## 5 Discussion
### Feasibility Evaluation of PSInSAR Result Stitching Based on Overlap Constraints  
Traditional PSInSAR methods rely on a single control point and a global connected network for phase unwrapping and parameter transfer. The global network construction faces computational bottlenecks when dealing with a vast number of PS points. The block strategy proposed in this paper adjusts for systematic biases between sub-blocks by analyzing the statistical characteristics of deformation rate differences and elevation residual differences at common points in the overlap region. The theoretical assumption is that block differences arise from variations in local control points and network structures, manifesting as overall offsets (systematic biases) rather than random noise. By analyzing the statistical properties of the differences at common points in the overlap regions, the mathematical rationality and practical applicability of the method can be verified. The statistical analysis results of the effective overlap regions (Figure 12) show that the stitching error between sub-blocks was within a controllable range: (1) The mean standard deviations of elevation residual differences and deformation rate differences were 0.326 m and 0.126 mm/y, respectively. Among these, 88% of the effective overlap regions had an elevation residual standard deviation \(\leq\) 0.6 m and a deformation rate standard deviation \(\leq\) 0.2 mm/y, indicating that the differences between the sub-blocks were mainly driven by systematic biases. (2) The mean absolute values of the adjustment for elevation residuals and deformation rate were 18.201 m and 2.532 mm/y, respectively. The corresponding standard deviations (0.326 m and 0.126 mm/y) led to relative errors of 1.8% and 5.0%, respectively. This demonstrates that the adjustment values had far less dispersion than the mean, confirming the effectiveness of adjusting the entire block based on the mean value of differences at common points in the overlap region. Figure 12: Effective overlap region parameter difference and adjustment statistics distribution: (**a**) absolute value distribution of elevation residual adjustment; (**b**) standard deviation distribution of elevation residual differences; (**c**) absolute value distribution of deformation rate adjustment; (**d**) standard deviation distribution of deformation rate differences. The PSInSAR method based on grid partitioning and connectivity constraints has demonstrated significant technical advantages when dealing with high-resolution or ultra-high-resolution SAR imagery (strip-map or spotlight mode) with large data volumes and heterogeneous PS point distributions. This method breaks down massive amounts of data into independently processed sub-blocks via grid partitioning, reducing memory requirements to levels that are manageable by standard computational devices, thus overcoming the memory limitations that prevent traditional methods from processing ultra-large images. It significantly lowers the technical barriers for processing large-scale high-resolution data and ensures efficient full-area coverage. The independent processing characteristic of sub-blocks naturally adapts to distributed computing frameworks, allowing for parallel acceleration based on multi-core CPUs or computing clusters, drastically reducing the processing time and providing the technical foundation for near-real-time large-area deformation monitoring. In addition, the global constraint mechanism for the overlap regions between sub-blocks effectively suppresses discontinuities in the deformation field or elevation residual field at the stitching boundaries, ensuring spatial consistency across the entire region.