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30 December 1994 Fuzzy segmentation and structural knowledge for satellite image interpretation
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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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Laurent Wendling and Mustapha Zehana "Fuzzy segmentation and structural knowledge for satellite image interpretation", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); https://doi.org/10.1117/12.196710
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