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IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium by Zeng, Haijin (author);Xie, Xiaozhen (author);Ning, Jifeng (author) is a nonfiction available to read on EtoBox.

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Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs). Conventionally, the rank of LR matrix is approximated using nuclear norm (NN). The NN is defined by adding all singular values together, which is essentially a L 1 -norm of the singular values. It results in non-negligible approximation errors and thus the resulting matrix estimator can be significantly biased. Moreover, these bandwise TV-based methods exploit the spatial information in a separate manner. To cope with these problems, we propose a spatial-spectral TV (SSTV) regularized non-convex local LR matrix approximation (NonLLRTV) method to remove mixed noise in HSIs. From one aspect, local LR of HSIs is formulated using a non-convex L γ -norm, which provides a closer approximation to the matrix rank than the traditional NN. From another aspect, HSIs are assumed to be piecewisely smooth in the global spatial domain. The TV regularization is effective in preserving the smoothness and removing Gaussian noise. These facts inspire the integration of the NonLLR with TV regularization. To address the limitations of bandwise TV, we use

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Author

Zeng, Haijin (author);Xie, Xiaozhen (author);Ning, Jifeng (author)

Publisher

IEEE

Published

2020

Language

EN

Category

nonfiction