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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

1
Discipline of Geography and Spatial Sciences, School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, Private Bag 76, Hobart, Tasmania 7001, Australia
2
Timberlands Pacific, Level 1, Cimitiere House, 113-115 Cimitiere Street, Launceston, Tasmania 7250, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 503; https://doi.org/10.3390/rs11050503
Received: 11 January 2019 / Revised: 13 February 2019 / Accepted: 25 February 2019 / Published: 1 March 2019
(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)
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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. View Full-Text
Keywords: GEOBIA; rule-based classification; ontology; machine learning; random forests; rules extraction; variogram; semantic similarities; semantic variogram GEOBIA; rule-based classification; ontology; machine learning; random forests; rules extraction; variogram; semantic similarities; semantic variogram
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rajbhandari, S.; Aryal, J.; Osborn, J.; Lucieer, A.; Musk, R. Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping. Remote Sens. 2019, 11, 503.

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