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super resolution of hyperspectral images
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Remote sensing hyperspectral images (HSIs) are
quite often low rank, in the sense that the data belong to
a low dimensional subspace/manifold. This has been recently
exploited for the fusion of low spatial resolution HSI with
high spatial resolution multispectral images in order to obtain
super-resolution HSI. Most approaches adopt an unmixing or
a matrix factorization perspective. The derived methods have
led to state-of-the-art results when the spectral information
lies in a low-dimensional subspace/manifold. However, if the
subspace/manifol
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