Browsing by Author "Cavur, M."

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    An Evaluation of Land Use Land Cover (Lulc) Classification for Urban Applications With Quickbird and Worldview2 Data
    (Institute of Electrical and Electronics Engineers Inc., 2015) Cavur, M.; Kemec, S.; Nabdel, L.; Sebnem Duzgun, H.
    Monitoring and analysis of the land and rapid environmental change, leads to the use of Land Use and Land Cover (LULC) classification approaches from remote sensing data. The main focus of this aper is to illustrate the practical approach to analysis and mapping of land use and land cover features using high resolution satellite images. The study is carried out for two different places, Basel and Tel Aviv. For this purpose, Quickbird satellite imagery is used for Basel and WorldView2 imagery for Tel Aviv. The classification method chosen for the Quickbird image is Support Vector Machine (SVM) classifier and Maximum Likelihood method for the WordView2 satellite imagery. Both of the methods are applied using ENVI 5.0 Remote Sensing software. An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. © 2015 IEEE.
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    Land Use and Land Cover Classification of Sentinel 2-A: St Petersburg Case Study
    (International Society for Photogrammetry and Remote Sensing, 2019) Cavur, M.; Duzgun, H.S.; Kemec, S.; Demirkan, D.C.
    Land use and land cover (LULC) maps in many areas have been used by companies, government offices, municipalities, and ministries. Accurate classification for LULC using remotely sensed data requires State of Art classification methods. The SNAP free software and ArcGIS Desktop were used for analysis and report. In this study, the optical Sentinel-2 images were used. In order to analyze the data, an object-oriented method was applied: Supported Vector Machines (SVM). An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. The overall classification accuracy of 83,64% with the kappa value of 0.802 was achieved using SVM. The study indicated that of SVM algorithms, the proposed framework on Sentinel-2 imagery results is satisfactory for LULC maps. © Authors 2019.