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Open AccessArticle

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

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Faculty of Geotechnical Engineering, University of Zagreb, Hallerova aleja 7, 42000 Varaždin, Croatia
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Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 655; https://doi.org/10.3390/rs11060655
Received: 22 January 2019 / Revised: 2 March 2019 / Accepted: 15 March 2019 / Published: 18 March 2019
(This article belongs to the Section Urban Remote Sensing)
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. View Full-Text
Keywords: machine learning; support vector machine; kernels; green urban areas extraction; satellite images machine learning; support vector machine; kernels; green urban areas extraction; satellite images
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MDPI and ACS Style

Kranjčić, N.; Medak, D.; Župan, R.; Rezo, M. Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns. Remote Sens. 2019, 11, 655.

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