如今高光谱数据的有效压缩已成为遥感技术发展中需要迫切解决的问题，为了对高光谱数据进行有效压缩，提出了一种基于3维上下文预测的高光谱图像无损压缩算法。该算法首先根据相邻波段间的相关性大小进行波段分组，同时对各个分组重新进行波段排序；然后采用自适应波段选择算法对高光谱图像进行降维，再利用k means算法对降维后的波段谱向矢量进行聚类；最后在参考波段和当前波段中通过定义3维上下文预测结构，在聚类结果的基础上，对各个分类分别训练其最优的预测系数。实验结果表明，该方法可显著降低压缩后图像编码的平均比特率。

The request for efficient compression of hyperspectral images becomes pressing. A new lossless compression algorithm based on 3D contexts prediction for hyperspectral images is presented. Spectral band grouping algorithm is introduced to divide hyperspectral images into groups according to the neighboring band correlations, then band reordering is performed for each group. The important bands containing large information can be determined by using adaptive band selection algorithm, on which clustering is carried out according to the spectral vectors. 3D contexts are defined based on the neighboring causal pixels in current band and the corresponding colocated causal pixels in reference band. Combined with the clustering results, the optimal predictive coefficients of each cluster are trained respectively. Experimental results show that the proposed algorithm can give better lossless coding performance.