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Spatial-Temporal Super-Resolution Land Cover Mapping With a Local Spatial-Temporal Dependence Model
Li, Xiaodong1,2,3; Ling, Feng1,2; Foody, Giles M.3; Ge, Yong4; Zhang, Yihang1,2; Wang, Lihui1,2; Shi, Lingfei1,2; Li, Xinyan1,2; Du, Yun1,2
2019-07-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume57Issue:7Pages:4951-4966
Corresponding AuthorLing, Feng(lingf@whigg.ac.cn)
AbstractThe mixed pixel problem is common in remote sensing. A soft classification can generate land cover class fraction images that illustrate the areal proportions of the various land cover classes within pixels. The spatial distribution of land cover classes within each mixed pixel is, however, not represented. Super-resolution land cover mapping (SRM) is a technique to predict the spatial distribution of land cover classes within the mixed pixel using fraction images as input. Spatial-temporal SRM (STSRM) extends the basic SRM to include a temporal dimension by using a finer-spatial resolution land cover map that pre- or postdates the image acquisition time as ancillary data. Traditional STSRM methods often use one land cover map as the constraint, but neglect the majority of available land cover maps acquired at different dates and of the same scene in reconstructing a full state trajectory of land cover changes when applying STSRM to time-series data. In addition, the STSRM methods define the temporal dependence globally, and neglect the spatial variation of land cover temporal dependence intensity within images. A novel local STSRM (LSTSRM) is proposed in this paper. LSTSRM incorporates more than one available land cover map to constrain the solution, and develops a local temporal dependence model, in which the temporal dependence intensity may vary spatially. The results show that LSTSRM can eliminate speckle-like artifacts and reconstruct the spatial patterns of land cover patches in the resulting maps, and increase the overall accuracy compared with other STSRM methods.
KeywordImage series spatial dependence super-resolution mapping (SRM) temporal dependence
DOI10.1109/TGRS.2019.2894773
WOS KeywordHOPFIELD NEURAL-NETWORK ; REMOTELY-SENSED IMAGES ; FOREST COVER ; TIME-SERIES ; MODIS ; ALGORITHM ; SCALE ; REFLECTANCE ; MAPS
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of Chinese Academy of Sciences (CAS)[XDA 2003030201] ; Youth Innovation Promotion Association CAS[2017384] ; Natural Science Foundation of China[61671425] ; Natural Science Foundation of China[51809250] ; Hubei Province Natural Science Fund for Distinguished Young Scholars[2018CFA062] ; British Academy's Visiting Fellowships Programme under the U.K. Government's Rutherford Fund
Funding OrganizationStrategic Priority Research Program of Chinese Academy of Sciences (CAS) ; Youth Innovation Promotion Association CAS ; Natural Science Foundation of China ; Hubei Province Natural Science Fund for Distinguished Young Scholars ; British Academy's Visiting Fellowships Programme under the U.K. Government's Rutherford Fund
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000473436000062
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/58516
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLing, Feng
Affiliation1.Chinese Acad Sci, Key Lab Monitoring & Estimate Environm & Disaster, Inst Geodesy & Geophys, Wuhan 430077, Hubei, Peoples R China
2.Chinese Acad Sci, Sinoafrica Joint Res Ctr, Wuhan 430074, Hubei, Peoples R China
3.Univ Nottingham, Sch Geog, Univ Pk, Nottingham NG7 2RD, England
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Li, Xiaodong,Ling, Feng,Foody, Giles M.,et al. Spatial-Temporal Super-Resolution Land Cover Mapping With a Local Spatial-Temporal Dependence Model[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2019,57(7):4951-4966.
APA Li, Xiaodong.,Ling, Feng.,Foody, Giles M..,Ge, Yong.,Zhang, Yihang.,...&Du, Yun.(2019).Spatial-Temporal Super-Resolution Land Cover Mapping With a Local Spatial-Temporal Dependence Model.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,57(7),4951-4966.
MLA Li, Xiaodong,et al."Spatial-Temporal Super-Resolution Land Cover Mapping With a Local Spatial-Temporal Dependence Model".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 57.7(2019):4951-4966.
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