Name
Title | ||
|---|---|---|
Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/s22010343 | SENSORS |
Keywords | DocType | Volume |
hyperspectral images, hyperspectral remoting sensing, Bayesian learning, compressive sensing, low-rank and joint-sparse | Journal | 22 |
Issue | ISSN | Citations |
1 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanbin Zhang | 1 | 0 | 0.34 |
| Long-Ting Huang | 2 | 0 | 0.34 |
| Yangqing Li | 3 | 1 | 2.04 |
| Kai Zhang | 4 | 0 | 0.34 |
| Changchuan Yin | 5 | 548 | 56.53 |