Very-High-Resolution SAR Images and Linked Open Data Analytics Based on Ontologies
Authors: 
Daniela Espinoza-Molina
Authors: 
Charalampos Nikolaou
Authors: 
Corneliu Octavian Dumitru
Authors: 
Konstantina Bereta
Authors: 
Manolis Koubarakis
Authors: 
Gottfried Schwarz
Authors: 
Mihai Datcu
Date published: 
2015
Published In: 
Selected Topics in Applied Earth Observations and Remote Sensing, Journal of IEEE, Vol. 8, Issue 4, pages 1696-1708
Type: 
Journal Article
Abstract: 

In this paper, we deal with the integration of multiple sources of information such as Earth observation (EO) synthetic aperture radar (SAR) images and their metadata, semantic descriptors of the image content, as well as other publicly available geospatial data sources expressed as linked open data for posing complex queries in order to support geospatial data analytics. Our approach lays the foundations for the development of richer tools and applications that focus on EO image analytics using ontologies and linked open data. We introduce a system architecture where a common satellite image product is transformed from its initial format into to actionable intelligence information, which includes image descriptors, metadata, image tiles, and semantic labels resulting in an EO-data model. We also create a SAR image ontology based on our EO-data model and a two-level taxonomy classification scheme of the image content. We demonstrate our approach by linking high-resolution TerraSAR-X images with information from CORINE Land Cover (CLC), Urban Atlas (UA), GeoNames, and OpenStreetMap (OSM), which are represented in the standard triple model of the resource description frameworks (RDFs)

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