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Remote Sens. 2017, 9(4), 329; doi:10.3390/rs9040329

An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology

1
Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing 100830, China
2
School of Geodesy and Geomatics, Wuhan University, Luojiashan, Wuhan 430072, China
3
Department of Geoinformatics—Z_GIS, University of Salzburg, Schillerstrasse 30, Salzburg 5020, Austria
4
Institute for Photogrammetry, University of Stuttgart, Geschwister-Scholl-Str. 24D, 70174 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Norman Kerle, Markus Gerke, Sébastien Lefèvre, Xiaofeng Li and Prasad S. Thenkabail
Received: 30 December 2016 / Revised: 17 March 2017 / Accepted: 24 March 2017 / Published: 30 March 2017
View Full-Text   |   Download PDF [10139 KB, uploaded 30 March 2017]   |  

Abstract

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—similar to other emerging paradigms—lacks 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—as compared to the decision tree classification without using the ontology—yielded 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. View Full-Text
Keywords: geographic object-based image analysis; ontology; semantic network model; web ontology language; semantic web rule language; machine learning; semantic rule; land-cover classification geographic object-based image analysis; ontology; semantic network model; web ontology language; semantic web rule language; machine learning; semantic rule; land-cover classification
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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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Gu, H.; Li, H.; Yan, L.; Liu, Z.; Blaschke, T.; Soergel, U. An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology. Remote Sens. 2017, 9, 329.

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