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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


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Rajbhandari, S ORCID: 0000-0002-9952-0801, Aryal, J ORCID: 0000-0002-4875-2127, Osborn, J ORCID: 0000-0003-2278-3766, Lucieer, A ORCID: 0000-0002-9468-4516 and Musk, R 2019 , 'Leveraging machine learning to extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): a case study in forest-type mapping' , Remote Sensing, vol. 11, no. 5 , pp. 1-25 , doi: 10.3390/rs11050503.

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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 Type: Article
Authors/Creators:Rajbhandari, S and Aryal, J and Osborn, J and Lucieer, A and Musk, R
Keywords: GEOBIA, rule-based classification, ontology, machine learning, random forests, rules extraction, variogram, semantic similarities, semantic variogram, Earth observation
Journal or Publication Title: Remote Sensing
Publisher: MDPIAG
ISSN: 2072-4292
DOI / ID Number: 10.3390/rs11050503
Copyright Information:

Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

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