## 4 Experimental Results and Analysis
### Accuracy Verification  
To verify the reliability of the proposed method, internal consistency validation was performed by comparing the results of the proposed method with those of the traditional global PSInSAR method using TerraSAR-X data. Cross-validation with multi-source data was also conducted using Sentinel-1 data. The traditional global PSInSAR method operates as a non-partitioned, full-scene-processing technique that constructs a global adjacency network across the monitoring area and solves deformation parameters through a least squares adjustment. This approach requires full network connectivity to maintain the coef  
Figure 8: Global elevation residual results. (**a**) Three-dimensional visualization of global elevation residuals. (**b**) Statistical distribution of global elevation residuals. (**c**) Three-dimensional visualization of elevation residuals for man-made features in region c. (**d**) Three-dimensional visualization of elevation residuals for man-made features in region d.  
Figure 9: Global deformation rate results. (**a**) Three-dimensional visualization of global deformation rates. (**b**) Statistical distribution of global deformation rates. (**c**) Three-dimensional visualization of deformation rates for man-made features in region c. (**d**) Three-dimensional visualization of deformation rates for man-made features in region d.  
ficient matrix's full-rank condition--a critical prerequisite for stable parameter estimation. To accommodate memory constraints in monolithic processing, we applied 5 \(\times\) 5 spatial down-sampling (5:5 multi-looking) to the TerraSAR-X dataset, reducing the PS density from 25,000 points/km\({}^{2}\) to 930 points/km\({}^{2}\) and original candidate PS points from 13,867,836 to 547,133. This density reduction fragmented the network topology into isolated subnetworks. Consequently, only the largest connected subnetwork (416,089 PS points) was retained to prevent coefficient matrix singularity, while the discontinuous regions (e.g., rural villages, mountainous areas) where subnetworks failed to satisfy the rank condition for stable least squares adjustment were systematically excluded. For the Sentinel-1 dataset, we used data from the same period, with cropped images of size 9000 \(\times\) 1900 (5:1 multi-looking), which yielded 349,674 PS points (Table 2). The elevation residuals and deformation rates of matched PS points obtained from different methods were quantitatively evaluated using three metrics: mean bias, standard deviation (SD), and correlation coefficient (Cor). The results are shown in Table 3. Notably, during cross-validation with Sentinel-1 data, geometric consistency between TerraSAR-X and Sentinel-1 datasets was ensured by projecting line-of-sight (LOS) deformation rates to the vertical direction through  
\[v_{\text{vertical}}=\frac{v_{\text{LOS}}}{\cos\theta} \tag{11}\]  
where \(v_{\text{LOS}}\) is the LOS deformation rate, and \(\theta\) is the radar incidence angle (32.6\({}^{\circ}\) for TerraSAR-X, 44.0\({}^{\circ}\) for Sentinel-1). The results of the elevation residuals and deformation rates from different methods are shown in Figures 10 and 11. The traditional global PSInSAR method using the TerraSAR-X and Sentinel-1 datasets produced fewer PS points (3.2% and 2.7% of the total points from the proposed method, respectively), which significantly reduced its ability to monitor fine details, such as individual buildings. Additionally, the PS points in the left lower corner of the image, which corresponded to urban villages and sparsely distributed villages and roads on the mountains (highlighted in red in Figure 10), were not effectively captured. The SD of the elevation residuals for the traditional and proposed methods were 3.38 m and 2.77 m, respectively, with correlation coefficients of 0.99 for both, indicating a high consistency between the two methods. In areas with concentrated deformation rates (shown in the blue rectangles in Figure 11), both methods provided similar results. However, for