多光谱信息融合是指将从多光谱探测器获得的同一场景的多光谱信息特征组合到一起，并利用它们在时空上的相关性及信息上的互补性，以利到对景物更全面，清晰的描述，其真实感成像是指融合后的图像能保留原多光谱图像目标在特定条件下的光谱特征，针对用同一种方法来融合不同场景图象存在的问题，通过分析现有融合方法的优缺点和它们适用的场合，提出了一种综合的图象融合方法，该方法对相关性较弱，且互补性明显的图象对，采用一种新的基于人类视觉系统HVS的自适应加权平衡的融合方法来进行融合，这样能突出目标的光谱特性，而对于相关性较强的图象对，则根据场景目标距离的远近，分别采用小波变换和主成份分析来进行融合，实验表明，该方面能适合于不同类型的场景，其得到的融合结果优于用单种方法进行融合的结果，也有利于对目标的探测和识别。

Multi-spectral information fusion is the combination of information acquired from multi-spectral sensors in the same scene to acquire clearer description about the scene by using the temporal and spacial correlation, and their complementarity. The realistic fusion refers to the conservation of spectral features from original multi-spectral images in certain case in the new fused image. In this paper we propose a new image fusion method by analyzing advantage and disadvantage of several common methods and studying those applicable environments. This method integrates several fusion methods according to those applicable environments. Besides we propose a new HVS based image fusion which is suitable for little correlation among the multi-spectral images. The HVS based method can extrude target spectral character. When the correlation among multi-spectral images is strong, the wavelet transform-based or PCA method is used according to the distance of the targets. Experiments show that our integrated method can be suitable for different type scene, and the fusion results are better than that using single fusion method and the result is advantageous to the detection and recognition for targets.