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.

Machine Learning Methods for Classification of the Green Infrastructure in City Areas

“…Table 3 shows the coordinates of the center of the study area in Varaždin and Osijek. Figure 1 shows the study areas with a signature and control samples [15].…”

“…The authors in [14] compared the random forest, K nearest neighbor, and SVM algorithms for land cover classification using Sentinel-2 imagery and concluded that SVM provided the best results. The authors in [15] explored how different kernels affect the land cover classification results using Sentinel-2 imagery. They found that the radial basis function produced the highest accuracy and proposed further research using different machine learning methods.…”

“…They found that the radial basis function produced the highest accuracy and proposed further research using different machine learning methods. This paper is follow-up to a paper published by [15]. Three new machine learning methods will be evaluated and compared to SVM.…”

“…The most commonly used kernels for image processing are the polynomial, radial basis function (RBF), and sigmoid kernels [18]. The authors in [15] discuss the effect of different kernels on supervised classification results. The authors in [15] concluded that the RBF kernel provides the best results with γ = 1 and C = 28, where γ is the free parameter of the radial basis function and C is parameter that allows to trade off training error versus model complexity.…”

“…The authors in [15] discuss the effect of different kernels on supervised classification results. The authors in [15] concluded that the RBF kernel provides the best results with γ = 1 and C = 28, where γ is the free parameter of the radial basis function and C is parameter that allows to trade off training error versus model complexity. They made conclusion based on experiment on two cities in Croatia on Sentinel 2 imagery.…”