Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review
Memorial University of Newfoundland · Centre For Cold Ocean Resources Engineering · +4 more institutions
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…
Citation impact
- FWCI
- 80.70
- Percentile
- 100%
- References
- 160
Authors
6- MSMohammadreza SheykhmousaCorresponding
- MMMasoud Mahdianpari
Memorial University of Newfoundland, Centre For Cold Ocean Resources Engineering
- HGHamid Ghanbari
Université Laval
- FMFariba Mohammadimanesh
Centre For Cold Ocean Resources Engineering
- PGPedram Ghamisi
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology
Topics & keywords
- Random forest
- Support vector machine
- Computer science
- Contextual image classification
- Remote sensing
- Artificial intelligence
- Pattern recognition (psychology)
- Statistical classification
- Life in Land