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Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016

文献类型：期刊论文

作者Shi, Lingfei1,2; Ling, Feng1; Ge, Yong3; Foody, Giles M.4; Li, Xiaodong1; Wang, Lihui1; Zhang, Yihang1; Du, Yun1
刊名REMOTE SENSING
出版日期2017-11-01
卷号9期号:11页码:19
ISSN号2072-4292
关键词Landsat support vector machine (SVM) impervious surface classification uncertainty uncertainty-based spatial-temporal consistency (USTC) model temporal consistency (TC) model
DOI10.3390/rs9111148
通讯作者Ling, Feng(lingf@whigg.ac.cn)
英文摘要Detailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial and temporal resolution. To improve the classification accuracy, a temporal consistency (TC) model may be applied on the original classification results of Landsat time-series datasets. However, existing TC models only use class labels, and ignore the uncertainty of classification during the process. In this study, an uncertainty-based spatial-temporal consistency (USTC) model was proposed to improve the accuracy of the long time series of impervious surface classifications. In contrast to existing TC methods, the proposed USTC model integrates classification uncertainty with the spatial-temporal context information to better describe the spatial-temporal consistency for the long time-series datasets. The proposed USTC model was used to obtain an annual map of impervious surfaces in Wuhan city with Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images from 1987 to 2016. The impervious surfaces mapped by the proposed USTC model were compared with those produced by the support vector machine (SVM) classifier and the TC model. The accuracy comparison of these results indicated that the proposed USTC model had the best performance in terms of classification accuracy. The increase of overall accuracy was about 4.23% compared with the SVM classifier, and about 1.79% compared with the TC model, which indicates the effectiveness of the proposed USTC model in mapping impervious surfaces from long-term Landsat sensor imagery.
WOS关键词SPECTRAL MIXTURE ANALYSIS ; RESOLUTION IMAGERY ; CELLULAR-AUTOMATA ; URBAN-DEVELOPMENT ; COMPOSITION INDEX ; SYNERGISTIC USE ; RIVER DELTA ; SOIL MODEL ; CLASSIFICATION ; DYNAMICS
资助项目Youth Innovation Promotion Association CAS[2017384] ; Natural Science Foundation of China[61671425] ; State Key Laboratory of Resources and Environmental Informational System
WOS研究方向Remote Sensing
语种英语
出版者MDPI AG
WOS记录号WOS:000416554100063
资助机构Youth Innovation Promotion Association CAS ; Natural Science Foundation of China ; State Key Laboratory of Resources and Environmental Informational System
源URL[http://ir.igsnrr.ac.cn/handle/311030/56746]  
专题中国科学院地理科学与资源研究所
通讯作者Ling, Feng
作者单位1.Chinese Acad Sci, Inst Geodesy & Geophys, Wuhan 430077, Hubei, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
4.Univ Nottingham, Sch Geog, Univ Pk, Nottingham NG7 2RD, England
推荐引用方式
GB/T 7714		 Shi, Lingfei,Ling, Feng,Ge, Yong,et al. Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016[J]. REMOTE SENSING,2017,9(11):19.
APA Shi, Lingfei.,Ling, Feng.,Ge, Yong.,Foody, Giles M..,Li, Xiaodong.,...&Du, Yun.(2017).Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016.REMOTE SENSING,9(11),19.
MLA Shi, Lingfei,et al."Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016".REMOTE SENSING 9.11(2017):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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