{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:17:43Z","timestamp":1777126663686,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,3,30]],"date-time":"2017-03-30T00:00:00Z","timestamp":1490832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371406"],"award-info":[{"award-number":["41371406"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41471299"],"award-info":[{"award-number":["41471299"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671440"],"award-info":[{"award-number":["41671440"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Geographic Object-Based Image Analysis (GEOBIA) techniques have become increasingly popular in remote sensing. GEOBIA has been claimed to represent a paradigm shift in remote sensing interpretation. Still, GEOBIA\u2014similar to other emerging paradigms\u2014lacks formal expressions and objective modelling structures and in particular semantic classification methods using ontologies. This study has put forward an object-based semantic classification method for high resolution satellite imagery using an ontology that aims to fully exploit the advantages of ontology to GEOBIA. A three-step workflow has been introduced: ontology modelling, initial classification based on a data-driven machine learning method, and semantic classification based on knowledge-driven semantic rules. The classification part is based on data-driven machine learning, segmentation, feature selection, sample collection and an initial classification. Then, image objects are re-classified based on the ontological model whereby the semantic relations are expressed in the formal languages OWL and SWRL. The results show that the method with ontology\u2014as compared to the decision tree classification without using the ontology\u2014yielded minor statistical improvements in terms of accuracy for this particular image. However, this framework enhances existing GEOBIA methodologies: ontologies express and organize the whole structure of GEOBIA and allow establishing relations, particularly spatially explicit relations between objects as well as multi-scale\/hierarchical relations.<\/jats:p>","DOI":"10.3390\/rs9040329","type":"journal-article","created":{"date-parts":[[2017,3,30]],"date-time":"2017-03-30T09:49:54Z","timestamp":1490867394000},"page":"329","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology"],"prefix":"10.3390","volume":"9","author":[{"given":"Haiyan","family":"Gu","sequence":"first","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing 100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitao","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing 100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Luojiashan, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0303-6290","authenticated-orcid":false,"given":"Zhengjun","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing 100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-8458","authenticated-orcid":false,"given":"Thomas","family":"Blaschke","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2014Z_GIS, University of Salzburg, Schillerstrasse 30, Salzburg 5020, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uwe","family":"Soergel","sequence":"additional","affiliation":[{"name":"Institute for Photogrammetry, University of Stuttgart, Geschwister-Scholl-Str. 24D, 70174 Stuttgart, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. 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