Two-Dimensional Spatial Variation Analysis and Correction Method for High-Resolution Wide-Swath Spaceborne Synthetic Aperture Radar (SAR) Imaging

With the development and application of spaceborne Synthetic Aperture Radar (SAR), higher resolution and a wider swath have become significant demands. However, as the resolution increases and the swath widens, the two-dimensional (2D) spatial variation between different targets in the scene and the radar becomes very pronounced, severely affecting the high-precision focusing and high-quality imaging of spaceborne SAR. In previous studies on the correction of two-dimensional spatial variation in spaceborne SAR, either the models were not accurate enough or the computational efficiency was low, limiting the application of corresponding algorithms. In this paper, we first establish a slant range model and a signal model based on the zero-Doppler moment according to the spaceborne SAR geometry, thereby significantly reducing the impact of azimuth spatial variation in two-dimensional spatial variation. Subsequently, we propose a Curve-Sphere Model (CUSM) to describe the ground observation geometry of spaceborne SAR, and based on this, we establish a more accurate theoretical model and quantitative description of two-dimensional spatial variation. Next, through modeling and simulation, we conduct an in-depth analysis of the impact of two-dimensional spatial variation on spaceborne SAR imaging, obtaining corresponding constraints and thresholds and concluding that in most cases, only one type of azimuth spatial variation needs to be considered, thereby greatly reducing the demand and difficulty of two-dimensional spatial variation correction. Relying on these, we propose a two-dimensional spatial variation correction method that combines range blocking and azimuth nonlinear chirp scaling processing and analyze its scalability to be applicable to more general cases. Finally, the effectiveness and applicability of the proposed method are validated through both simulation experiments and real data experiments.