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

94 Citations (Scopus)

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
Externally publishedYes

Fingerprint

multispectral image
exploitation
classifiers
pattern recognition
imagery
Feature extraction
Classifiers
image classification
image processing
Evolutionary algorithms
Image processing
Pipelines
comparison
experiment
detection
Experiments

Keywords

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

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics
  • Computers in Earth Sciences
  • Electrical and Electronic Engineering

Cite this

Harvey, N. R., Theiler, J., Brumby, S. P., Perkins, S., Szymanski, J. J., Bloch, J. J., ... 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

Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction. / Harvey, Neal R.; Theiler, James; Brumby, Steven P.; Perkins, Simon; Szymanski, John J.; Bloch, Jeffrey J.; Porter, Reid B.; Galassi, Mark; Young, A. Cody.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 2, 02.2002, p. 393-404.

Research output: Contribution to journalArticle

Harvey, NR, Theiler, J, Brumby, SP, Perkins, S, Szymanski, JJ, Bloch, JJ, Porter, RB, Galassi, M & Young, AC 2002, 'Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction', IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 2, pp. 393-404. https://doi.org/10.1109/36.992801
Harvey, Neal R. ; Theiler, James ; Brumby, Steven P. ; Perkins, Simon ; Szymanski, John J. ; Bloch, Jeffrey J. ; Porter, Reid B. ; Galassi, Mark ; Young, A. Cody. / Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction. In: IEEE Transactions on Geoscience and Remote Sensing. 2002 ; Vol. 40, No. 2. pp. 393-404.
@article{0b46ce43f84f4abd99fa94d1a2ad558c,
title = "Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction",
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.",
keywords = "Evolutionary algorithms, Genetic programming, Image processing, Multispectral imagery, Remote sensing, Supervised classification",
author = "Harvey, {Neal R.} and James Theiler and Brumby, {Steven P.} and Simon Perkins and Szymanski, {John J.} and Bloch, {Jeffrey J.} and Porter, {Reid B.} and Mark Galassi and Young, {A. Cody}",
year = "2002",
month = "2",
doi = "10.1109/36.992801",
language = "English (US)",
volume = "40",
pages = "393--404",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

TY - JOUR

T1 - Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

AU - Harvey, Neal R.

AU - Theiler, James

AU - Brumby, Steven P.

AU - Perkins, Simon

AU - Szymanski, John J.

AU - Bloch, Jeffrey J.

AU - Porter, Reid B.

AU - Galassi, Mark

AU - Young, A. Cody

PY - 2002/2

Y1 - 2002/2

N2 - 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.

AB - 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.

KW - Evolutionary algorithms

KW - Genetic programming

KW - Image processing

KW - Multispectral imagery

KW - Remote sensing

KW - Supervised classification

UR - http://www.scopus.com/inward/record.url?scp=0036477310&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036477310&partnerID=8YFLogxK

U2 - 10.1109/36.992801

DO - 10.1109/36.992801

M3 - Article

AN - SCOPUS:0036477310

VL - 40

SP - 393

EP - 404

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 2

ER -