Low-Rank Tensor Decomposition With Smooth and Sparse Regularization for Hyperspectral and Multispectral Data Fusion

Low-Rank Tensor Decomposition With Smooth and Sparse Regularization for Hyperspectral and Multispectral Data Fusion

The fusion of hyperspectral and multispectral images is an effective way to obtain hyperspectral super-resolution images with high spatial resolution. A hyperspectral image is a datacube containing two spatial dimensions and a spectral dimension. The fusion methods based on non-negative matrix facto...

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Bibliographic Details
Main Authors: Fei Ma, Feixia Yang, Yanwei Wang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Remote sensing
image fusion
low-rank tensor decomposition
total variation
super-resolution
Online Access:https://ieeexplore.ieee.org/document/9139926/

Internet

https://ieeexplore.ieee.org/document/9139926/

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