中文摘要：

针对高光谱图像谱间相关性较强的特点，提出了一种基于谱间预测的压缩感知算法．编码端通过线性预测算法，估计出相邻波段的预测系数作为辅助信息传送到解码端，再对图像进行独立地随机观测和量化．解码端根据预测系数和已重构相邻波段，估计出当前波段的预测波段，用来修正重构算法的初始值和收敛准则．最后用改进的重构算法解码当前波段．实验结果表明，在相同观测值数目下，该算法的PSNR提高了大约1．2dB，解码复杂度明显降低，而且编码复杂度低、易于硬件实现．

英文摘要：

A new compression algorithm for hyperspectral images based on compressed sensing is proposed which has the advantages of high reconstruction quality and low complexity by exploiting the strong spectral correlations. At the encoder, the prediction parameter between the neighboring bands is first estimated using the prediction algorithm and transmitted to the decoder. The random measurements of each band are then made, quantized and transmitted to the decoder independently. At the decoder, a new reconstruction algorithm with the proposed initialization and stopping criterion is applied to reconstruct the current band with the assistance of its prediction band, which is derived from the previous reconstructed neighboring band and the received prediction parameter using the prediction algorithm. Experimental results show that the proposed algorithm not only obtains a gain of about 1.2 dB but also greatly decreases decoding complexity. In addition, our algorithm has the characteristics of low-complexity encoding and easy hardware implementation.