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

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posted on 2023-05-19, 14:07 authored by Rajbhandari, S, Jagannath Aryal, Jonathan OsbornJonathan Osborn, Musk, R, Arko LucieerArko Lucieer
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.

History

Publication title

ISPRS International Journal of Geo-Information

Volume

6

Article number

386

Number

386

Pagination

1-24

ISSN

2220-9964

Department/School

School of Geography, Planning and Spatial Sciences

Publisher

M D P I AG

Place of publication

Switzerland

Rights statement

© 2017 by the Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/

Repository Status

  • Open

Socio-economic Objectives

Natural hazards not elsewhere classified

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