Airborne sensors reconnaissance and ground data fusion processing is the fourth grade of UAV situation awareness that planned by USA Office of the Secretary of Defense. Independent Component Analysis(ICA) that applied to the image processing field is a novel method of transform domain in the analysis of human visual system characteristics based on sparse coding theory, with multiple directions, excellent characteristic extraction and edge modeling feature. Color transfer is the best way to get natural sense color fusion image. The combination of studies highlighted the band characteristics of the natural sense color fusion method so as to enhance UAV situation awareness. Training image database was established according to the scene and the independent band feature information was extracted to construct ICA domain analysis kernel and synthesis kernel. In the ICA domain, the gray fusion image was generated applying area energy fusion rules, the gray fusion image color information was given using source image linear projection to the color channel. The various channels of source color fusion image and color reference image were multi-resolution decomposed using steerable pyramid, each channel transfer mean and variance were independently completed. Finally a similar color fusion image was obtained. Eye perception and objective evaluation show that outstanding band features and natural color enhance detail information to further improve the airborne platforms scene perception.