With the continuous improvement of satellite remote sensing technology, using super-resolution image reconstruction technology to reconstruct remote sensing images has important application significance for social development. In the generator model proposed in this paper, the standard convolution layer in the residual network structure is replaced by empty convolution to improve the overall performance of the model while keeping the number of parameters unchanged and the receptive field of convolution at each stage unchanged. By analyzing the advantages of residual network, dense connection network, and cavity convolution in the field of image super resolution, an optimized super-resolution reconstruction model of GAN image with cavity convolution is constructed with dense connection block of cavity residue as a generator component. The cloud computing-based service model is introduced into the image reconstruction system, and the background management module is built through the cloud service system, which is responsible for model training, image transmission, image processing request and database reading.Through experimental analysis, it is proved that the whole automatic data processing from automatic matching data to processing data can be completed, and the performance is better than the traditional service mode, which can produce great economic benefits.

随着卫星遥感技术的不断进步，利用超分辨率图像重建技术重建遥感图像对社会发展具有重要的应用意义。在本文提出的生成器模型中，将残差网络结构中的标准卷积层替换为空卷积，以提高模型的整体性能，同时保持参数数量不变和每个阶段卷积的感受野不变。通过分析残差网络、密集连接网络和空洞卷积在图像超分辨率领域的优势，以空洞残差的密集连接块为生成器，构建了优化的带空洞卷积的GAN图像超分辨率重构模型。