Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

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

Research output: Contribution to journalArticle

95 Scopus citations

Abstract

We have developed an automated feature detection/classification system, called GENetic Imagery Exploitation a (GENIE), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. GENiE is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multispectral remotely-sensed images. We describe our system in detail together with experiments involving comparisons of GENIE with several conventional supervised classification techniques, for a number of classification tasks using multispectral remotely sensed imagery.

Original languageEnglish (US)
Pages (from-to)393-404
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume40
Issue number2
DOIs
StatePublished - Feb 2002

Keywords

  • Evolutionary algorithms
  • Genetic programming
  • Image processing
  • Multispectral imagery
  • Remote sensing
  • Supervised classification

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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    Harvey, N. R., Theiler, J., Brumby, S. P., Perkins, S., Szymanski, J. J., Bloch, J. J., Porter, R. B., Galassi, M., & Young, A. C. (2002). Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 40(2), 393-404. https://doi.org/10.1109/36.992801