为了能够更好地把来自多传感器的图象信息综合起来，以提高对图象信息的分析和提取能力，在研究了小波包图象分析法之后，提出了一种基于小波包变换的图象融合方法，由于小波包变换能对图象进行多层次分解，包括对小波变换没有细分的高频部分也能进行进一步的分解，因此小波包分析能够为图象提供一种比小波多分辨分析更加精细的分析方法，利用此融合算法将由多传感器获得的同一目标不同波段的遥感图象和不同分辨率的遥感图象进行融合后得到的融合图象，能够很好地将源图象的细节融合在一起，通过与该融合图象进行主观与客观的评价比较，证明该融合方法可以获得更好的融合效果。

In order to merge information from multi-sensor adequately and to improve abilities of information analysis and feature extraction, we study wavelet packet analysis on image data and give a fusion method in pixel level by means of wavelet packet transform in this paper. Wavelet packet transform decomposes an image into low frequency band and high frequency band in different level. Besides decomposing low frequency band in a higher scale, it decomposes high frequency band in higher scale which wavelet analysis does not do. It offers a more precise way for image analysis than wavelet multi-resolution analysis. Through merging images data of different wavebands from multi-sensor to the same object and different resolution images data by applying image fusion method based on wavelet packet analysis, we get fused pictures. The method can fuse details of input images successfully, so that it display information of the each input image perfectly. Comparing with other fused images and appraising them on way of objective and subjective performance, we can draw the conclusion that using this image fusion method can get more satisfactory result than using others.