In order to quickly detect the geomorphic information in unknown areas, remote sensing satellites can scan specific areas to obtain remote sensing satellite images. When satellites pass through foreign areas, some satellites are unable to perform long-term resident scanning for a specific area. This paper proposes a Conditional Generative Adversarial Network (CGAN) for network training. In the early stage, the terrain information obtained by a certain method is used as the conditional constraint information in the CGAN network's generation network and identification network, through the mutual game between the network generator and discriminator during the training process, a specific output set is generated, effectively achieve end-to-end nonlinear mapping from a single electronic contour image to the corresponding satellite remote sensing image. This article compares four types of error calculations between the original real satellite remote sensing image and the generated satellite remote sensing image. The average error, mean square error, and structural similarity SSIM are all higher than 99%, and the peak signal-to-noise ratio is higher than 30 dB. The generated image has high similarity with the original image, achieve the reconstruction technology of remote sensing satellite image content in specific areas under the prior condition of obtaining coordinate positioning contour information.