most citedUsing Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing

88 citations · 88 across the 1 of their papers we have counts for

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1 paper

cs.CV2021★ 88 cited

Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing

Lianru Gao, Zhicheng Wang, Lina Zhuang +3

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-ord…