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Proceedings Paper

Fuzzy ontologies for semantic interpretation of remotely sensed images
Author(s): Khelifa Djerriri; Mimoun Malki
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Paper Abstract

Object-based image classification consists in the assignment of object that share similar attributes to object categories. To perform such a task the remote sensing expert uses its personal knowledge, which is rarely formalized. Ontologies have been proposed as solution to represent domain knowledge agreed by domain experts in a formal and machine readable language. Classical ontology languages are not appropriate to deal with imprecision or vagueness in knowledge. Fortunately, Description Logics for the semantic web has been enhanced by various approaches to handle such knowledge. This paper presents the extension of the traditional ontology-based interpretation with fuzzy ontology of main land-cover classes in Landsat8-OLI scenes (vegetation, built-up areas, water bodies, shadow, clouds, forests) objects. A good classification of image objects was obtained and the results highlight the potential of the method to be replicated over time and space in the perspective of transferability of the procedure.

Paper Details

Date Published: 15 October 2015
PDF: 10 pages
Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96432F (15 October 2015); doi: 10.1117/12.2195071
Show Author Affiliations
Khelifa Djerriri, Ctr. National des Techniques Spatiales (Algeria)
Univ. de Sidi-Bel-Abbes (Algeria)
Mimoun Malki, Univ. de Sidi-Bel-Abbes (Algeria)

Published in SPIE Proceedings Vol. 9643:
Image and Signal Processing for Remote Sensing XXI
Lorenzo Bruzzone, Editor(s)

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