Remote Sensing Image Jitter Restoration Based on Deep Generative Adversarial Network

High stability of the observation satellite platform is increasingly demanded in recent years. However, attitude jitter of observation satellites is a problem that degenerates the development of imaging quality and resolution. In order to reduce the geo-positioning errors and improve the geometric accuracy of remote sensing images, satellite jitter have been studied in recent years. In this work, a generative adversarial network (GAN) architecture is proposed to automatically learn and correct the deformed scene features from a single remote sensing image. In the proposed GAN, a convolutional neural network (CNN) is designed to discriminate the inputs and another CNN is used to generate so-called fake inputs. In order to explore the usefulness and effectiveness of GAN for jitter detection, the proposed GAN are trained on part of PatternNet dataset and tested on three popular remote sensing datasets. Several experiments show that the proposed models provide competitive results compared to other methods. the proposed GAN reveals the huge potential of GAN-based methods for the analysis of attitude jitter from remote sensing images.