## 1 Introduction  
The successive launches of high-resolution synthetic aperture radar (SAR) satellites in China have made it possible to efficiently access high-resolution SAR image data through satellite networking. Despite continuous enhancements in the resolution and radiometric quality of China's satellite-borne SAR images, a substantial portion of this valuable data remains underutilized, resulting in considerable data wastage [1]. SAR satellite images are more susceptible to geometric distortions, such as foresight contraction, overlaying, and shadowing, compared to optical images. This susceptibility arises from their side-looking imaging characteristics [2], which are influenced by image geometry and topographic relief [3]. In various applications, like multi-source remote sensing image fusion analysis and multi-temporal SAR image comparison analysis, SAR images free from substantial geometric distortions and possessing precise geographic location information are often essential prerequisites [4]. Consequently, there is a pressing need to perform geometric corrections on SAR images, accurately project them into geographic coordinate systems, and quantitatively analyze their errors [5; 6]. Geometric correction plays a crucial role in acquiring highly precise geographic location information of ground targets. The geometric positioning model of a spaceborne SAR system establishes a mathematical relationship between the three-dimensional spatial coordinates of a ground target point and the two-dimensional coordinates of the image plane of the corresponding image point. This model forms the fundamental basis for the geometric processing of spaceborne SAR data. In comparison to the Range-Doppler (RD) model [7; 8], the rational polynomial coefficient (RPC) model offers versatility and is applicable to various sensor types, without the need for extensive knowledge of imaging parameters and sensor system characteristics [9]. It is user-friendly and simplifies the process. The RPC parameters are available for SAR image products from satellites like RADARSAT-2, a well-established commercial satellite [10]. Similarly, the C-SAR series satellites also provide RPC parameters within their files for image geometric correction. [PERSON] et al. [10] have theoretically demonstrated that the RPC model can effectively replace the RD model for spaceborne SAR images. They conducted experiments using multiple types of spaceborne SAR data, revealing that the RPC model can achieve a high level of fitting accuracy across SAR systems with varying resolutions. This makes the RPC model a valuable tool in the photogrammetric processing of SAR images. SAR images lack the straightforward image-point-object-point correspondence commonly found in optical images. To assess geometric positioning accuracy in SAR images, control points within the SAR image are utilized. In contrast to distributed scattering targets, corner reflectors (CR) play a distinctive role. When the radar signal encounters a CR from a normal direction, it reflects the echo back towards the SAR system along the incident direction [11]. This reflection results in a distinct bright area on the SAR image, facilitating the accurate determination of the \"bright spot\" center [12], thereby achieving high-precision localization. Consequently, CRs are frequently employed as high-precision ground control points in SAR images. [PERSON] et al. [13] conducted ground measurements to obtain the geographic coordinates of CRs, translating them into theoretical values. They then compared these theoretical values with the range-azimuth pixel position of the CRs in the image. Their findings demonstrated that TerraSAR-X and TanDEM-X could achieve centimeter-level localization accuracy under ideal conditions [14]. Additionally, [PERSON] [1] developed a high-precision geometric calibration method for SAR satellites. This method involved error analysis and model construction, incorporating interpolation techniques to enhance the image pixel position accuracy to the sub-pixel level.