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Proceedings Paper

Investigation of image feature extraction by a genetic algorithm
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Paper Abstract

We describe the implementation and performance of a genetic algorithm which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of our analysis of the efficiency of the classic genetic operations of crossover and mutation for our application, and discuss our choice of evolutionary control parameters. We exhibit some of our evolved algorithms, and discuss possible avenues for future progress.

Paper Details

Date Published: 1 November 1999
PDF: 8 pages
Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); doi: 10.1117/12.367697
Show Author Affiliations
Steven P. Brumby, Los Alamos National Lab. (United States)
James P. Theiler, Los Alamos National Lab. (United States)
Simon J. Perkins, Los Alamos National Lab. (United States)
Neal R. Harvey, Los Alamos National Lab. (United States)
John J. Szymanski, Los Alamos National Lab. (United States)
Jeffrey J. Bloch, Los Alamos National Lab. (United States)
Melanie Mitchell, Santa Fe Institute (United States)


Published in SPIE Proceedings Vol. 3812:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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