Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/lgrs.2019.2894805 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Spatial resolution,Remote sensing,Uncertainty,Graphical models,Distribution functions,Indexes | Endmember,Remote sensing,Multispectral image,Spatial variability,Pixel,Graphical model,Land cover,Image resolution,Mathematics,Spatial distribution | Journal |
Volume | Issue | ISSN |
16 | 8 | 1545-598X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| XinYan Li | 1 | 3 | 5.17 |
| Xiaodong Li | 2 | 171 | 16.82 |
| Giles M. Foody | 3 | 374 | 39.06 |
| xiaohong yang | 4 | 0 | 2.03 |
| Yihang Zhang | 5 | 86 | 8.80 |
| Yun Du | 6 | 153 | 16.11 |
| Feng Ling | 7 | 209 | 21.29 |