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Spatial-temporal super-resolution land cover mapping with a local spatial-temporal dependence model

Li, Xiaodong; Ling, Feng; Foody, Giles M.; Ge, Yong; Zhang, Yihang; Wang, Lihui; Shi, Lingfei; Li, Xinyan; Du, Yun

Spatial-temporal super-resolution land cover mapping with a local spatial-temporal dependence model Thumbnail


Xiaodong Li

Feng Ling

Professor of Geographical Information

Yong Ge

Yihang Zhang

Lihui Wang

Lingfei Shi

Xinyan Li

Yun Du


The 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.

Journal Article Type Article
Acceptance Date Dec 14, 2018
Online Publication Date Mar 29, 2019
Publication Date Jul 1, 2019
Deposit Date Mar 7, 2019
Publicly Available Date Mar 7, 2019
Journal IEEE Transactions on Geoscience and Remote Sensing
Print ISSN 0196-2892
Electronic ISSN 1558-0644
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 57
Issue 7
Pages 4951-4966
Keywords Super-resolution mapping; Image series; Spatial dependence; Temporal dependence
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