针对多波段遥感图象的空间和谱间结构特点，提出了多方式预测的概念，一幅图内的任一象素按照给定的准则在侯选预测函数集中选取其实际采用的预测函数，从而更大程度去除相关；同时利用谱间结构相关，令谱间邻点选用相同的预测方式，使多方式预测导致的附加存储代价大大缩小。提出以极小熵原则作为选取预测函数的理论判据，并将其等效为最小误差的最高频次准则。对TM图象的实验证明此方法能更有效去除相关、压缩比有较大提高。

According to the spatial and spectral structural characteristics of multi spectral image data, this paper present a concept of Multi Pattern Prediction : given a principle, any pixel in an image can be predicted by any prediction function selected from an alternative function set in order to decorrelate the image more efficiently ; at the same time, taking the advantage of spectral structural correlation, the spectral adjacent pixels are decorrelated by the same prediction function, so the additional cost of storage in Multi Pattern Prediction would be reduced smartly. We present the Minimum Entropy Principle as the theoretic principle to select the predition function. And we get an equivalent principle named as Maximal Frequency of Minimum-Error Standard. Experiments on TM images show that this method can decorrelate images much more efficiently and lead to higher compression ratios.