Optimal endmember-based super-resolution land cover mapping

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

Abstract

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.

Citation

Li, X., Li, X., Foody, G., Yang, X., Zhang, Y., Du, Y., & Ling, F. (2019). Optimal endmember-based super-resolution land cover mapping. IEEE Geoscience and Remote Sensing Letters, 16(8), 1279-1283. https://doi.org/10.1109/lgrs.2019.2894805

• 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
• DOI: https://doi.org/10.1109/lgrs.2019.2894805
• Keywords: Spatial resolution , Remote sensing , Uncertainty , Graphical models , Distribution functions , Indexes
• Public URL: https://nottingham-repository.worktribe.com/output/1615691
• Publisher URL: https://ieeexplore.ieee.org/document/8651308
• Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
• Contract Date: Mar 7, 2019