Using Low-Rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing
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DOI: 10.1109/tgrs.2021.3065990
Abstract: Tensor-based methods have been widely studied to attack inverse problems in hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented as a third-order tensor, which can perfectly retain the spatial information in… read more here.
Keywords: abundance maps; low rank; nonlinear unmixing; tensor ... See more keywords