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Proceedings Paper

Fuzzy segmentation and structural knowledge for satellite image interpretation
Author(s): Laurent Wendling; Mustapha Zehana
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Paper Abstract

In this paper we present a segmentation method using fuzzy sets theory applied to remote sensing image interpretation. We have developed a new fuzzy segmentation system in order to take into account complex spatial knowledge (elongated shape, compact area, features based on surfaces, perimeters,...) involving topologic attributes and also relative position of searched areas in Certainty Factors images. A C.F. image represents the belonging degrees of each pixel to a given class and is supposed to have been obtained by a previous classification (involving simple contextual knowledge). To improve this previous classification, we introduce structural rules which allow us to manage with region characteristics. These structural characteristics are obtained by using a fuzzy segmentation technique. The proposed system uses random sets representation (convex combination of sets) for each fuzzy region (weighted set of connected pixels) automatically extracted from a C.F. image. The main interest of this method is to split a C.F. image in fuzzy regions. A fuzzy region is defined by a set of concentric crisp regions and for each of them, topologic attributes are computed to provide the value of the final attribute of the fuzzy region.

Paper Details

Date Published: 30 December 1994
PDF: 10 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196710
Show Author Affiliations
Laurent Wendling, Univ. Paul Sabatier (France)
Mustapha Zehana, Univ. Paul Sabatier (France)


Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

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