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

Published in

IEEE Journal of Selected Topics in Applied Eart...(2020)

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TL;DR

A meta-analysis of 251 peer-reviewed journal papers relevant to remote sensing image classification and a comparative analysis regarding the performances of RF and SVM classification against various parameters is applied.

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.

Authors

Pedram Ghamisi

7 papers

M. Sheykhmousa

1 papers

M. Mahdianpari

1 papers

References158 items

Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran

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Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

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

Published in

IEEE Journal of Selected Topics in Applied Eart...(2020)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Pedram Ghamisi

7 papers

M. Sheykhmousa

1 papers

M. Mahdianpari

1 papers

References158 items

Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran

The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine

Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

H. Ghanbari

1 papers

F. Mohammadimanesh

1 papers

Saeid Homayouni

2 papers

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Alan-turinginstitute/MLJ.jl: v0.5.3 (Version v0.5.3). Zenodo

Field of Study

Computer Science

Journal Information

Name

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Page

6308-6325

Volume

Venue Information

Name

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Type

journal

URL

https://ieeexplore.ieee.org/servlet/opac?punumber=4609443

Alternate Names

IEEE J Sel Top Appl Earth Obs Remote Sens