eCite Digital Repository
Benchmarking the applicability of ontology in geographic object-based image analysis
Citation
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]
 | PDF 4Mb |
Copyright Statement
© 2017 by the Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/
Abstract
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 |
Repository Staff Only: item control page