Please use this identifier to cite or link to this item:
http://148.72.244.84/xmlui/handle/xmlui/4606
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.date.accessioned | 2023-10-18T06:59:15Z | - |
dc.date.available | 2023-10-18T06:59:15Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | https://dx.doi.org/10.24237/djps.15.03.486B | en_US |
dc.identifier.issn | 2222-8373 | - |
dc.identifier.uri | http://148.72.244.84:8080/xmlui/handle/xmlui/4606 | - |
dc.description.abstract | Satellite 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.sponsorship | https://djps.uodiyala.edu.iq/ | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Diyala | en_US |
dc.subject | Keywords: Supervised Classification, Satellite Images, Feature Extraction, GLCM, LBP, SVM, KNN | en_US |
dc.title | Satellite Images Scene Classification Based Support Vector Machines and K-Nearest Neighbor | en_US |
dc.title.alternative | تصنيف مشهد القمر الصناعي باعتماد خوارزمية شعاع الدعم االلي وخوارزمية الجار األقرب | en_US |
dc.type | Article | en_US |
Appears in Collections: | مجلة ديالى للعلوم الاكاديمية / Academic Science Journal (Acad. Sci. J.) |
Files in This Item:
File | Description | Size | Format | |
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5e-P1(486).pdf | 773.3 kB | Adobe PDF | View/Open |
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