Automatic colorization for grayscale satellite images can help with eliminating lighting differences between multi-spectral captures, and provides strong prior information for ground type classification and object detection. In this paper, we introduced a novel generative adversarial network with multiple discriminators for colorizing gray-scale satellite images with pseudo-natural appearances. Although being powerful, deep generative model in its common form with a single discriminator could be unstable for achieving spatial consistency on local textured regions, especially highly textured ones. To address this issue, the generator in our proposed structure produces a group of colored outputs from feature maps at different scale levels of the network, each being supervised by an independent discriminator to fit the original colored training input in discrete Lab color space. The final colored output is a cascaded ensemble of these preceding by-products via summation, thus the fitting errors are reduced by a geometric series form. Quantitative and qualitative comparisons with the sole-discriminator version have been performed on highresolution satellite images in experiments, where significant reductions in prediction errors have been observed.