基于环境卫星多光谱图像特点的分析，提出了一种新的基于三维等级树集合划分算法(3D-SPIHT)和感兴趣区域(ROI)编码相结合的多光谱图像压缩算法。首先在谱间采用两种小波基相结合的三维离散小波变换(3D-DWT)，去除多光谱图像在空间和谱间的冗余信息，减少恢复光谱的误差值，然后采用部分三维等级树集合划分算法和小波系数提升的感兴趣区域编码相结合的方法。该方法对小波系数从空间方向树上按对恢复光谱信息的重要性不同进行合理的码率分配，使得恢复光谱具有更好的分辨率，并依据比特平面层中重要系数的统计概率来自适应地进行3种编码模式的选择，提高了编码效率。实验数据结果表明，该算法比传统算法更好地保护了多光谱图像中的光谱信息，在压缩比为8∶1的情况下，满足了环境卫星多光谱图像压缩系统的要求。

Based on the analyses of environmental satellite multispectral images, a new compression algorithm is proposed based on the three-dimentional set partitioning in hierarchical trees (3D-SPIHT) and region of interest (ROI) coding. To reduce the redundancies in the spatial and spectral domain and decrease the reconstructed spectrum error, a three-dimentional discrete wavelet transformation (3D-DWT) which combines two wavelet bases in the spectral domain is carried out. Then, a method which unites partial 3D-SPIHT with lifting the wavelet coefficients of ROI coding algorithm, is adopted to allocate the coding rate in reason according to the different significance of the reconstructed spectrum in the spatial orientation tree wavelet coefficient. The new algorithm on the probability of the significant coefficients in each bit plane to select one of three different coding modes, which enhances the coding efficiency. Finally, the experimental results show that the proposed algorithm achieves improved performance over the conventional algorithm. With the 8∶1 compression ratio, the algorithm satisfies the requirement of the satellite multispectral image system.