Please use this identifier to cite or link to this item: http://148.72.244.84/xmlui/handle/xmlui/4606
Title: Satellite Images Scene Classification Based Support Vector Machines and K-Nearest Neighbor
Other Titles: تصنيف مشهد القمر الصناعي باعتماد خوارزمية شعاع الدعم االلي وخوارزمية الجار األقرب
Keywords: Keywords: Supervised Classification, Satellite Images, Feature Extraction, GLCM, LBP, SVM, KNN
Issue Date: 2019
Publisher: University of Diyala
Citation: https://dx.doi.org/10.24237/djps.15.03.486B
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.
URI: http://148.72.244.84:8080/xmlui/handle/xmlui/4606
ISSN: 2222-8373
Appears in Collections:مجلة ديالى للعلوم الاكاديمية / Academic Science Journal (Acad. Sci. J.)

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