Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ att...

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Autores principales:	Hamid Jafarzadeh, Masoud Mahdianpari, Eric Gill, Fariba Mohammadimanesh, Saeid Homayouni
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	classification ensemble classifier bagging boosting multispectral hyperspectral Science Q
Acceso en línea:	https://doaj.org/article/be0aeffa2aae4a19b80dce589367d7a7
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spelling	oai:doaj.org-article:be0aeffa2aae4a19b80dce589367d7a72021-11-11T18:55:43ZBagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation10.3390/rs132144052072-4292https://doaj.org/article/be0aeffa2aae4a19b80dce589367d7a72021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4405https://doaj.org/toc/2072-4292In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data.Hamid JafarzadehMasoud MahdianpariEric GillFariba MohammadimaneshSaeid HomayouniMDPI AGarticleclassificationensemble classifierbaggingboostingmultispectralhyperspectralScienceQENRemote Sensing, Vol 13, Iss 4405, p 4405 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	classification ensemble classifier bagging boosting multispectral hyperspectral Science Q
spellingShingle	classification ensemble classifier bagging boosting multispectral hyperspectral Science Q Hamid Jafarzadeh Masoud Mahdianpari Eric Gill Fariba Mohammadimanesh Saeid Homayouni Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
description	In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data.
format	article
author	Hamid Jafarzadeh Masoud Mahdianpari Eric Gill Fariba Mohammadimanesh Saeid Homayouni
author_facet	Hamid Jafarzadeh Masoud Mahdianpari Eric Gill Fariba Mohammadimanesh Saeid Homayouni
author_sort	Hamid Jafarzadeh
title	Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
title_short	Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
title_full	Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
title_fullStr	Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
title_full_unstemmed	Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
title_sort	bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and polsar data: a comparative evaluation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/be0aeffa2aae4a19b80dce589367d7a7
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Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

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