数据压缩是高光谱图像处理应用中的一个关键问题。为了对高光谱图像进行有效压缩，在2维小波变换的基础上，提出了一种分组矢量量化的高光谱图像有损压缩方案。该方案首先按照谱段类型对高光谱图像进行分组，然后对每个谱段分别进行2维小波变换，最后变换系数再使用一种Kronecker-Product形状-增益矢量量化方法来进行量化编码。计算机仿真结果证明．该算法在取得高压缩率的同时，不仅能很好地保持数据的谱特征，并能降低运算量。

Data compression is a key problem in the applications of hyperspectral images. To meet the demand, based on 2-D wavelet transform, this paper proposes a lossy compression method using band grouping and vector quantization. Every band of hyperspectral images is decomposed by biorthogonal filters and the coefficients are coded by Kronecker-Product Gain-Shape Vector Quantization. The experimental results have shown that the computational complexity is reduced. The high compression ratio is achieved and the spectral characteristics are preserved.