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有效的星载超光谱图像压缩技术对于解决超光谱图像实时传输极为重要。针对超光谱图像传统的联合编解码算法的不足，提出了一种基于分布式信源编码(Distributed Source Coding，DSC)的超光谱图像无损压缩算法。为利用超光谱图像的局部空间相关性，将超光谱图像进行分块处理；引入多元线性回归模型构建编码块的边信息，并为每个编码块选取最优的预测阶数，以有效利用超光谱图像的局部谱间相关性。根据(n，k)线性分组码的原理，通过多元陪集码实现超光谱图像的分布式无损压缩。实验结果表明：该算法能够取得较好的无损压缩性能，同时具有较低的编码复杂度，适合星载超光谱图像的压缩实现。“,”The efficient onboard lossless compression is very important for the real-time transmission of hyperspectral images. Due to the shortages of the traditional joint encoding and decoding algorithms of hyperspectral images, a lossless compression algorithm based on distributed source coding (DSC) was proposed. To make use of the local spatial correlation, multiple linear regression was employed to construct the side information of each block, and the optimal predictive order was determined for each block in order to make full use of the local spectral correlation. According to the principle of (n, k) linear grouping codes, distributed lossless coding of hyperspectral images was realized by using multilevel coset codes. Experimental results show that the proposed algorithm achieved competitive compression performance and low complexity compared with those existing classical algorithms, which is suitable for the onboard compression of hyperspectral images.