DocumentCode :
1266165
Title :
Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction
Author :
Harvey, Neal R. ; Theiler, James ; Brumby, Steven P. ; Perkins, Simon ; Szymanski, John J. ; Bloch, Jeffrey J. ; Porter, Reid B. ; Galassi, Mark ; Young, A. Cody
Author_Institution :
Los Alamos Nat. Lab., NM, USA
Volume :
40
Issue :
2
fYear :
2002
fDate :
2/1/2002 12:00:00 AM
Firstpage :
393
Lastpage :
404
Abstract :
The authors have developed an automated feature detection/classification system, called GENetic Imagery Exploitation (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. The authors describe their 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 :
feature extraction; genetic algorithms; geophysical signal processing; geophysical techniques; geophysics computing; image classification; multidimensional signal processing; terrain mapping; GENIE; GENetic Imagery Exploitation; IR; feature extraction; geophysical measurement technique; hybrid evolutionary algorithm; image classification; image processing; infrared; land surface; multispectral remote sensing; supervised classifier; terrain mapping; visible; Computer vision; Feature extraction; Genetic programming; Image generation; Image processing; Image segmentation; Multispectral imaging; Pipelines; Remote sensing; Supervised learning;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/36.992801
Filename :
992801
Link To Document :
بازگشت