Abstract Compressed sensing provides the possibility of efficient compression of massive hyperspectral data. However, the existing methods often use the organization pattern of image blocks in sparse representation, and cannot make full use of inter spectral correlation. The separate retrieval of dictionary and measurement matrix also restricts the processing efficiency. To solve these problems, a novel and efficient hyperspectral image compression method based on compressed sensing and joint optimization is proposed in this paper. In sparse representation, the data organization pattern on spectral dimension is adopted to better express the correlation between spectra and improve the efficiency of operation. On the dictionary and measurement matrix, a joint optimization algorithm is proposed, which synchronously inhibits the sparse representation error and the reconstruction error. Experimental results show that compared with similar methods, the reconstruction error of this method is increased by 3 dB and the number of iterations is reduced by seven times, and the compression rate can reach 1/18.

中文翻译：

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一种基于压缩感知和联合优化的高光谱图像高效压缩方法

摘要 压缩感知为海量高光谱数据的高效压缩提供了可能。然而，现有方法在稀疏表示中往往使用图像块的组织模式，不能充分利用谱间相关性。字典和度量矩阵的单独检索也限制了处理效率。针对这些问题，本文提出了一种基于压缩感知和联合优化的新型高效高光谱图像压缩方法。在稀疏表示中，采用光谱维度上的数据组织模式，以更好地表达光谱之间的相关性，提高运算效率。在字典和测量矩阵上，提出了联合优化算法，同步抑制稀疏表示错误和重构错误。实验结果表明，与同类方法相比，该方法重构误差增加3 dB，迭代次数减少7次，压缩率可达1/18。