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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Detalles Bibliográficos
Autores principales: Zhang, Yanbin, Huang, Long-Ting, Li, Yangqing, Zhang, Kai, Yin, Changchuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749654/
https://www.ncbi.nlm.nih.gov/pubmed/35009885
http://dx.doi.org/10.3390/s22010343

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749654/
https://www.ncbi.nlm.nih.gov/pubmed/35009885
http://dx.doi.org/10.3390/s22010343

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