{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T17:03:18Z","timestamp":1773766998726,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T00:00:00Z","timestamp":1551398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs11050503","type":"journal-article","created":{"date-parts":[[2019,3,4]],"date-time":"2019-03-04T05:45:36Z","timestamp":1551678336000},"page":"503","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9952-0801","authenticated-orcid":false,"given":"Sachit","family":"Rajbhandari","sequence":"first","affiliation":[{"name":"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-2127","authenticated-orcid":false,"given":"Jagannath","family":"Aryal","sequence":"additional","affiliation":[{"name":"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2278-3766","authenticated-orcid":false,"given":"Jon","family":"Osborn","sequence":"additional","affiliation":[{"name":"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9468-4516","authenticated-orcid":false,"given":"Arko","family":"Lucieer","sequence":"additional","affiliation":[{"name":"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"}]},{"given":"Robert","family":"Musk","sequence":"additional","affiliation":[{"name":"Timberlands Pacific, Level 1, Cimitiere House, 113-115 Cimitiere Street, Launceston, Tasmania 7250, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis - Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. 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