根据高光谱图像的特点，提出一种基于谱间去相关模型的迭代硬阈值重构算法。根据高光谱图像序列的相邻图像之间具有很强的相关性，在迭代硬阈值重构算法中建立谱间去相关模型，除去重构图像观测数据中谱间相关的观测数据，去相关后的图像的观测数据更加稀疏，重构性能更高。实验结果表明，在相同观测数目下，本算法与迭代硬阈值重构算法相比，有效提高了图像的重构质量。

According to the characteristic of hyperspectral images, a novel iterative hard thresholding (IHT) recovery algorithm based on spectral decorrelation model is proposed. In terms of apparent correlations between the image series, a spectral decorrelation model is established in iterative hard thresholding recovery algorithm. The spectral redundancies are removed in the measured data of current image by the model, and the decorrelation image data is much sparser, which can be reconstructed easily. Experimental results show that the proposed algorithm achieves improved reconstruction performance over IHT algorithms with the same measurement number.