Multidimensional data classification

  • Authors:

  • Dana Klimešová;Eva Ocelíková

  • Affiliations:

  • Faculty of Economics and Management, Czech Univ of Life Sci., Prague 6, Suchdol, Czech Republic and Dept. of Image Processing, Inst. of Info Theory and Automation, Czech Academy of Sci., Prague 8, ...;Faculty of Electrical Engineering and Informatics, Technical University of Košice, Košice, Slovakia

  • Venue:
  • ICAI'09 Proceedings of the 10th WSEAS international conference on Automation & information
  • Year:
  • 2009

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Abstract

This paper deals with the classification of objects into the limited number of classes. Objects are characterised by n-features, e.g. n-dimensional vectors describe them. The paper focuses on the Bayes classifier based on the probability principle, with the fixed number of the features during classification process. Bayes classifier, which uses criterion of the minimum error, was applied on the set of the multispectral data. They represent real images of the Earth surface obtained from remote Earth sensing. The paper describes experience and results obtained during the classification of extensive set of these multispectral data and analysis of influence of dispersions and mean values of features on classification results.