U.S. Department of EnergyOffice of Scientific and Technical Information
GENIE: A HYBRID GENETIC ALGORITHM FOR FEATURE CLASSIFICATION IN MULTI-SPECTRAL IMAGES
Abstract
We consider the problem of pixel-by-pixel classification of a multi-spectral image using supervised learning. Conventional supervised classification techniques such as maximum likelihood classification and less conventional ones such as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see why the color of a pixel provides a nice, bounded, fixed dimensional space in which these classifiers work well. It is often the case however, that spectral information alone is not sufficient to correctly classify a pixel. Maybe spatial neighborhood information is required as well. Or may be the raw spectral components do not themselves make for easy classification, but some arithmetic combination of them would. In either of these cases we have the problem of selecting suitable spatial, spectral or spatio-spectral features that allow the classifier to do its job well. The number of all possible such features is extremely large. How can we select a suitable subset? We have developed GENIE, a hybrid learning system that combines a genetic algorithm that searches a space of image processing operations for a set that can produce suitable feature planes, and a more conventional classifier which uses those feature planes to output amore »
- Authors:
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- US Department of Energy (US)
- OSTI Identifier:
- 769263
- Report Number(s):
- LA-UR-00-5892
TRN: AH200121%%62
- DOE Contract Number:
- W-7405-ENG-36
- Resource Type:
- Conference
- Resource Relation:
- Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 1 Dec 2000
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; CLASSIFICATION; COLOR; FORESTS; GENETICS; IMAGE PROCESSING; LEARNING; NEURAL NETWORKS
Citation Formats
PERKINS, S, and ET AL. GENIE: A HYBRID GENETIC ALGORITHM FOR FEATURE CLASSIFICATION IN MULTI-SPECTRAL IMAGES. United States: N. p., 2000.
Web.
PERKINS, S, & ET AL. GENIE: A HYBRID GENETIC ALGORITHM FOR FEATURE CLASSIFICATION IN MULTI-SPECTRAL IMAGES. United States.
PERKINS, S, and ET AL. 2000.
"GENIE: A HYBRID GENETIC ALGORITHM FOR FEATURE CLASSIFICATION IN MULTI-SPECTRAL IMAGES". United States. https://www.osti.gov/servlets/purl/769263.
@article{osti_769263,
title = {GENIE: A HYBRID GENETIC ALGORITHM FOR FEATURE CLASSIFICATION IN MULTI-SPECTRAL IMAGES},
author = {PERKINS, S and ET AL},
abstractNote = {We consider the problem of pixel-by-pixel classification of a multi-spectral image using supervised learning. Conventional supervised classification techniques such as maximum likelihood classification and less conventional ones such as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see why the color of a pixel provides a nice, bounded, fixed dimensional space in which these classifiers work well. It is often the case however, that spectral information alone is not sufficient to correctly classify a pixel. Maybe spatial neighborhood information is required as well. Or may be the raw spectral components do not themselves make for easy classification, but some arithmetic combination of them would. In either of these cases we have the problem of selecting suitable spatial, spectral or spatio-spectral features that allow the classifier to do its job well. The number of all possible such features is extremely large. How can we select a suitable subset? We have developed GENIE, a hybrid learning system that combines a genetic algorithm that searches a space of image processing operations for a set that can produce suitable feature planes, and a more conventional classifier which uses those feature planes to output a final classification. In this paper we show that the use of a hybrid GA provides significant advantages over using either a GA alone or more conventional classification methods alone. We present results using high-resolution IKONOS data, looking for regions of burned forest and for roads.},
doi = {},
url = {https://www.osti.gov/biblio/769263},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Dec 01 00:00:00 EST 2000},
month = {Fri Dec 01 00:00:00 EST 2000}
}