Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy

The importance of Land Cover (LC) classification is recognized by an increasing number of scholars who employ LC information in various applications (i.e., address global climate change and achieve sustainable development). However, studying the roles of balancing data, image integration, and perfor...

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Autores principales:	Hamid Ebrahimy, Amin Naboureh, Bakhtiar Feizizadeh, Jagannath Aryal, Omid Ghorbanzadeh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Machine Learning (ML) Geometric Synthetic Minority Over-Sampling Technique (G-SMOTE) land cover mapping European Space Agency (ESA) class imbalance problem Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/53dd90d65c874a458964198a3cd3cb57
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spelling	oai:doaj.org-article:53dd90d65c874a458964198a3cd3cb572021-11-11T15:20:18ZIntegration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy10.3390/app1121103092076-3417https://doaj.org/article/53dd90d65c874a458964198a3cd3cb572021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10309https://doaj.org/toc/2076-3417The importance of Land Cover (LC) classification is recognized by an increasing number of scholars who employ LC information in various applications (i.e., address global climate change and achieve sustainable development). However, studying the roles of balancing data, image integration, and performance of different machine learning algorithms in various landscapes has not received as much attention from scientists. Therefore, the present study investigates the performance of three frequently used Machine Learning (ML) algorithms, including Extreme Learning Machines (ELM), Support Vector Machines (SVM), and Random Forest (RF) in LC mapping at six different landscapes. Moreover, the Geometric Synthetic Minority Over-sampling Technique (G-SMOTE) was adopted to deal with the class imbalance problem. In this work, the time-series of Sentinel-1 and Sentinel-2 data were integrated to improve LC mapping accuracy, taking advantage of both data. Moreover, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was implemented to distinguish the most informative features. Based on the results, the RF integrated with G-SMOTE showed the best result for four landscapes (coastal, cropland, desert, and semi-arid). SVM integrated with G-SMOTE had the highest accuracy in the remaining two landscapes (plain and mountain). Applied ML algorithms showed good performances in various landscapes, ranging Overall Accuracy (OA) from 85% to 93% for RF, 83% to 94% for SVM, and 84% to 92% for ELM. The outcomes exhibit that although applying G-SMOTE may slightly decrease OA values, it generally boosts the results of LC classification accuracies in various landscapes, particularly for minority classes.Hamid EbrahimyAmin NabourehBakhtiar FeizizadehJagannath AryalOmid GhorbanzadehMDPI AGarticleMachine Learning (ML)Geometric Synthetic Minority Over-Sampling Technique (G-SMOTE)land cover mappingEuropean Space Agency (ESA)class imbalance problemTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10309, p 10309 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine Learning (ML) Geometric Synthetic Minority Over-Sampling Technique (G-SMOTE) land cover mapping European Space Agency (ESA) class imbalance problem Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	Machine Learning (ML) Geometric Synthetic Minority Over-Sampling Technique (G-SMOTE) land cover mapping European Space Agency (ESA) class imbalance problem Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Hamid Ebrahimy Amin Naboureh Bakhtiar Feizizadeh Jagannath Aryal Omid Ghorbanzadeh Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy
description	The importance of Land Cover (LC) classification is recognized by an increasing number of scholars who employ LC information in various applications (i.e., address global climate change and achieve sustainable development). However, studying the roles of balancing data, image integration, and performance of different machine learning algorithms in various landscapes has not received as much attention from scientists. Therefore, the present study investigates the performance of three frequently used Machine Learning (ML) algorithms, including Extreme Learning Machines (ELM), Support Vector Machines (SVM), and Random Forest (RF) in LC mapping at six different landscapes. Moreover, the Geometric Synthetic Minority Over-sampling Technique (G-SMOTE) was adopted to deal with the class imbalance problem. In this work, the time-series of Sentinel-1 and Sentinel-2 data were integrated to improve LC mapping accuracy, taking advantage of both data. Moreover, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was implemented to distinguish the most informative features. Based on the results, the RF integrated with G-SMOTE showed the best result for four landscapes (coastal, cropland, desert, and semi-arid). SVM integrated with G-SMOTE had the highest accuracy in the remaining two landscapes (plain and mountain). Applied ML algorithms showed good performances in various landscapes, ranging Overall Accuracy (OA) from 85% to 93% for RF, 83% to 94% for SVM, and 84% to 92% for ELM. The outcomes exhibit that although applying G-SMOTE may slightly decrease OA values, it generally boosts the results of LC classification accuracies in various landscapes, particularly for minority classes.
format	article
author	Hamid Ebrahimy Amin Naboureh Bakhtiar Feizizadeh Jagannath Aryal Omid Ghorbanzadeh
author_facet	Hamid Ebrahimy Amin Naboureh Bakhtiar Feizizadeh Jagannath Aryal Omid Ghorbanzadeh
author_sort	Hamid Ebrahimy
title	Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy
title_short	Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy
title_full	Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy
title_fullStr	Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy
title_full_unstemmed	Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy
title_sort	integration of sentinel-1 and sentinel-2 data with the g-smote technique for boosting land cover classification accuracy
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/53dd90d65c874a458964198a3cd3cb57
work_keys_str_mv	AT hamidebrahimy integrationofsentinel1andsentinel2datawiththegsmotetechniqueforboostinglandcoverclassificationaccuracy AT aminnaboureh integrationofsentinel1andsentinel2datawiththegsmotetechniqueforboostinglandcoverclassificationaccuracy AT bakhtiarfeizizadeh integrationofsentinel1andsentinel2datawiththegsmotetechniqueforboostinglandcoverclassificationaccuracy AT jagannatharyal integrationofsentinel1andsentinel2datawiththegsmotetechniqueforboostinglandcoverclassificationaccuracy AT omidghorbanzadeh integrationofsentinel1andsentinel2datawiththegsmotetechniqueforboostinglandcoverclassificationaccuracy
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Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy

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