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dc.contributor.authorTASDEMIR KADIMen_GB
dc.date.accessioned2011-04-30T02:00:40Z-
dc.date.available2011-04-29en_GB
dc.date.available2011-04-30T02:00:40Z-
dc.date.created2011-04-13en_GB
dc.date.issued2010en_GB
dc.date.submitted2010-06-17en_GB
dc.identifier.citationProceedings of the 2010 IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - ISBN 978-1-4244-7257-4 p. 1-4en_GB
dc.identifier.urihttp://ieeexplore.ieee.org//xpls/abs_all.jsp?arnumber=5742803en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC59235-
dc.description.abstractLand cover identification from remote sensing images by using automatic methods has been essential for agricultural management and monitoring. In particular, accurate detection of lands covered with nuts orchards from VHR images is an ongoing challenge mainly due to varying statistics of orchards, such as crown sizes, overlapping crowns, distances between the orchards, existence of different tree species and surface structure. An innovative approach, becomingmore popular everyday to overcome similar problems, is to merge spectral and spatial information for utilizing their advantages. In this paper, we propose a self-organizing map that exploits these two information without additional computation of texture. Experimental results on detection of hazelnut orchards from Quickbird imagery show that the proposed method outperforms methods based only on spectral or on spatial information.en_GB
dc.description.sponsorshipJRC.DDG.H.4-Monitoring agricultural resourcesen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.relation.ispartofseriesJRC59235en_GB
dc.titleClassification of hazelnut orchards by self-organizing mapsen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1109/PRRS.2010.5742803en_GB
JRC Directorate:Sustainable Resources

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