A Layered Architecture for a Fuzzy Semantic Approach for Satellite Image Analysis

A Layered Architecture for a Fuzzy Semantic Approach for Satellite Image Analysis

Cecilia Zanni-Merk (ICube Laboratory, University of Strasbourg, Illkirch, France), Stella Marc-Zwecker (ICube Laboratory, University of Strasbourg, Illkirch, France), Cédric Wemmert (ICube Laboratory, University of Strasbourg, Illkirch, France) and François de Bertrand de Beuvron (ICube Laboratory, University of Strasbourg, Illkirch, France)
Copyright: © 2015 |Pages: 26
DOI: 10.4018/IJKSS.2015040103
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The extended use of high and very high spatial resolution imagery inherently demands the adoption of classification methods capable of capturing the underlying semantic. Object-oriented classification methods are currently considered as the most appropriate alternative, due to the incorporation of contextual information and domain knowledge into the analysis. Integrating knowledge initially requires a detailed process of acquisition and later the achievement of a formal representation. Ontologies constitute a very suitable approach to address both knowledge formalization and exploitation. A novel semi-automatic fuzzy semantic approach focused on the extraction and classification of urban objects is hereby introduced. The use of a four-layered architecture allows the separation of concerns among knowledge, rules, experience and meta-knowledge. Knowledge represents the fundamental layer with which the other layers interact. Rules are meant to derive conclusions and make assertions based on knowledge. The experience layer supports the classification process in case of failure when attempting to identify an object, by applying specific expert rules to infer unusual membership. Finally, the meta-knowledge layer contains knowledge about the use of the other layers.
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2. Object-Oriented Image Analysis Combined With Ontologies

A current tendency in formal knowledge representation is the implementation of ontologies, along with sets of logic rules for its management and exploitation. Ontologies are largely described in (Uschold & King, 1995; Guarino, Oberle & Staab, 2009). Basically, they are defined as conceptualizations of certain reality or domain designed for a specific purpose.

Several efforts have been done to delineate strategies combining object feature extraction from remote sensing imagery with the implementation of ontology-based classification methods. We highlight here the propositions that seem pertinent to tackle the semantic gap problem. Puissant et al. (2007) made use of data mining processes for multi-level interpretation of multi-source images. Metral et al. (2010) proposed an urban ontology connected to CityGML to build a 3D city model. Forestier et al. (2012) implemented an automatic region labelling method, by assigning segmented regions from an image into semantic objects contained in a knowledge base.

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