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Title: GENIE: A HYBRID GENETIC ALGORITHM FOR FEATURE CLASSIFICATION IN MULTI-SPECTRAL IMAGES

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 » 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.« less
Authors:
;
Publication Date:
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
Research Org:
Los Alamos National Lab., NM (US)
Sponsoring Org:
US Department of Energy (US)
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