engineering
Condensed Matter Physics

Access to Document

10.1117/12.478765

Cite this

Szymanski, J. J., Brumby, S. P., Pope, P., Eads, D., Esch-Mosher, D., Galassi, M., Harvey, N. R., McCulloch, H. D. W., Perkins, S. J., Porter, R., Theiler, J., Young, A. C., Bloch, J. J., & David, N. (2002). Feature extraction from multiple data sources using genetic programming. In S. S. Shen, & P. E. Lewis (Eds.), Proceedings of SPIE - The International Society for Optical Engineering (Vol. 4725, pp. 338-345) https://doi.org/10.1117/12.478765

Szymanski, JJ, Brumby, SP, Pope, P, Eads, D, Esch-Mosher, D, Galassi, M, Harvey, NR, McCulloch, HDW, Perkins, SJ, Porter, R, Theiler, J, Young, AC, Bloch, JJ & David, N 2002, Feature extraction from multiple data sources using genetic programming. in SS Shen & PE Lewis (eds), Proceedings of SPIE - The International Society for Optical Engineering. vol. 4725, pp. 338-345, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, Orlando, FL, United States, 4/1/02. https://doi.org/10.1117/12.478765

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abstract = "Feature extraction from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. We use the GENetic Imagery Exploitation (Genie) software for this purpose, which produces image-processing software that inherently combines spatial and spectral processing. Genie is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. Genie then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land cover features including towns, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.",

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author = "Szymanski, {John J.} and Brumby, {Steven P.} and Paul Pope and Damian Eads and Diana Esch-Mosher and Mark Galassi and Harvey, {Neal R.} and McCulloch, {Hersey D W} and Perkins, {Simon J.} and Reid Porter and James Theiler and Young, {A. Cody} and Bloch, {Jeffrey J.} and Nancy David",

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AU - Harvey, Neal R.

AU - McCulloch, Hersey D W

AU - Perkins, Simon J.

AU - Porter, Reid

AU - Theiler, James

AU - Young, A. Cody

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AB - Feature extraction from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. We use the GENetic Imagery Exploitation (Genie) software for this purpose, which produces image-processing software that inherently combines spatial and spectral processing. Genie is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. Genie then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land cover features including towns, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

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Feature extraction from multiple data sources using genetic programming

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Keywords

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