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Unsupervised Fuzzy Classification of Multispectral Imagery Using Spatial-Spectral Features

  • Rafael Wiemker
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Pixel-wise spectral classification is a widely used technique to produce thematic maps from remotely sensed multispectral imagery. It is commonly based on purely spectral features. In our approach we additionally consider additional spatial features in the form of local context information. After all, spatial context is the defining property of an image. Markov random field modeling provides the assumption that the probability of a certain pixel to belong to a certain class depends on the pixel’s local neighborhood. We enhance the ICM algorithm of Besag (1986) to account for the fuzzy class membership in the fuzzy clustering algorithm of Bezdek (1973). The algorithm presented here was tested on simulated and real remotely sensed multispectral imagery. We demonstrate the improvement of the clustering as achieved by the additional spatial fuzzy neighborhood features.

Keywords

Cluster Center Multispectral Image Unsupervised Cluster Fuzzy Cluster Algorithm Spectral Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

  • Rafael Wiemker
    • 1
  1. 1.II. Institut für ExperimentalphysikUniversität Hamburg, KOGS / InformatikHamburgGermany

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