Abstract:<p>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 a tensor nuclear norm and a ℓ1-norm is employed to separate the low-rank hyperspectral image from the sparse noise. Tensor nuclear norm minimization is solved by iteratively performing tensor singular value thresholding (T-SVT). Howeve… Show more
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