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Open AccessArticle

Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis

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Discipline of Geography and Spatial Sciences, University of Tasmania, Private Bag 76, Hobart, TAS 7001, Australia
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Sustainable Timber Tasmania, 99 Bathurst Street, Hobart, TAS 7000, Australia
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(12), 386; https://doi.org/10.3390/ijgi6120386
Received: 19 September 2017 / Revised: 24 October 2017 / Accepted: 22 November 2017 / Published: 28 November 2017
(This article belongs to the Special Issue GEOBIA in a Changing World)
In Geographic Object-based Image Analysis (GEOBIA), identification of image objects is normally achieved using rule-based classification techniques supported by appropriate domain knowledge. However, GEOBIA currently lacks a systematic method to formalise the domain knowledge required for image object identification. Ontology provides a representation vocabulary for characterising domain-specific classes. This study proposes an ontological framework that conceptualises domain knowledge in order to support the application of rule-based classifications. The proposed ontological framework is tested with a landslide case study. The Web Ontology Language (OWL) is used to construct an ontology in the landslide domain. The segmented image objects with extracted features are incorporated into the ontology as instances. The classification rules are written in Semantic Web Rule Language (SWRL) and executed using a semantic reasoner to assign instances to appropriate landslide classes. Machine learning techniques are used to predict new threshold values for feature attributes in the rules. Our framework is compared with published work on landslide detection where ontology was not used for the image classification. Our results demonstrate that a classification derived from the ontological framework accords with non-ontological methods. This study benchmarks the ontological method providing an alternative approach for image classification in the case study of landslides. View Full-Text
Keywords: GEOBIA; ontology; rule-based classification; landslides; machine learning; random forest GEOBIA; ontology; rule-based classification; landslides; machine learning; random forest
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Rajbhandari, S.; Aryal, J.; Osborn, J.; Musk, R.; Lucieer, A. Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis. ISPRS Int. J. Geo-Inf. 2017, 6, 386.

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