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Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns (CROSBI ID 262017)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Kranjčić, Nikola ; Medak, Damir ; Župan, Robert ; Rezo, Milan Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns // Remote sensing, 11 (2019), 6; 655, 13. doi: 10.3390/rs11060655

Podaci o odgovornosti

Kranjčić, Nikola ; Medak, Damir ; Župan, Robert ; Rezo, Milan

engleski

Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns

The most commonly used model for analyzing satellite imagery is the Support Vector Machine (SVM). Since there are a large number of possible variables for use in SVM, this paper will provide a combination of parameters that fit best for extracting green urban areas from Copernicus mission satellite images. This paper aims to provide a combination of parameters to extract green urban areas with the highest degree of accuracy, in order to speed up urban planning and ultimately improve town environments. Two different towns in Croatia were investigated, and the results provide an optimal combination of parameters for green urban areas extraction with an overall kappa index of 0.87 and 0.89, which demonstrates a very high classification accuracy.

machine learning ; support vector machine ; kernels ; green urban areas extraction ; satellite images

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Podaci o izdanju

11 (6)

2019.

655

13

objavljeno

2072-4292

10.3390/rs11060655

Trošak objave rada u otvorenom pristupu

Povezanost rada

Geodezija

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