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.