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RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine

文献类型：期刊论文

作者Naboureh, Amin1,4; Ebrahimy, Hamid2; Azadbakht, Mohsen2; Bian, Jinhu1; Amani, Meisam3
刊名REMOTE SENSING
出版日期2020
卷号12期号:21页码:140262
关键词Google Earth Engine Sentinel-2 land cover mapping support vector machine imbalanced data
DOI10.1016/j.scitotenv.2020.140262
产权排序1
通讯作者Bian, Jinhu(bianjinhu@imde.ac.cn)
文献子类Article
英文摘要Timely and accurate Land Cover (LC) information is required for various applications, such as climate change analysis and sustainable development. Although machine learning algorithms are most likely successful in LC mapping tasks, the class imbalance problem is known as a common challenge in this regard. This problem occurs during the training phase and reduces classification accuracy for infrequent and rare LC classes. To address this issue, this study proposes a new method by integrating random under-sampling of majority classes and an ensemble of Support Vector Machines, namely Random Under-sampling Ensemble of Support Vector Machines (RUESVMs). The performance of RUESVMs for LC classification was evaluated in Google Earth Engine (GEE) over two different case studies using Sentinel-2 time-series data and five well-known spectral indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The performance of RUESVMs was also compared with the traditional SVM and combination of SVM with three benchmark data balancing techniques namely the Random Over-Sampling (ROS), Random Under-Sampling (RUS), and Synthetic Minority Over-sampling Technique (SMOTE). It was observed that the proposed method considerably improved the accuracy of LC classification, especially for the minority classes. After adopting RUESVMs, the overall accuracy of the generated LC map increased by approximately 4.95 percentage points, and this amount for the geometric mean of producer's accuracies was almost 3.75 percentage points, in comparison to the most accurate data balancing method (i.e., SVM-SMOTE). Regarding the geometric mean of users' accuracies, RUESVMs also outperformed the SVM-SMOTE method with an average increase of 6.45 percentage points.
电子版国际标准刊号1879-1026
WOS关键词RANDOM FOREST ; CLASSIFICATION ; INDEX ; SMOTE ; PERFORMANCE ; SENTINEL-2 ; VEGETATION ; IMAGES ; AREAS ; MAP
资助项目National Key Research and Development Program of China[2016YFC0500201-06] ; National Natural Science Foundation project of China[41701432] ; National Natural Science Foundation project of China[41631180] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19030303] ; CAS Light of West China Program ; Youth program of Institute of Mountain Hazards and Environment, CAS[SDS-QN-1902] ; Youth Innovation Promotion Association CAS[2019365] ; CAS-TWAS president's fellowship for international doctoral students
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000568815100006
出版者MDPI
资助机构National Key Research and Development Program of China ; National Natural Science Foundation project of China ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) ; CAS Light of West China Program ; Youth program of Institute of Mountain Hazards and Environment, CAS ; Youth Innovation Promotion Association CAS ; CAS-TWAS president's fellowship for international doctoral students
源URL[http://ir.imde.ac.cn/handle/131551/50736]  
专题成都山地灾害与环境研究所_数字山地与遥感应用中心
通讯作者Bian, Jinhu
作者单位1.Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China;
2.Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran 19839 69411, Iran;
3.Wood Environment & Infrastructure Solutions, Ottawa, ON K2E 7K3, Canada
4.University of Chinese Academy of Sciences, Beijing 100049, China;
推荐引用方式
GB/T 7714		 Naboureh, Amin,Ebrahimy, Hamid,Azadbakht, Mohsen,et al. RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine[J]. REMOTE SENSING,2020,12(21):140262.
APA Naboureh, Amin,Ebrahimy, Hamid,Azadbakht, Mohsen,Bian, Jinhu,&Amani, Meisam.(2020).RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine.REMOTE SENSING,12(21),140262.
MLA Naboureh, Amin,et al."RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine".REMOTE SENSING 12.21(2020):140262.

入库方式： OAI收割

来源：成都山地灾害与环境研究所

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