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Leveraging machine learning to extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): a case study in forest-type mapping
Citation
Rajbhandari, S and Aryal, J and Osborn, J and Lucieer, A and Musk, R, Leveraging machine learning to extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): a case study in forest-type mapping, Remote Sensing, 11, (5) Article 503. ISSN 2072-4292 (2019) [Refereed Article]
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Copyright Statement
Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Abstract
Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | GEOBIA, rule-based classification, ontology, machine learning, random forests, rules extraction, variogram, semantic similarities, semantic variogram, Earth observation |
| Research Division: | Engineering |
| Research Group: | Geomatic engineering |
| Research Field: | Photogrammetry and remote sensing |
| Objective Division: | Environmental Management |
| Objective Group: | Terrestrial systems and management |
| Objective Field: | Terrestrial biodiversity |
| 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) |
| UTAS Author: | Musk, R (Dr Robert Musk) |
| ID Code: | 131140 |
| Year Published: | 2019 |
| Web of Science® Times Cited: | 14 |
| Deposited By: | Geography and Spatial Science |
| Deposited On: | 2019-03-05 |
| Last Modified: | 2020-05-19 |
| Downloads: | 35 View Download Statistics |
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