Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. A wide range of pansharpening methods are available, each producing images with different characteristics. To compare the performances and characteristics of different methods, a contest was held in 2006 by the IEEE Data Fusion Technical Committee. In this contest, À trous wavelet transform-based pansharpening (AWLP) and Laplacian pyramid-based context adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, we observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to design a method taking advantage of both methods by locally selecting the best one. This adaptive decision fusion is performed based on the local scale of the structure. The interest of the proposed method is verified using both visual and quantitative analyses for different Pléiades data sets.