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Fuzzy ARTMAP — A Neural Classifier for Multispectral Image Classification

In: GeoComputational Modelling

Author

Listed:
  • Sucharita Gopal

    (Boston University)

  • Manfred M. Fischer

    (Vienna University of Economics and Business Administration)

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, Maslanik and Schweiger 1989; McClellan et al. 1989; Benediktsson, Swain and Ersoy 1990; Hepner et al. 1990). Subsequent studies analyzed backpropagation networks in more detail and compared them to standard statistical classifiers such as the Gaussian maximum likelihood (see Bischof; Schneider and Pinz 1992; Kanellopoulos, Wilkinson and Mégier 1993; Fischer et al. 1994).

Suggested Citation

  • Sucharita Gopal & Manfred M. Fischer, 2001. "Fuzzy ARTMAP — A Neural Classifier for Multispectral Image Classification," Advances in Spatial Science, in: Manfred M. Fischer & Yee Leung (ed.), GeoComputational Modelling, chapter 7, pages 165-194, Springer.
    • Handle: RePEc:spr:adspcp:978-3-662-04637-1_7
    DOI: 10.1007/978-3-662-04637-1_7
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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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