Satellite images increasingly become a major data source for monitoring changes in the
natural environment. A main task in the analysis of satellite images is concerned with the
modelling of land use classes by reducing uncertainty during a classification process. In the
approach presented in this paper uncertainty is perceived to be due to the vagueness of
geographical categories caused by either the complexity of the category (like 'urban area') or
by the use of the category in several application contexts. Two circumstances of use of an
extended set theoretical concept (fuzzy sets) are discussed. First, two algorithms from the
ISODATA class are used to determine the grades of membership to three a priori defined
classes (woodland, suburban area, urban area) of a LANDSAT TM satellite image of Vienna,
Austria. The results are visualized by a RGB image of the grades of membership to each
category. Second, a measure of fuzziness is employed on the results. The measure relies on
the concept of distance between a seUcategory and its complement. The so determined
vagueness provide more information on the uncertainty of the different categories and may
improve further information processing tasks. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:4174 |
| Date | 01 1900 |
| Creators | Fischer, Manfred M., Benedikt, Josef |
| Publisher | WU Vienna University of Economics and Business |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Paper, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://epub.wu.ac.at/4174/ |
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