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

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

doi:10.3390/s22010343

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

Sensors (Basel). 2022 Jan 4;22(1):343. doi: 10.3390/s22010343.

Authors

Yanbin Zhang^{1

2}, Long-Ting Huang³, Yangqing Li¹, Kai Zhang¹, Changchuan Yin¹

Affiliations

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.

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