17 June 1996 Improving the performance of genetic algorithms for terrain categorization of multispectral images
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A method that uses a genetic algorithm (GA) to optimize rules for categorizing terrain as depicted in multispectral data has been developed by us. A variety of multispectral data have been used in the work. Linear techniques have not separated terrain categories with sufficient accuracy so that genetic algorithms have been applied to the problem. Genetic algorithms, in general, are a nonlinear optimization technique based on the biological ideas of natural selection and survival of the fittest. For the work presented here, the genetic algorithm optimizes rules for the categorization of terrain. The genetic algorithm produced promising results for terrain categorization; however, work continues with efforts to improve classification accuracy. As part of this effort, new rule types have been added to the genetic algorithm's repertoire. These new rule types include the clustering of data, the ratio of bands, the linear combination of bands, boxes in spectral space, and the second order combination of three bands. Improved performance of the rules is demonstrated.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David E. Larch, "Improving the performance of genetic algorithms for terrain categorization of multispectral images", Proc. SPIE 2758, Algorithms for Multispectral and Hyperspectral Imagery II, (17 June 1996); doi: 10.1117/12.243205; https://doi.org/10.1117/12.243205

Genetic algorithms

Multispectral imaging

Neural networks


Image classification

Algorithm development

Binary data

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