Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image classification in several remote sensing application...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 13; S. 6308 - 6325
Hauptverfasser:	Sheykhmousa, Mohammadreza, Mahdianpari, Masoud, Ghanbari, Hamid, Mohammadimanesh, Fariba, Ghamisi, Pedram, Homayouni, Saeid
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Analysis Classification Classification algorithms Comparative analysis Deep learning Feature extraction Geographical distribution Hyperspectral sensors Image classification Image processing Learning algorithms Machine learning Meta-analysis Qualitative analysis Radio frequency random forest (RF) Remote sensing remote sensing (RS) Spatial discrimination Spatial resolution support vector machine (SVM) Support vector machines
ISSN:	1939-1404, 2151-1535
Online-Zugang:	Volltext
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Abstract	Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image classification in several remote sensing applications. This article reviews RF and SVM concepts relevant to remote sensing image classification and applies a meta-analysis of 251 peer-reviewed journal papers. A database with more than 40 quantitative and qualitative fields was constructed from these reviewed papers. The meta-analysis mainly focuses on 1) the analysis regarding the general characteristics of the studies, such as geographical distribution, frequency of the papers considering time, journals, application domains, and remote sensing software packages used in the case studies, and 2) a comparative analysis regarding the performances of RF and SVM classification against various parameters, such as data type, RS applications, spatial resolution, and the number of extracted features in the feature engineering step. The challenges, recommendations, and potential directions for future research are also discussed in detail. Moreover, a summary of the results is provided to aid researchers to customize their efforts in order to achieve the most accurate results based on their thematic applications.
AbstractList	Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image classification in several remote sensing applications. This article reviews RF and SVM concepts relevant to remote sensing image classification and applies a meta-analysis of 251 peer-reviewed journal papers. A database with more than 40 quantitative and qualitative fields was constructed from these reviewed papers. The meta-analysis mainly focuses on 1) the analysis regarding the general characteristics of the studies, such as geographical distribution, frequency of the papers considering time, journals, application domains, and remote sensing software packages used in the case studies, and 2) a comparative analysis regarding the performances of RF and SVM classification against various parameters, such as data type, RS applications, spatial resolution, and the number of extracted features in the feature engineering step. The challenges, recommendations, and potential directions for future research are also discussed in detail. Moreover, a summary of the results is provided to aid researchers to customize their efforts in order to achieve the most accurate results based on their thematic applications.
Author	Homayouni, Saeid Ghanbari, Hamid Ghamisi, Pedram Mahdianpari, Masoud Mohammadimanesh, Fariba Sheykhmousa, Mohammadreza
Author_xml	– sequence: 1 givenname: Mohammadreza orcidid: 0000-0002-3673-7544 surname: Sheykhmousa fullname: Sheykhmousa, Mohammadreza email: mohammadreza.sheykhmousa@opengeohub.org organization: OpenGeoHub, Wageningen, The Netherlands – sequence: 2 givenname: Masoud orcidid: 0000-0002-7234-959X surname: Mahdianpari fullname: Mahdianpari, Masoud email: m.mahdianpari@mun.ca organization: C-CORE, St. John's, NL, Canada – sequence: 3 givenname: Hamid orcidid: 0000-0002-9557-495X surname: Ghanbari fullname: Ghanbari, Hamid email: hamid.ghanbari.1@ulaval.ca organization: Department of Geography, Université Laval, Québec, QC, Canada – sequence: 4 givenname: Fariba surname: Mohammadimanesh fullname: Mohammadimanesh, Fariba email: fariba.mohammadimanesh@c-core.ca organization: C-CORE, St. John's, NL, Canada – sequence: 5 givenname: Pedram orcidid: 0000-0003-1203-741X surname: Ghamisi fullname: Ghamisi, Pedram email: p.ghamisi@gmail.com organization: Division of “Exploration Technology,” Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany – sequence: 6 givenname: Saeid orcidid: 0000-0002-0214-5356 surname: Homayouni fullname: Homayouni, Saeid email: saeid.homayouni@ete.inrs.ca organization: Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Quebec City, QC, Canada
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Snippet	Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning...
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SubjectTerms	Algorithms Analysis Classification Classification algorithms Comparative analysis Deep learning Feature extraction Geographical distribution Hyperspectral sensors Image classification Image processing Learning algorithms Machine learning Meta-analysis Qualitative analysis Radio frequency random forest (RF) Remote sensing remote sensing (RS) Spatial discrimination Spatial resolution support vector machine (SVM) Support vector machines
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Title	Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review
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