基于稀疏表示的高光谱数据压缩算法

Hyperspectral Data Compression Based on Sparse Representation

如何降低高光谱图像大规模数据的存储和传输代价一直是学者们关心的问题。该文提出一种基于稀疏表示的高光谱数据压缩算法,通过一种波段选择算法构造训练样本集合,利用训练得到的基函数字典对高光谱数据所有波段进行稀疏编码,并对表示结果中非零元素的位置和数值进行量化和熵编码,从而实现高光谱图像压缩。实验结果表明该文算法与3维小波相比具有更好的非线性逼近性能,其率失真性能明显优于3D-SPIHT,并且在光谱信息保留上具有巨大的优势。

How to reduce the storage and transmission cost of mass hyperspectral data is concerned with growing interest. This paper proposes a hyperspectral data compression algorithm using sparse representation. First, a training sample set is constructed with a band selection algorithm, and then all hyperspectral bands are coded sparsely using a basis function dictionary learned from the training set. Finally, the position indices and values of the non-zero elements are entropy coded to finish the compression. Experimental results reveal that the proposal algorithm achieves better nonlinear approximation performance than 3D-DWT and outperforms 3D-SPIHT. Besides, the algorithm has better performance in spectral information preservation.