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Title: INVESTIGATION OF IMAGE FEATURE EXTRACTION BY A GENETIC ALGORITHM

Abstract

No abstract prepared.

Authors:
; ;
Publication Date:
Research Org.:
Los Alamos National Lab., NM (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
785449
Report Number(s):
LA-UR-99-3264
TRN: US200307%%494
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 Jul 1999
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; ALGORITHMS; GENETICS; LANL

Citation Formats

S. P. BRUMBY, S. J. PERKINS, and ET AL. INVESTIGATION OF IMAGE FEATURE EXTRACTION BY A GENETIC ALGORITHM. United States: N. p., 1999. Web.
S. P. BRUMBY, S. J. PERKINS, & ET AL. INVESTIGATION OF IMAGE FEATURE EXTRACTION BY A GENETIC ALGORITHM. United States.
S. P. BRUMBY, S. J. PERKINS, and ET AL. 1999. "INVESTIGATION OF IMAGE FEATURE EXTRACTION BY A GENETIC ALGORITHM". United States. doi:. https://www.osti.gov/servlets/purl/785449.
@article{osti_785449,
title = {INVESTIGATION OF IMAGE FEATURE EXTRACTION BY A GENETIC ALGORITHM},
author = {S. P. BRUMBY and S. J. PERKINS and ET AL},
abstractNote = {No abstract prepared.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 1999,
month = 7
}

Conference:
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  • 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 componentsmore » 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.« less
  • Feature extration from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. The tool used is the GENetic Imagery Exploitation (GENIE) software, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniquesmore » to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land-cover features including towns, grasslands, wild fire burn scars, and several types of forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.« less
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