IGSNRR OpenIR
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
Shi, Lingfei1,2; Ling, Feng1; Ge, Yong3; Foody, Giles M.4; Li, Xiaodong1; Wang, Lihui1; Zhang, Yihang1; Du, Yun1
2017-11-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume9Issue:11Pages:19
Corresponding AuthorLing, Feng(lingf@whigg.ac.cn)
AbstractDetailed 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.
KeywordLandsat support vector machine (SVM) impervious surface classification uncertainty uncertainty-based spatial-temporal consistency (USTC) model temporal consistency (TC) model
DOI10.3390/rs9111148
WOS KeywordSPECTRAL MIXTURE ANALYSIS ; RESOLUTION IMAGERY ; CELLULAR-AUTOMATA ; URBAN-DEVELOPMENT ; COMPOSITION INDEX ; SYNERGISTIC USE ; RIVER DELTA ; SOIL MODEL ; CLASSIFICATION ; DYNAMICS
Indexed BySCI
Language英语
Funding ProjectYouth Innovation Promotion Association CAS[2017384] ; Natural Science Foundation of China[61671425] ; State Key Laboratory of Resources and Environmental Informational System
Funding OrganizationYouth Innovation Promotion Association CAS ; Natural Science Foundation of China ; State Key Laboratory of Resources and Environmental Informational System
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000416554100063
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/56747
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLing, Feng
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Shi, Lingfei]'s Articles
[Ling, Feng]'s Articles
[Ge, Yong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Shi, Lingfei]'s Articles
[Ling, Feng]'s Articles
[Ge, Yong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Shi, Lingfei]'s Articles
[Ling, Feng]'s Articles
[Ge, Yong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.