GF-3 satellite is the first C-band multi-polarimetric synthetic apertu re radar satellite with a space resolution up to 1 m in China,in which scan synthetic ape rture radar (ScanSAR) is one of the important mode of GF-3.The working mechani s m of this mode results in the phenomena of serious nonuniformity,generally show ing bright and dark stripes,also known as scalloping.In view of scalloping in ScanSAR mode of GF-3,this paper proposes a model combining self-attention mec ha nism and cycle-consistent adversarial networks (CycleGAN),so as to perform des c alloping.The proposed descalloping method is compared with traditional descalloping methods a nd deep learning related algorithms,and analyzed by indicators such as brightne ss average and average gradient.The experimental results demonstrate that the p roposed method in this paper can better complete descalloping in the GF-3 ScanSAR image, effectively suppress the stripes phenomenon of the image,and improve the image quality,which is of great practical significance.