SUPER-RESOLUTION RECONSTRUCTION OF HYPERSPECTRAL IMAGERY USING AN SPECTRAL UNMIXING BASED REPRESENTATIONAL MODEL

Efficient super-resolution of hyperspectral images (HSI) relies on the representational model (RM) that is capable of capturing the spatial and spectral correlation in hyperspectral images. In this paper, the spectral information in hyperspectral images is explained by linear spectral mixture model (LSMM), which expressed the observed pixels as a linear combination of endmembers, and the spatial information is captured by a spatial auto-regression model. The two component is combined in the maximum likelihood estimation (MLE) framework and solved by the expectation and maximization (EM) algorithm. Experiments on both simulated and real hyperspectral images demonstrate that the proposed method is not only capable of providing an accurate and effective super-resolution reconstruction of the image, but also capable of resisting the influence of noise.