Spaceborne scanning synthetic aperture radar (ScanSAR) is widely used in global observations due to its ability to perform wide-swath mapping in the range direction. However, its special working mode causes scalloping, which reduces the quality of images and affects subsequent applications. According to its characteristics, an adaptive method based on pre-estimation and weighted filtering is proposed in this paper to suppress scalloping in the image domain. First, the azimuth intensity distribution of the image after scalloping suppression is estimated, which is used for scene stationarity test. Then, the images that cannot meet the stationary standard are segmented into subimages using maximum entropy principle and mathematical morphology. Finally, an algorithm based on adaptive weighted filtering is introduced to suppress scalloping, and the suppressed subimages are fused to obtain final results. The performance of the proposed method is tested with real ScanSAR data from the Gaofen-3 (GF-3) satellite. The experimental results indicate that the effect of scalloping suppression is excellent, since the depth of scalloping can be suppressed to approximately 0.3 dB. Notably, the proposed system is efficient in processing large-area images, such as GF-3 ScanSAR images whose actual width is more than 300 km. The entire process requires no parameter adjustment, and the proposed method is suitable for various complex scenes. The effectiveness, high efficiency, adaptability and robustness of the proposed system are verified.

星载扫描合成孔径雷达（ScanSAR）由于能够在距离方向进行大范围的测绘，因此在全球观测中得到了广泛应用。但其特殊的工作模式会造成扇形，降低图像质量，影响后续应用。针对其特点，本文提出了一种基于预估计和加权滤波的自适应方法来抑制图像域中的扇形。首先，估计扇形抑制后图像的方位强度分布，用于场景平稳性测试。然后，利用最大熵原理和数学形态学将不能满足静止标准的图像分割成子图像。最后，引入了一种基于自适应加权滤波的算法来抑制扇形，并融合抑制的子图像以获得最终结果。使用来自高分三号（GF-3）卫星的真实 ScanSAR 数据测试了所提出方法的性能。实验结果表明，扇贝抑制效果非常好，扇贝的深度可以抑制到0.3dB左右。值得注意的是，该系统在处理大面积图像方面非常有效，例如实际宽度超过 300 km 的 GF-3 ScanSAR 图像。整个过程无需参数调整，所提方法适用于各种复杂场景。验证了所提系统的有效性、高效性、适应性和鲁棒性。实验结果表明，扇贝抑制效果非常好，扇贝的深度可以抑制到0.3dB左右。值得注意的是，该系统在处理大面积图像方面非常有效，例如实际宽度超过 300 km 的 GF-3 ScanSAR 图像。整个过程无需参数调整，所提方法适用于各种复杂场景。验证了所提系统的有效性、高效性、适应性和鲁棒性。实验结果表明，扇贝抑制效果非常好，扇贝的深度可以抑制到0.3dB左右。值得注意的是，该系统在处理大面积图像方面非常有效，例如实际宽度超过 300 km 的 GF-3 ScanSAR 图像。整个过程无需参数调整，所提方法适用于各种复杂场景。验证了所提系统的有效性、高效性、适应性和鲁棒性。整个过程无需参数调整，所提方法适用于各种复杂场景。验证了所提系统的有效性、高效性、适应性和鲁棒性。整个过程无需参数调整，所提方法适用于各种复杂场景。验证了所提系统的有效性、高效性、适应性和鲁棒性。