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Original scientific paper

CLASSIFICATION OF MULTISPECTRAL DATA

Eva Ocelikova ; Faculty of Electrical Engineering and Informatics, Technical University of Košice, Slovak Republic
Jan Krištof ; Faculty of Electrical Engineering and Informatics, Technical University of Košice, Slovak Republic

Fulltext: english, pdf (3 MB) pages 35-41 downloads: 184* cite
APA 6th Edition
Ocelikova, E. & Krištof, J. (2001). CLASSIFICATION OF MULTISPECTRAL DATA. Journal of Information and Organizational Sciences, 25 (1), 35-41. Retrieved from https://hrcak.srce.hr/78460
MLA 8th Edition
Ocelikova, Eva and Jan Krištof. "CLASSIFICATION OF MULTISPECTRAL DATA." Journal of Information and Organizational Sciences, vol. 25, no. 1, 2001, pp. 35-41. https://hrcak.srce.hr/78460. Accessed 17 Sep. 2019.
Chicago 17th Edition
Ocelikova, Eva and Jan Krištof. "CLASSIFICATION OF MULTISPECTRAL DATA." Journal of Information and Organizational Sciences 25, no. 1 (2001): 35-41. https://hrcak.srce.hr/78460
Harvard
Ocelikova, E., and Krištof, J. (2001). 'CLASSIFICATION OF MULTISPECTRAL DATA', Journal of Information and Organizational Sciences, 25(1), pp. 35-41. Available at: https://hrcak.srce.hr/78460 (Accessed 17 September 2019)
Vancouver
Ocelikova E, Krištof J. CLASSIFICATION OF MULTISPECTRAL DATA. Journal of Information and Organizational Sciences [Internet]. 2001 [cited 2019 September 17];25(1):35-41. Available from: https://hrcak.srce.hr/78460
IEEE
E. Ocelikova and J. Krištof, "CLASSIFICATION OF MULTISPECTRAL DATA", Journal of Information and Organizational Sciences, vol.25, no. 1, pp. 35-41, 2001. [Online]. Available: https://hrcak.srce.hr/78460. [Accessed: 17 September 2019]

Abstracts
This paper deals with the classification of objects into a limited number of classes. Objects are characterised by n-features, e.g. n-dimensional vector are used to describe them. The paper focuses on the Bayes classifier based on the probability principle, with a fixed number of features during the classification process. Bayes classifier, that is which uses the criterion of the minimum error, was applied to the set of the multispectral data. They represented real images of the Earth 's surface obtained from remote Earth sensing. This paper describes the experiences and resuIts obtained during the classification of extensive sets of this multispectral data and an analysis of the influence of dispersions and the mean values of the features of the classification results.

Keywords
classification; Bayes classifier; features; multispectral data; decision rule

Hrčak ID: 78460

URI
https://hrcak.srce.hr/78460

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