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Fuzzy ARTMAP — A Neural Classifier for Multispectral Image Classification

In: Recent Developments in Spatial Analysis

Author

Listed:
  • Sucharita Gopal

    (Boston University)

  • Manfred M. Fischer

    (Vienna University of Economics and Business Administration
    Institute for Urban and Regional Research)

Abstract

Spectral pattern recognition deals with classifications that utilize pixel-by-pixel spectral information from satellite imagery. The literature on neural network applications in this area is relatively new, dating back only about six to seven years. The first studies established the feasibility of error-based learning systems such as backpropagation (see Key et al., 1989, McClellan et al., 1989, Benediktsson et al., 1990, Hepner et al., 1990). Subsequent studies analysed backpropagation networks in more detail and compared them to standard statistical classifiers such as the Gaussian maximum likelihood (see Bischof et al., 1992, Kanellopoulos et al., 1993, Fischer et al., 1994).

Suggested Citation

  • Sucharita Gopal & Manfred M. Fischer, 1997. "Fuzzy ARTMAP — A Neural Classifier for Multispectral Image Classification," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 16, pages 306-335, Springer.
    • Handle: RePEc:spr:adspcp:978-3-662-03499-6_16
    DOI: 10.1007/978-3-662-03499-6_16
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    Cited by:

    1. M M Fischer, 1998. "Computational Neural Networks: A New Paradigm for Spatial Analysis," Environment and Planning A, , vol. 30(10), pages 1873-1891, October.

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