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Proceedings Paper

Derivation of land cover information by fuzzy clustering of remotely sensed imagery
Author(s): Helmut Beissmann; Gernot Tutsch
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Paper Abstract

Automatic pattern recognition by means of fuzzy logic had been applied to several fields during the last years. The spectral properties of different land cover types as seen in multiband images can also be interpreted as patterns in the dimension of gray values. Fuzzy clustering therefore is a new promising approach to mapping land cover from remotely send images. The traditional method of classifying a remotely sensed image is the transformation via a classification algorithm into a single classified image of the land surface, but natural landscapes present a continuum of variety at many different scales and a high proportion of the discretely sampled pixels within an image contains mixed spectral signature and are not easily placed into fixed thematic classes. The estimation of fuzzy memberships to vague classes of land cover more faithfully represents the true situation.

Paper Details

Date Published: 1 February 1998
PDF: 10 pages
Proc. SPIE 3346, Sixth International Workshop on Digital Image Processing and Computer Graphics: Applications in Humanities and Natural Sciences, (1 February 1998); doi: 10.1117/12.301371
Show Author Affiliations
Helmut Beissmann, Institute of Information Processing (Austria)
Gernot Tutsch, Institute of Information Processing (Austria)


Published in SPIE Proceedings Vol. 3346:
Sixth International Workshop on Digital Image Processing and Computer Graphics: Applications in Humanities and Natural Sciences
Emanuel Wenger; Leonid I. Dimitrov, Editor(s)

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