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TASK Quarterly

MEMBERSHIP FUNCTION – ARTMAP NEURAL NETWORKS

Abstract

The project deals with the application of computational intelligence (CI) tools for multispectral image classification. Pattern recognition scheme is a global approach where the classification part is playing an important role to achieve the highest classification accuracy. Multispectral images are data mainly used in remote sensing and this kind of classification is very difficult to assess the accuracy of classification results. There is a feedback problem in adjusting the parts of pattern recognition scheme. Precise classification accuracy assessment is almost impossible to obtain, being an extremely laborious procedure. The paper presents simple neural networks for multispectral image classification, ARTMAP-like neural networks as more sophisticated tools for classification, and a modular approach to achieve the highest classification accuracy of multispectral images. There is a strong link to advances in computer technology, which gives much better conditions for modelling more sophisticated classifiers for multispectral images.

Keywords:

pattern recognition principles, classifier design, classification accuracy assessment, contingency tables, backpropagation neural networks, fuzzy BP neural networks, ART and ARTMAP neural networks, modular neural networks

Details

Issue
Vol. 7 No. 1 (2003)
Section
Research article
Published
2003-03-31
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biographies

MARCEL HRIC,
Faculty of EE and Informatics Technical University, Center for Intelligent Technologies



JAN VASˇCˇAK,
Faculty of EE and Informatics Technical University, Center for Intelligent Technologies



Authors

  • PETER SINCˇAK

    Siemens AG, Vienna PSE Department, ECANSE working group
  • MARCEL HRIC

    Faculty of EE and Informatics Technical University, Center for Intelligent Technologies
  • JAN VASˇCˇAK

    Faculty of EE and Informatics Technical University, Center for Intelligent Technologies

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