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

Zhang, Yanbin; Huang, Long-Ting; Li, Yangqing; Zhang, Kai; Yin, Changchuan

doi:10.3390/s22010343

Open AccessArticle

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

by

Yanbin Zhang

^1,2,*

,

Long-Ting Huang

³,

Yangqing Li

¹,

Kai Zhang

¹

and

Changchuan Yin

¹

Beijing Laboratory of Advanced Information Networks, Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China

²

China Fire and Rescue Institute, Beijing 102202, China

³

School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China

^*

Author to whom correspondence should be addressed.

Sensors 2022, 22(1), 343; https://doi.org/10.3390/s22010343

Submission received: 3 October 2021 / Revised: 20 December 2021 / Accepted: 28 December 2021 / Published: 4 January 2022

(This article belongs to the Special Issue Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention)

Download

Browse Figures

Versions Notes

Abstract

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 in HSI data using sparse Bayesian learning and compressive sensing (CS). By utilizing a simultaneously L&S data model, we employ the information of the principal components and Bayesian learning to reconstruct the hyperspectral images. The simulation results demonstrate that the proposed method is superior to LRMR and SS&LR methods in terms of reconstruction accuracy and computational burden under the same signal-to-noise tatio (SNR) and compression ratio.

Keywords: hyperspectral images; hyperspectral remoting sensing; Bayesian learning; compressive sensing; low-rank and joint-sparse

Share and Cite

MDPI and ACS Style

Zhang, Y.; Huang, L.-T.; Li, Y.; Zhang, K.; Yin, C. Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning. Sensors 2022, 22, 343. https://doi.org/10.3390/s22010343

AMA Style

Zhang Y, Huang L-T, Li Y, Zhang K, Yin C. Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning. Sensors. 2022; 22(1):343. https://doi.org/10.3390/s22010343

Chicago/Turabian Style

Zhang, Yanbin, Long-Ting Huang, Yangqing Li, Kai Zhang, and Changchuan Yin. 2022. "Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning" Sensors 22, no. 1: 343. https://doi.org/10.3390/s22010343

APA Style

Zhang, Y., Huang, L.-T., Li, Y., Zhang, K., & Yin, C. (2022). Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning. Sensors, 22(1), 343. https://doi.org/10.3390/s22010343

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI