星载高光谱图像的有效压缩已经成为高光谱遥感领域亟待解决的难题。分布式信源编码具有较低的编码复杂度与良好的抗误码性,在高光谱图像压缩领域具有广阔的应用前景。提出了一种基于多元陪集码的高光谱图像分布式近无损压缩算法。根据多元陪集码的Slepian-Wolf无损编码的压缩过程,提出了面向高光谱图像分布式近无损压缩的最优量化方案,使得高光谱图像在给定目标码率条件下的失真达到最小,在此基础上对量化值进行Slepian-Wolf无损编码,从而实现了高光谱图像的分布式近无损压缩。实验结果表明,与典型的传统算法相比,该算法取得了较好的近无损压缩性能和较低的编码复杂度。 Effective compression of spaceborne hyperspectral images has become an urgent problem to be solved in the field of hyperspectral remote sensing. Distributed source coding has low coding complexity and good error resilience and has broad application prospects in the field of hyperspectral image compression. A near lossless compression algorithm for hyperspectral image based on multivariate cosulence codes is proposed. According to the compression process of Slepian-Wolf lossless coding with multivariate coset codes, an optimal quantization scheme for near lossless compression of hyperspectral image is proposed, which minimizes the distortion of hyperspectral image at a given target bit rate. In Based on this, Slepian-Wolf quantized values ​​are losslessly encoded, so that distributed and near-lossless compression of hyperspectral images is achieved. Experimental results show that the proposed algorithm achieves good near-lossless compression performance and lower coding complexity compared with typical traditional algorithms.