针对高光谱图像压缩算法存在的解码端计算复杂度高,且没有充分考虑高光谱图像结构特征信息等问题,提出了一种基于块稀疏表达模式的高光谱图像压缩方法.主要通过在编码端利用结构字典对稀疏系数进行结构化压缩编码,避免解码端非线性重构,以达到缩短高光谱图像重构时间的目的.实验证明该方法在压缩比较低(0.015 9)时依然能获得较高的重构精度(峰值信噪比为22.240 3,结构相似度为0.511 4). In order to solve the problem of hyperspectral image compression algorithm which has high computational complexity on the decoding side and does not fully consider the feature information of hyperspectral image, a hyperspectral image compression method based on block sparse representation is proposed. The structure dictionary encodes the sparse coefficients in a structured way and avoids the non-linear reconstruction of the decoder, so as to reduce the reconstruction time of the hyperspectral image.Experiments show that this method can still obtain a higher compression ratio (0.015 9) Reconstruction accuracy (peak signal to noise ratio of 22.240 3, the structural similarity of 0.511 4).