Identification of hazelnut fields using spectral and Gabor textural features

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dc.authoridTasdemir, Kadim -- 0000-0001-7519-1911;
dc.contributor.authorReis, Selcuk
dc.contributor.authorTaşdemir, Kadim
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-29T19:26:25Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-29T19:26:25Z
dc.date.issued2011
dc.departmentMühendislik Fakültesi
dc.description.abstractLand cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchards are planted irregularly and densely at fields with high slope, textural delineation of these orchards from other woody vegetation becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High F-beta values (a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar F-beta values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards. (C) 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.isprsjprs.2011.04.006
dc.identifier.endpage661en_US
dc.identifier.issn0924-2716
dc.identifier.issn1872-8235
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage652en_US
dc.identifier.urihttps://doi.org/10.1016/j.isprsjprs.2011.04.006
dc.identifier.urihttps://hdl.handle.net/20.500.12451/5588
dc.identifier.volume66en_US
dc.identifier.wosWOS:000295653300010
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofISPRS Journal of Photogrammetry and Remote Sensing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectOrchard Detection
dc.subjectHazel Orchards
dc.subjectTexture Analysis
dc.subjectMulti-scale Gabor Features
dc.subjectSelf-organizing Maps
dc.subjectMaximum Likelihood Classifier
dc.titleIdentification of hazelnut fields using spectral and Gabor textural features
dc.typeArticle

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