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

Remote Sensing

Damir Medak¹  Robert Župan¹  Nikola Kranjčić²  Milan Rezo² 

[1] Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia;Faculty of Geotechnical Engineering, University of Zagreb, Hallerova aleja 7, 42000 Varaždin, Croatia;

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

DOI  :  10.3390/rs11060655

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown