干旱区生态环境知识资源中心(Arid): Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy

Arid

DOI	10.3390/app112110309
	Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy
	Ebrahimy, Hamid; Naboureh, Amin; Feizizadeh, Bakhtiar; Aryal, Jagannath; Ghorbanzadeh, Omid
通讯作者	Ghorbanzadeh, O (corresponding author), Inst Adv Res Artificial Intelligence IARAI, Landstr Hauptstr 5, A-1030 Vienna, Austria.
来源期刊	APPLIED SCIENCES-BASEL
EISSN	2076-3417
出版年	2021
卷号	11 期号:21
英文摘要	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.
英文关键词	Machine Learning (ML) Geometric Synthetic Minority Over-Sampling Technique (G-SMOTE) land cover mapping European Space Agency (ESA) class imbalance problem
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000726470700001
WOS关键词	RANDOM FOREST ; PERFORMANCE ; MACHINE ; SELECTION ; PALSAR ; MODIS ; AREA
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/373725
作者单位	[Ebrahimy, Hamid] Shahid Beheshti Univ, Remote Sensing & GIS Res Ctr, Fac Earth Sci, Tehran 653641255, Iran; [Naboureh, Amin] Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Peoples R China; [Naboureh, Amin] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Feizizadeh, Bakhtiar] Univ Tabriz, Dept Remote Sensing & GIS, Tabriz 51666, Iran; [Feizizadeh, Bakhtiar] Humboldt Univ, Dept Geog, D-12489 Berlin, Germany; [Aryal, Jagannath] Univ Melbourne, Fac Engn & IT, Dept Infrastruct Engn, Melbourne, Vic 3010, Australia; [Ghorbanzadeh, Omid] Inst Adv Res Artificial Intelligence IARAI, Landstr Hauptstr 5, A-1030 Vienna, Austria
推荐引用方式 GB/T 7714	Ebrahimy, Hamid,Naboureh, Amin,Feizizadeh, Bakhtiar,et al. Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy[J],2021,11(21).
APA	Ebrahimy, Hamid,Naboureh, Amin,Feizizadeh, Bakhtiar,Aryal, Jagannath,&Ghorbanzadeh, Omid.(2021).Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy.APPLIED SCIENCES-BASEL,11(21).
MLA	Ebrahimy, Hamid,et al."Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy".APPLIED SCIENCES-BASEL 11.21(2021).