Pan-sharpening aims to obtain high-resolution multi-spectral images by fusing panchromatic images and low-resolution multi-spectral images though reasonable rules. This paper proposed a novel generative adversarial network for pan-sharpening, which utilizes the supplemented spectral information from low-resolution multi-spectral images and the enhanced structural information from panchromatic images to generate high-resolution multi-spectral images. Firstly, the forward differential operator is used to extract the spatial structural information of the panchromatic image both in the horizontal and vertical directions. Secondly, an architecture of generative adversarial network is designed. The enhanced structural information generated by the accumulation of the structural information of the two directions is added to the image fusion process in generator and the discriminating process in discriminator, and a new optimization objective is designed accordingly. What is more, the low-resolution multi-spectral image is added to the convolution process in the generator as a supplement to the spectral information. Finally, in order to obtain better image generation effect, a special objective function of the generator is designed, which adds a unique relationship to reduce the loss of spatial structural information and spectral information of fused images. Experiments on QuickBird and WorldView-3 satellites datasets show that the proposed method can generate high quality fused images and is better than most advanced methods in both objective indicators and intuitive observations.

泛锐化的目的是通过合理的规则融合全色图像和低分辨率多光谱图像来获得高分辨率多光谱图像。本文提出了一种新的泛锐化生成对抗网络，该网络利用低分辨率多光谱图像的补充光谱信息和全色图像的增强结构信息生成高分辨率多光谱图像。首先，使用前向微分算子在水平和垂直方向上提取全色图像的空间结构信息。其次，设计了生成对抗网络的体系结构。通过将两个方向的结构信息的积累而生成的增强的结构信息添加到生成器中的图像融合过程和鉴别器中的鉴别过程中，从而设计了新的优化目标。此外，将低分辨率多光谱图像添加到生成器中的卷积过程中，作为对光谱信息的补充。最后，为了获得更好的图像生成效果，设计了生成器的特殊目标函数，它添加了独特的关系以减少融合图像的空间结构信息和光谱信息的损失。