Authors:
Fethi Ghazouani
1
;
Wassim Messaoudi
1
and
Imed Riadh Farah
2
Affiliations:
1
National School of Computer Science and University of Manouba, Tunisia
;
2
National School of Computer Science, University of Manouba and Telecom Bretagne, Tunisia
Keyword(s):
Spatio-temporal Object, Dynamics Object, Change Detection, Domain Ontology, Upper Ontology, Multi-level.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Symbolic Systems
Abstract:
Land-use/cover change, climate change, sea level evolution are examples of application that are associated
with change detection. Actually, we use satellite image time series to monitor the change where entities are
often dynamic along time. Moreover, knowledge associated to these spatio-temporal objects can evolve when
changes occur. Thus, for modeling this kind of knowledge it is necessary to deal with four aspects: spectral,
spatial, temporal and semantic. Such approach can be modeled by ontologies in many levels. Thereby, a
shared ontology can be an ontology or a combination of some ontologies based on some mechanisms of
linking. Such link process should maintain consistency between represented knowledge. In this paper, we
propose a multi-level ontological approach for monitoring dynamics in remote sensing images. The proposed
methodology aims to link our domain ontology to an upper level ontology thus enabling to represent existing
change processes.