3D Contexts-based Predictive Lossless-Coding for Hyperspectral Images
基于3维上下文预测的高光谱图像无损压缩

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 co-located 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.