Please use this identifier to cite or link to this item: http://148.72.244.84:8080/xmlui/handle/xmlui/4606
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dc.date.accessioned2023-10-18T06:59:15Z-
dc.date.available2023-10-18T06:59:15Z-
dc.date.issued2019-
dc.identifier.citationhttps://dx.doi.org/10.24237/djps.15.03.486Ben_US
dc.identifier.issn2222-8373-
dc.identifier.urihttp://148.72.244.84:8080/xmlui/handle/xmlui/4606-
dc.description.abstractSatellite image classification is a valuable technique for producing worthy information. This paper deal with high-resolution satellite for scene classification. In this research presents three algorithms were used to extract the features which are local binary patterns, gray level co occurrence matrix, and color histogram features. The classification process included the use of two types of data mining techniques belongs to supervisor classification which are support vector machines, and k-nearest neighbor. Test results explain that the proposed classification method obtains a very auspicious performance.en_US
dc.description.sponsorshiphttps://djps.uodiyala.edu.iq/en_US
dc.language.isoenen_US
dc.publisherUniversity of Diyalaen_US
dc.subjectKeywords: Supervised Classification, Satellite Images, Feature Extraction, GLCM, LBP, SVM, KNNen_US
dc.titleSatellite Images Scene Classification Based Support Vector Machines and K-Nearest Neighboren_US
dc.title.alternativeتصنيف مشهد القمر الصناعي باعتماد خوارزمية شعاع الدعم االلي وخوارزمية الجار األقربen_US
dc.typeArticleen_US
Appears in Collections:مجلة ديالى للعلوم الاكاديمية / Academic Science Journal (Acad. Sci. J.)

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