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Hyperspectral Image Super-Resolution via Subspace-Based Low Tensor Multi-Rank Regularization | IEEE Journals & Magazine | IEEE Xplore

Hyperspectral Image Super-Resolution via Subspace-Based Low Tensor Multi-Rank Regularization


Abstract:

Recently, combining a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) into an HR-HSI has become a popular ...Show More

Abstract:

Recently, combining a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) into an HR-HSI has become a popular scheme to enhance the spatial resolution of HSI. We propose a novel subspace-based low tensor multi-rank regularization method for the fusion, which fully exploits the spectral correlations and non-local similarities in the HR-HSI. To make use of high spectral correlations, the HR-HSI is approximated by spectral subspace and coefficients. We first learn the spectral subspace from the LR-HSI via singular value decomposition, and then estimate the coefficients via the low tensor multi-rank prior. More specifically, based on the learned cluster structure in the HR-MSI, the patches in coefficients are grouped. We collect the coefficients in the same cluster into a three-dimensional tensor and impose the low tensor multi-rank prior on these collected tensors, which fully model the non-local self-similarities in the HR-HSI. The coefficients optimization is solved by the alternating direction method of multipliers. Experiments on two public HSI datasets demonstrate the advantages of our method.
Published in: IEEE Transactions on Image Processing ( Volume: 28, Issue: 10, October 2019)
Page(s): 5135 - 5146
Date of Publication: 20 May 2019

ISSN Information:

PubMed ID: 31107646

Funding Agency:


References

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