Satellite Image Translation Method Based on Attention Residual Network

Satellite image translation is one of the important application scenarios of generative adversarial networks. The existing satellite image translation has the problems of low generation quality, weak generalization ability, and high computational cost. Based on the cycle generative adversarial network, a lightweight attention residual module is designed to improve the image translation quality and reduce the parameter computation of the model. At the same time, the least squares loss is introduced to improve the stability of the training process. The experimental results show that the proposed method has good translation quality in satellite image translation tasks while maintaining high training stability and low model computation.