Summary
Neural networks are attractive for the supervised classification of remotely sensed data. There are, however, many problems with their use, restricting the realisation of their full potential. This article focuses on the accommodation of fuzziness in the classification procedure. This is required if the classes to be mapped are continuous or if there is a large proportion of mixed pixels. A continuum of fuzzy classifications was proposed and it is shown that a neural network may be configured at any point along this continuum, from a completely-hard to a fully-fuzzy classification. Examples of fuzzy classifications are given illustrating the potential for mapping continuous classes and reducing the mixed pixel problem.
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© 1997 Springer-Verlag Berlin Heidelberg
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Foody, G.M. (1997). Land Cover Mapping from Remotely Sensed Data with a Neural Network: Accommodating Fuzziness. In: Kanellopoulos, I., Wilkinson, G.G., Roli, F., Austin, J. (eds) Neurocomputation in Remote Sensing Data Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59041-2_4
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DOI: https://doi.org/10.1007/978-3-642-59041-2_4
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