Journal
REMOTE SENSING
Volume 11, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/rs11060655
Keywords
machine learning; support vector machine; kernels; green urban areas extraction; satellite images
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Funding
- Croatian Science Foundation under the project Geospatial Monitoring of Green Infrastructure by Means of Terrestrial, Airborne and Satellite Imagery [IP-2016-06-5621]
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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.
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