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Remote Sens. 2015, 7(7), 9473-9491; doi:10.3390/rs70709473

Improving the Computational Performance of Ontology-Based Classification Using Graph Databases

1
School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Urstein Süd 1, Puch, Salzburg 5412, Austria
2
Department of Geoinformatics (Z_GIS), University of Salzburg, Schillerstrasse 30, Salzburg 5020, Austria
3
IT Innovation Centre, University of Southampton, Gamma House, Enterprise Road, Southampton SO16 7NS, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 31 March 2015 / Revised: 16 July 2015 / Accepted: 17 July 2015 / Published: 22 July 2015
View Full-Text   |   Download PDF [13493 KB, uploaded 22 July 2015]   |  

Abstract

The increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography) represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi-) automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protégé environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protégé-based approach. View Full-Text
Keywords: ontology; graph database; Neo4j; remote sensing; classification ontology; graph database; Neo4j; remote sensing; classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lampoltshammer, T.J.; Wiegand, S. Improving the Computational Performance of Ontology-Based Classification Using Graph Databases. Remote Sens. 2015, 7, 9473-9491.

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