Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning

Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning

In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method. The proposed method exploits spatial and spectral correlations i...

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Bibliographic Details
Main Authors: Yanbin Zhang, Long-Ting Huang, Yangqing Li, Kai Zhang, Changchuan Yin
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Sensors
Subjects:
hyperspectral images
hyperspectral remoting sensing
Bayesian learning
compressive sensing
low-rank and joint-sparse
Online Access:https://www.mdpi.com/1424-8220/22/1/343

Internet

https://www.mdpi.com/1424-8220/22/1/343

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