GF-3 satellite is the first C-band multi-polarimetric synthetic aperture radar satellite with a space resolution up to 1 m in China,in which scan synthetic aperture radar (ScanSAR) is one of the important mode of GF-3.The working mechanism of this mode results in the phenomena of serious nonuniformity,generally showing 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 mechanism and cycle-consistent adversarial networks (CycleGAN),so as to perform descalloping.The proposed descalloping method is compared with traditional descalloping methods and deep learning related algorithms,and analyzed by indicators such as brightness average and average gradient.The experimental results demonstrate that the proposed 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.