Abstract:Sparse unmixing of hyperspectral images aims to separate the endmembers and
estimate the abundances of mixed pixels. This approach is the essential
step for many applications involving hyperspectral images. The multi scale
spatial sparse hyperspectral unmixing algorithm (MUA) could achieve higher
accuracy than many state-of-the-art algorithms. The regularization
parameters, whose combinations markedly influence the unmixing accuracy, are
determined by manually searching in the broad para… Show more
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