Hyperspectral remote-sensing images have the characteristics of large transmission data and high propagation requirements, so they are faced with transmission and preservation problems in the process of transmission. In view of this situation, this paper proposes a spectral image reconstruction algorithm based on GISMT compressed sensing and interspectral prediction. Firstly, according to the high spectral correlation of hyperspectral remote-sensing images, the hyperspectral images are grouped according to the band, and a standard band is determined in each group. The standard band in each group is weighted by the GISMT compressed sensing method. Then, a prediction model of the general band in each group is established to realize the remote-sensing image reconstruction in the general band. Finally, the difference between the actual measured value and the predicted value is calculated. According to the prediction algorithm, the corresponding difference vector is obtained and the predicted measured value is iteratively updated by the difference vector until the hyperspectral reconstructed image of the relevant general band is finally reconstructed. It is shown by experiments that this method can effectively improve the reconstruction effect of hyperspectral images.

中文翻译：

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基于GISMT压缩感知和谱间预测的高光谱图像重建研究。

高光谱遥感图像具有传输数据量大，传播要求高的特点，因此在传输过程中面临着传输和保存问题。针对这种情况，提出了一种基于GISMT压缩感知和谱间预测的光谱图像重建算法。首先，根据高光谱遥感图像的高光谱相关性，根据波段对高光谱图像进行分组，并在每组中确定标准波段。每组中的标准频段通过GISMT压缩传感方法加权。然后，建立各组通用频段的预测模型，以实现通用频段的遥感图像重建。最后，计算出实际测量值和预测值之间的差。根据该预测算法，获得相应的差分矢量，并且通过该差分矢量迭代地更新预测的测量值，直到最终重构出相关通用频带的高光谱重构图像为止。实验表明，该方法可以有效提高高光谱图像的重建效果。