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

Abstract

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.