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

Autor:	Nikola Kranjčić, Damir Medak, Robert Župan, Milan Rezo
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	machine learning support vector machine kernels green urban areas extraction satellite images Science
Zdroj:	Remote Sensing, Vol 11, Iss 6, p 655 (2019)
Druh dokumentu:	article
ISSN:	2072-4292
DOI:	10.3390/rs11060655
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/af9b818f03a44d8cbba470960fd2653e Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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