Randomized Tensor Robust PCA for Noisy Hyperspectral Image Classification
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DOI: 10.1109/lgrs.2023.3251861
Abstract: Removing noise from hyperspectral images can be very beneficial for improving classification accuracy. Recently, tensor robust principal component analysis (TRPCA) has been successfully employed to reduce noise in hyperspectral images. In TRPCA, a minimization involving… read more here.
Keywords: tensor; classification; tensor nuclear; tensor robust ... See more keywords