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Optimal endmember-based super-resolution land cover mapping

Li, Xinyan; Li, Xiaodong; Foody, Giles; Yang, Xiaohong; Zhang, Yihang; Du, Yun; Ling, Feng

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Xinyan Li

Xiaodong Li

Professor of Geographical Information

Xiaohong Yang

Yihang Zhang

Yun Du

Feng Ling


Super-resolution mapping (SRM) aims to determine the spatial distribution of the land cover classes contained in the area represented by mixed pixels to obtain a more appropriate and accurate map at a finer spatial resolution than the input remotely sensed image. The image-based SRM models directly use the observed images as input and can mitigate the uncertainty caused by class fraction errors. However, existing image-based SRM models always adopt a fixed set of endmembers used in the entire image, ignoring the spatial variability and spectral uncertainty of endmembers. To address this problem, this letter proposed an optimal endmember-based SRM (OESRM) model, which considers the spatial variations in endmembers, and determines the best-fit one for each coarse resolution pixel using the spectral angle and the spectral distance as the spectral similarity indexes. A Sentinel-2A and a Landsat-8 multispectral images were used to analyze the performance of OESRM, by comparing with three other SRM methods which adopt a fixed endmember set or multiple endmember sets. The results showed that OESRM generated resultant land cover maps with more spatial detail, and reduced the confusion between land cover classes with similar spectral features. The proposed OESRM model produced the results with the highest overall accuracy in both experiments, showing its effectiveness in reducing the effect of endmember uncertainty on SRM.

Journal Article Type Article
Acceptance Date Jan 12, 2019
Online Publication Date Feb 25, 2019
Publication Date 2019-08
Deposit Date Mar 7, 2019
Publicly Available Date Mar 7, 2019
Journal IEEE Geoscience and Remote Sensing Letters
Print ISSN 1545-598X
Electronic ISSN 1558-0571
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 16
Issue 8
Pages 1279-1283
Keywords Spatial resolution , Remote sensing , Uncertainty , Graphical models , Distribution functions , Indexes
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