A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions
文献类型：期刊论文
| 作者 | Naboureh, Amin1,2; Li, Ainong2 ; Bian, Jinhu2; Lei, Guangbin2; Amani, Meisam3
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020 |
| 卷号 | 12期号:20页码:3301 |
| 关键词 | class imbalance problem Google Earth Engine land cover mapping mountainous regions time-series of Landsat |
| DOI | 10.3390/rs12203301 |
| 产权排序 | 1 |
| 通讯作者 | Li, Ainong(ainongli@imde.ac.cn) |
| 文献子类 | Article |
| 英文摘要 | Distribution of Land Cover (LC) classes is mostly imbalanced with some majority LC classes dominating against minority classes in mountainous areas. Although standard Machine Learning (ML) classifiers can achieve high accuracies for majority classes, they largely fail to provide reasonable accuracies for minority classes. This is mainly due to the class imbalance problem. In this study, a hybrid data balancing method, called the Partial Random Over-Sampling and Random Under-Sampling (PROSRUS), was proposed to resolve the class imbalance issue. Unlike most data balancing techniques which seek to fully balance datasets, PROSRUS uses a partial balancing approach with hundreds of fractions for majority and minority classes to balance datasets. For this, time-series of Landsat-8 and SRTM topographic data along with various spectral indices and topographic data were used over three mountainous sites within the Google Earth Engine (GEE) cloud platform. It was observed that PROSRUS had better performance than several other balancing methods and increased the accuracy of minority classes without a reduction in overall classification accuracy. Furthermore, adopting complementary information, particularly topographic data, considerably increased the accuracy of minority classes in mountainous areas. Finally, the obtained results from PROSRUS indicated that every imbalanced dataset requires a specific fraction(s) for addressing the class imbalance problem, because different datasets contain various characteristics. |
| 电子版国际标准刊号 | 2072-4292 |
| WOS关键词 | LAND-COVER CLASSIFICATION ; RANDOM FORESTS ; TIME-SERIES ; INDEX ; IMAGES ; AREAS ; SMOTE ; MAP |
| 资助项目 | strategic priority research program of the Chinese Academy of Science (CAS)[XDA19030303] ; national natural science foundation project of china[41631180] ; national natural science foundation project of china[41701432] ; national natural science foundation project of china[41571373] ; national key research and development program of China[2016YFA0600103] ; national key research and development program of China[2016YFC0500201-06] ; CAS light of west China program ; youth innovation promotion association CAS[2019365] ; CAS-TWAS |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000585563400001 |
| 出版者 | MDPI |
| 资助机构 | strategic priority research program of the Chinese Academy of Science (CAS) ; national natural science foundation project of china ; national key research and development program of China ; CAS light of west China program ; youth innovation promotion association CAS ; CAS-TWAS |
| 源URL | [http://ir.imde.ac.cn/handle/131551/50744]  |
| 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 | Li, Ainong |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China; 2.Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041; 3.Wood Environment & Infrastructure Solutions, Ottawa, ON K2E 7K3, Canada |
| 推荐引用方式 GB/T 7714 | Naboureh, Amin,Li, Ainong,Bian, Jinhu,et al. A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions[J]. REMOTE SENSING,2020,12(20):3301. |
| APA | Naboureh, Amin,Li, Ainong,Bian, Jinhu,Lei, Guangbin,&Amani, Meisam.(2020).A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions.REMOTE SENSING,12(20),3301. |
| MLA | Naboureh, Amin,et al."A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions".REMOTE SENSING 12.20(2020):3301. |
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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