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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