Adaptive Threshold ArrayInSAR Point Cloud Denoising Method for Improved Hierarchical Moving Surface Fitting

ArrayInSAR has three-dimensional resolution capability, which can obtain three-dimensional information of the observation scene with a single flight, solving the problem of layover in InSAR mapping. In the process of ArrayInSAR data imaging, due to factors such as inconsistent antenna patterns, baseline errors, multipath effects, etc., there are noise points in the obtained 3D point cloud data of the observation scene, which reduces the quality of the 3D imaging results. This article proposes an ArrayInSAR point cloud denoising method based on an improved hierarchical moving surface fitting and adaptive threshold(AD-IHMSFA). Firstly, preprocess the point cloud data to remove discrete outliers in the air. Then, the improved hierarchical moving surface fitting algorithm is used to extract ground information from ArrayInSAR point cloud data. Finally, the DEM obtained from ground information interpolation is used to remove noise points from the SAR point cloud. The experimental results show that compared with traditional point cloud denoising methods, our method can effectively remove noise points in ArrayInSAR point cloud data and improve the accuracy of ArrayInSAR 3D reconstruction results.