<p>The pansharpening method of fusing remote sensing satellite photography to obtain higher quality images is an increasingly hot research topic. However, the scarcity of the ground truth makes it difficult to conduct supervised learning with large amounts of data. In this paper, we propose a multi-scale network for semi-supervised learning (PSSGNet), in which we adopt generative adversarial network (GAN) for the extracted features of each layer to reduce the domain differences on reduced and full resolution data. Our framework is mainly divided into two parts. Firstly, we propose a multi-scale framework to extract informative features and restore more spectral information from images of different scales, thus realizing the fusion and complementary of features of multiple images. Secondly, we apply GAN to the output of each layer to eliminate the negative impact caused by inherent domain gap, with which we obtain relevant parameters to migrate to the training of real images and use a small amount of real data to lead the reduced resolution data to compensate for the missing information. Extensive experiments show that compared with other recent competitive methods, the proposed one has certain advantages in the quantitative metrics especially on full resolution data, and also achieves better visual results.</p>