期刊
COMPUTERS & GEOSCIENCES
卷 102, 期 -, 页码 158-166出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2017.02.018
关键词
Ontology; Expert knowledge; Remote sensing; Image classification; Description logics
资金
- PO FEDER GUYANE program
- European Union FP7 BIO_SOS project (BIOdiversity Multi-Source Monitoring System: from Space To Species) [263435]
- GEOSUD project of the Investissements d'Avenir program [ANR-10-EQPX-20]
Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for domain experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.
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