Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery

Hyperspectral imagery contains a large number of mixed pixels, which limits its utility. Super-resolution mapping is a potential solution to this problem, designed to use the proportion of land covers to obtain a sharpened thematic map with higher resolution. Endmember is a fundamental variable in the process, which is a critical issue for decomposing the mixed pixels and sharpening the subpixel level images. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional soft classification methods neglect this point and model endmembers as fixed composition entities. Due to the reliance on this flawed spectral mixture model, the super-resolution mapping is unable to represent detail in the following result image precisely and effectively. In this work, therefore, endmember variability is considered, focusing on identifying the most suitable form of the endmember combination. This issue is addressed by applying a new selective endmember spectral mixture (SESM) model, which allows the endmember number and type to vary at a per pixel level, and then super-resolution mapping can be subsequently performed according to the produced spectral abundances. Two different types of hyperspectral data are used in our experiments. First, the SESM model is tested individually for validation of its applicability. Then the complete algorithm integrating SESM and super-resolution mapping based on a back-propagation neural network is evaluated. It showed that a more accurate endmember combination in the parent pixel results in a finer representation image. The experimental results prove that the proposed algorithm can effectively improve the accuracy of the super-resolution mapping results compared to the traditional method.

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