## 3 Methods
### CR Location Extraction Based on FFT Interpolation and the Sliding Window Method  
The precise extraction of CR coordinates from SAR images has a direct impact on the accuracy of geometric validation. However, accurately pinpointing CR positions in the original SAR images can be challenging due to limited resolution or unclear details. To address this issue, we employed an approach involving FFT interpolation and the sliding window method to accurately determine the phase center position of CR from SLC images. Figure 7: Example of RPC geometric correction results for C-SAR images (the yellow triangles in the figure represent the theoretical positions of the corner reflectors in the figure): (**a**) October 18 C-SAR/02 FSI mode image in SLC, (**b**) November 22 C-SAR/01 FSI mode image in SLC, (**c**) October 18 C-SAR/02 FSI image after geometric correction using the RPC model and a detailed image for CRs, and (**d**) November 22 C-SAR/01 FSI image after geometric correction using the RPC model and a detailed image for CRs. Utilizing the grayscale distribution characteristics of the target image points, we applied an interpolated subdivision algorithm to locate target image points, achieving sub-pixel level measurement accuracy. When determining the phase center position of the point target, we utilized the 8-fold FFT zero-padding interpolation algorithm [18] to interpolate the local signals around the point target by initially performing an FFT of the signals in the time domain, and then performing zero-padding in the high frequencies (i.e., the middle portion of the signals in the frequency domain) to produce the interpolation effect in the time domain. In Figure 8a identifies the point target before interpolation, Figure 8b illustrates the result after zero-padding the spectrum, and Figure 8c displays the point target after interpolation. The white cross-shaped dashed box in Figure 8b identifies the regions with invalid spectral regions that can be zeroed and for which extended point targets can be obtained by inverting the 2D discrete FFT after zero-padding. A buffer is created using the center-of-maximum method and is shifted within the buffer using a suitably sized adaptive window [19]. The sum of pixel values within the adaptive window is calculated. When the sum of values reaches its maximum, the center of the adaptive window is considered as the center of the CR. Specifically, this window size is usually determined based on the width of the point target side lobe before interpolation. In real applications, this value still needs to be determined based on experimental results. We analyzed the impulse response function of the interpolated point target in terms of resolution, peak sidelobe ratio, and integral sidelobe ratio using the method described in reference [23], as shown in Figure 9. CR-4 in the GF-3 UFS mode image acquired on November 21 is used as an example. The spatial resolution is usually expressed in terms of the impulse response width (IRW), i.e., the \(-3\) dB width of the main lobe of the impulse response function of the point target. The Peak Sidelobe Ratio (PSLR) is defined as the ratio of the first sidelobe intensity of the point target impulse response to the peak intensity of the main lobe. The Integral Sidelobe Ratio (ISLR) is the ratio of the sidelobe energy to the main lobe energy in the impulse response profile of a point target. The resolution is an important indicator of the ability of the image to distinguish ground point targets, the PSRL describes the extent to which the weak scattering points are masked by the strong scattering points, and the ISLR measures the energy that overflows from the main lobe to the sidelobe [23], which characterize different aspects of the image quality. The evaluated resolutions in the range and azimuth directions were \(1.214\) m and \(2.164\) m, respectively.