Zhang Yanbin, Huang Long-Ting, Li Yangqing, Zhang Kai, Yin Changchuan
Beijing Laboratory of Advanced Information Networks, Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
China Fire and Rescue Institute, Beijing 102202, China.
Sensors (Basel). 2022 Jan 4;22(1):343. doi: 10.3390/s22010343.
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.
为了减少通过高光谱遥感（HRS）所需传输的高光谱成像（HSI）数据量，我们提出了一种结构化低秩和联合稀疏（L&S）数据压缩与重建方法。所提方法利用稀疏贝叶斯学习和压缩感知（CS）来挖掘HSI数据中的空间和光谱相关性。通过使用同时的L&S数据模型，我们利用主成分信息和贝叶斯学习来重建高光谱图像。仿真结果表明，在相同信噪比（SNR）和压缩率下，所提方法在重建精度和计算负担方面优于LRMR和SS&LR方法。
