The combination of multi-scale transform and the rules which are “high-frequency coefficients combined by selecting the maximum gray value or energy” and “low-pass ones combined by weighting average” is an effective method in dual-band image fusion. However, when these methods are used to fuse multi-band images, sequential weighted average often leads the weakening of the inherent different information of original images, which affects the subsequent target recognition and scene understanding. The problem is more obvious when fusing multi-band images with texture features. In order to describe the scene in a more comprehensive and precise way, a new multi-band texture image fusion method based on embedded multi-scale decomposition and possibility theory is proposed. The method consists of three parts. The original multi-band images are decomposed into their high- and low-frequency components through a multi-scale transform. The high-frequency components are fused per-pixel by extracting the maximum gray value, whereas the last layer of low-frequency components of original multi-band images with the largest standard deviation is blocked through the another multi-scale transform. Based on the specific sizes and positions of these blocks, the remaining two original images are divided. All the blocks from three bands are traversely fused according to the possibility theory, and the low-frequency image is formed by mosaicing these fused blocks. Then, this image is inversely transformed with its high-frequency counterparts to get the final fusion image. This method not only integrates the pixel-level with feature-level fusion methods, but also integrates the space domain with transform domain technologies together, and solves the problem of sawtooth effect on the edge of the target through the different fusion rules with the different sizes of blocks. The validity of the method proposed is proved.