在分析多光谱图像小波变换后系数特点的基础上, 提出了一种共享有效图的小波变换压缩方法(SSMWT)。该方法将小波变换压缩技术中的零树编码推广到多光谱图像压缩中, 利用多光谱图像的结构相关性, 对多幅小波图像只需构造一幅有效图, 同时去除空间冗余和谱间结构冗余, 并与KL变换相结合, 进一步去除谱间统计冗余, 实验表明了该方法的有效性。

For the multispectral image (MSI) data, there are two types of redundancy: spatial redundancy and spectral redundancy. In this paper, we classified the spectral redundancy into two categories: spectral statistical redundancy and spectral structural redundancy. The former is based on the spectral resolution. The higher spectral resolution the more redundancy. The latter is caused by the same imaging objects of all bands images, it is essentially based on the arrangement of earth objects. Essentially, the two types of redundancy are different. Here, we proposed a lossy compression technique based on wavelet transformation: Share Significance Map Wavelet Transform (SSMWT). With this technique, zerotree coding was used in compression of MSI, and we only need to create one significance map for all bands of images in MSI, is the light of structure correlation between all bands of images after WT, and then to remove spatial correlation and spectral structure correlation. Combined with K L transformation, the spectral statistic correlation of MSI can be removed. The experiments have shown the efficiency of this technique.