Genetic programming approach to extracting features from remotely sensed imagery

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Description

Multi-instrument data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problems of spatial co-registration, the remote sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. We describe a genetic programming/supervised classifier software system, called Genie, which evolves and combines spatio-spectral image processing tools for remotely sensed imagery. We describe our representation of candidate image processing pipelines, and discuss our set of primitive image operators. Our primary application has been in the field of geospatial feature extraction, including wildfire scars and ... continued below

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8 p.

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Theiler, J. P. (James P.); Perkins, S. J. (Simon J.); Harvey, N. R. (Neal R.); Szymanski, J. J. (John J.) & Brumby, Steven P. January 1, 2001.

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Description

Multi-instrument data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problems of spatial co-registration, the remote sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. We describe a genetic programming/supervised classifier software system, called Genie, which evolves and combines spatio-spectral image processing tools for remotely sensed imagery. We describe our representation of candidate image processing pipelines, and discuss our set of primitive image operators. Our primary application has been in the field of geospatial feature extraction, including wildfire scars and general land-cover classes, using publicly available multi-spectral imagery (MSI) and hyper-spectral imagery (HSI). Here, we demonstrate our system on Landsat 7 Enhanced Thematic Mapper (ETM+) MSI. We exhibit an evolved pipeline, and discuss its operation and performance.

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8 p.

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  • "Submitted to: Fusion 2001, 4th International Conference on Information Fusion Montreal, Canada, Aug. 7-10"

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  • Report No.: LA-UR-01-2787
  • Grant Number: none
  • Office of Scientific & Technical Information Report Number: 975334
  • Archival Resource Key: ark:/67531/metadc930345

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • January 1, 2001

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 9, 2016, 11:37 p.m.

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Theiler, J. P. (James P.); Perkins, S. J. (Simon J.); Harvey, N. R. (Neal R.); Szymanski, J. J. (John J.) & Brumby, Steven P. Genetic programming approach to extracting features from remotely sensed imagery, article, January 1, 2001; United States. (digital.library.unt.edu/ark:/67531/metadc930345/: accessed December 11, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.