College of Life Information & Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2018 Sep 30;18(10):3289. doi: 10.3390/s18103289.
In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results-the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain-than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance.
为了提高海量高光谱数据的存储和传输性能，提出了一种基于预测的空谱自适应高光谱压缩感知（PSSAHCS）算法。首先，根据空间自相关系数自适应地获得高光谱图像的空间块大小。其次，使用 k-means 聚类算法对高光谱图像进行分组。然后，我们使用局部均值和局部标准差（LMLSD）算法在组中找到最佳图像作为关键带，组中非关键带可以通过线性预测进行平滑。第四，使用随机高斯测量矩阵作为采样矩阵，离散余弦变换（DCT）矩阵作为稀疏基。最后，使用分阶段正交匹配追踪（StOMP）来重构高光谱图像。实验结果表明，所提出的 PSSAHCS 算法可以获得更好的评估结果-主观评估、峰值信噪比和空间域中的空间自相关系数，以及光谱域中的光谱曲线比较和光谱相关性-比单光谱压缩感知（SSCS）、块高光谱压缩感知（BHCS）和自适应分组分布式压缩感知（AGDCS）更好。PSSAHCS 不仅可以有效地压缩和重构高光谱图像，而且具有很强的去噪性能。
