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Benchmarking the applicability of ontology in geographic object-based image analysis


Rajbhandari, S and Aryal, J and Osborn, J and Musk, R and Lucieer, A, Benchmarking the applicability of ontology in geographic object-based image analysis, ISPRS International Journal of Geo-Information, 6 Article 386. ISSN 2220-9964 (2017) [Refereed Article]


Copyright Statement

2017 by the Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

DOI: doi:10.3390/ijgi6120386


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.

Item Details

Item Type:Refereed Article
Keywords:GEOBIA, ontology, rule-based classification, landslides, machine learning, random forest
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Environmental Policy, Climate Change and Natural Hazards
Objective Group:Natural hazards
Objective Field:Natural hazards not elsewhere classified
UTAS Author:Rajbhandari, S (Mr Sachit Rajbhandari)
UTAS Author:Aryal, J (Dr Jagannath Aryal)
UTAS Author:Osborn, J (Dr Jon Osborn)
UTAS Author:Lucieer, A (Professor Arko Lucieer)
ID Code:123012
Year Published:2017
Web of Science® Times Cited:13
Deposited By:Geography and Spatial Science
Deposited On:2017-12-11
Last Modified:2018-05-22
Downloads:107 View Download Statistics

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