基于改进Kalman滤波模型的扫描合成孔径雷达图像扇贝效应校正方法

星载扫描合成孔径雷达(ScanSAR)采取Burst工作模式，该模式在获得宽幅测绘能力的同时，也导致图像中产生了固有的扇贝效应，严重影响图像的视觉效果和定量应用。该文结合对ScanSAR图像方位向统计特性的分析，针对现有滤波模型稳定性差和时间复杂度高等缺点，提出了一种改进的Kalman滤波模型，对图像方位向标准差和均值进行滤波以校正扇贝条纹。在高分三号(GF-3)卫星获取的真实ScanSAR图像上的校正结果验证了改进算法的有效性和高效性，此外在建筑群和海陆交界等复杂场景图像上的实验结果表明，改进算法具有较强的鲁棒性。

A Scalloping Correction Method for ScanSAR Image Based on Improved Kalman Filter Model

The spaceborne Scanning Synthetic Aperture Radar (ScanSAR) adopts the Burst working mode. While obtaining wide-range mapping capabilities, this mode also causes an inherent scalloping in the image, which seriously affects the visual effects and quantitative applications of the image. Based on the analysis of the azimuth statistical characteristics of ScanSAR images and aimed at the shortcomings of the existing filtering model such as poor stability and high time complexity, an improved Kalman filtering model is proposed, which filters the standard deviation and mean of image in azimuth position to correct scallop stripes. The correction results on the real ScanSAR images acquired by the GF-3 satellite verify the effectiveness and efficiency of the improved algorithm. Furthermore, the experimental results on complex scene images such as buildings and the junction of sea and land indicate that the strong robustness of the improved algorithm.