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An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images

文献类型：期刊论文

作者Sun, Fei4; Fang, Fang5; Wang, Run1; Wan, Bo5; Guo, Qinghua2; Li, Hong5; Wu, Xincai5
刊名SENSORS
出版日期2020
卷号20期号:22
关键词image classification class imbalance impartial semi-supervised learning strategy (ISS) extreme gradient boosting (XGB) very-high-resolution (VHR)
DOI10.3390/s20226699
文献子类Article
英文摘要Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing.
学科主题Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
出版地BASEL
电子版国际标准刊号1424-8220
WOS关键词RANDOM FOREST ; MACHINE ; SMOTE ; PERFORMANCE ; CHALLENGES ; DIVERSITY ; ALGORITHM
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000594558300001
出版者MDPI
资助机构Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education [GLAB2019ZR14] ; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [26420190051]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/21507]  
专题植被与环境变化国家重点实验室
作者单位1.China Univ Geosci, Natl Engn Res Ctr Geog Informat Syst, Wuhan 430078, Peoples R China
2.China Univ Geosci, OfMinistry Educ, Key Lab Geol Survey & Evaluat, Wuhan 430078, Peoples R China
3.Chinese Acad Sci, State Key Lab Vegetat & Environm Change, Inst Bot, Beijing 100093, Peoples R China
4.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430078, Peoples R China
5.Huanggang Normal Univ, Acad Comp, 146 Xinggang 2nd Rd, Huanggang 438000, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Fei,Fang, Fang,Wang, Run,et al. An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images[J]. SENSORS,2020,20(22).
APA Sun, Fei.,Fang, Fang.,Wang, Run.,Wan, Bo.,Guo, Qinghua.,...&Wu, Xincai.(2020).An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images.SENSORS,20(22).
MLA Sun, Fei,et al."An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images".SENSORS 20.22(2020).

入库方式： OAI收割

来源：植物研究所

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