多波段遥感图象是序列图象的一种，通常序列图象的帧间压缩是基于帧与帧之间的相关性。对于多波段遥感图象，其帧间相关性较小，因此用常规的帧间压缩收效甚微。此文提出一种基于分类的多波段的无损压缩方法(CBS2P2)，先对图象进行分类，然后采用谱间（即帧间）和空间预测方法，进行无损压缩。在多波段遥感图象中，物体在不同波段图象中的灰度值是由其本身固有的光谱反射特性决定的，所以，利用每一类的光谱反射特性构造谱间预测器，可以实现谱间压缩，同时，由于同一帧图象中同类象素之间存在空间相关性，我们选用ＪＰＥＧ标准定义的空间ＤＰ

Multispectral imagery is one of sequence images. Usually, the frame compression of sequence images is based on the decorrelation between frames. To multispectral imagery, there is less correlation between frames. So the conventional frame compression is invalid. In this paper, we present a new lossless compression technique that based on classification to decorrelate spectral correlation and spatial correlation, we called it classification～based spectral prediction and spatial prediction (CBS2P2) method. The imagery was classified first, then was compressed by spectral prediction and spatial prediction. We created spectral pridictors with spectral vectors, for the gray values of image pixels were based on spectral vectors, and chosed the second mode of JPEG standard rediction modes for lossless compression as spatial predictors. The experiments with 6 bands SPOT data, 5 bands NOAA-AVHRR data and 6 bands TM data show the compression ratios can be improved.