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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/

DOI: doi:10.3390/rs11050503

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:Environment
Objective Group:Flora, Fauna and Biodiversity
Objective Field:Forest and Woodlands Flora, Fauna and 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 (Associate Professor Arko Lucieer)
UTAS Author:Musk, R (Dr Robert Musk)
ID Code:131140
Year Published:2019
Deposited By:Geography and Spatial Science
Deposited On:2019-03-05
Last Modified:2019-04-24
Downloads:7 View Download Statistics

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