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

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

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...

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Bibliographic Details
Main Authors: Nikola Kranjčić, Damir Medak, Robert Župan, Milan Rezo
Format: Article
Language:English
Published: MDPI AG 2019-03-01
Series:Remote Sensing
Subjects:
machine learning
support vector machine
kernels
green urban areas extraction
satellite images
Online Access:http://www.mdpi.com/2072-4292/11/6/655
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spelling doaj-af9b818f03a44d8cbba470960fd2653e2020-11-25T01:29:36ZengMDPI AGRemote Sensing2072-42922019-03-0111665510.3390/rs11060655rs11060655Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in TownsNikola Kranjčić0Damir Medak1Robert Župan2Milan Rezo3Faculty of Geotechnical Engineering, University of Zagreb, Hallerova aleja 7, 42000 Varaždin, CroatiaFaculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, CroatiaFaculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, CroatiaFaculty of Geotechnical Engineering, University of Zagreb, Hallerova aleja 7, 42000 Varaždin, CroatiaThe 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.http://www.mdpi.com/2072-4292/11/6/655machine learningsupport vector machinekernelsgreen urban areas extractionsatellite images
collection DOAJ
language English
format Article
sources DOAJ
author Nikola Kranjčić
Damir Medak
Robert Župan
Milan Rezo
spellingShingle Nikola Kranjčić
Damir Medak
Robert Župan
Milan Rezo
Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns
Remote Sensing
machine learning
support vector machine
kernels
green urban areas extraction
satellite images
author_facet Nikola Kranjčić
Damir Medak
Robert Župan
Milan Rezo
author_sort Nikola Kranjčić
title Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns
title_short Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns
title_full Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns
title_fullStr Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns
title_full_unstemmed Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns
title_sort support vector machine accuracy assessment for extracting green urban areas in towns
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2019-03-01
description 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.
topic machine learning
support vector machine
kernels
green urban areas extraction
satellite images
url http://www.mdpi.com/2072-4292/11/6/655
work_keys_str_mv AT nikolakranjcic supportvectormachineaccuracyassessmentforextractinggreenurbanareasintowns
AT damirmedak supportvectormachineaccuracyassessmentforextractinggreenurbanareasintowns
AT robertzupan supportvectormachineaccuracyassessmentforextractinggreenurbanareasintowns
AT milanrezo supportvectormachineaccuracyassessmentforextractinggreenurbanareasintowns
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