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.
Highlights
SUPER-RESOLUTION mapping (SRM) is a process aiming to determine the spatial distribution of different land cover classes within mixed pixels
An endmember library is first constructed, and the optimal endmember combination for each land cover class is selected for each coarse resolution pixel with a certain criterion, such as root-mean-squared error (RMSE) [15], the spectral angle mapper (SAM) criterion [16], and spectral angle and spectral distance parameter [17]
Comparing these maps with the reference map, it was evident that the map produced by the optimal endmember based SRM (OESRM) method included more spatial detail and was visually closer to the reference map than the maps from the SMA_PS, MESMA_PS and SRM_LM
Summary
SUPER-RESOLUTION mapping (SRM) is a process aiming to determine the spatial distribution of different land cover classes within mixed pixels. Fraction based SRM is a method in which land cover fraction images are produced from the remotely sensed imagery by spectral unmixing and used as the input to the SRM analysis to estimate the fine spatial resolution land cover map. The aim of image based SRM models is the direct generation of a fine spatial resolution land cover map from coarse resolution remotely sensed imagery. An endmember library is first constructed, and the optimal endmember combination for each land cover class is selected for each coarse resolution pixel with a certain criterion, such as root-mean-squared error (RMSE) [15], the spectral angle mapper (SAM) criterion [16], and spectral angle and spectral distance parameter [17] This kind of method can, to a large extent, reduce the errors in spectral unmixing related to endmember variability. These two terms in the goal function are balanced by the parameter
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