## 4 Results
### Real Data
For every case of \(N\), the interferograms with the largest temporal baseline were generated based on the EVD and the CPPCA results, respectively (see Figure 6a,b). Under a certain value of \(N\), an interferogram was affected by the temporal decorrelation to the utmost extent, which was especially suitable for demonstrating the capability of phase reconstruction techniques. It must be noted that the interferometric signals generated based on unreliable phase reconstruction results could be severely deviated from the true values. Therefore, the pixels whose PGoF values less than 0.3 were deleted for the purpose of intuitive representation. As can be seen from Figure 6, the signals contained in each interferogram appear a good consistency and no obvious high frequency noises are presented. The phase difference maps between the EVD and the CPPCA interferograms were generated via conjugated multiplications, as shown in Figure 6c. Since no abrupt signals could be observed, the interferometric signals from the CPPCA method agreed well with those from the EVD method. The approximate execution times of the two methods are listed in Table 1. When \(N=21\), the time required by the proposed CPPCA method was around 53% of that required by the EVD method. As \(N\) rose, such a CPPCA/EVD time cost ratio (TCR) was basically getting lower and lower. When \(N\) reached 101, the corresponding TCR was 10%. Evidently, the excellent computational efficiency of the proposed CPPCA method was further confirmed, in particular in the case of dealing with high dimensional SAR stacks. Figure 4: The number of SHP maps overlaid on the average SAR amplitude under a stack dimension of 21 (**a**), 31 (**b**),..., 101 (**i**). A PSI style MT-InSAR processing plan was further applied to the full SAR stack (\(N=101\)) reconstructed by the proposed CPPCA method. The PGoF was used as the criterion for selecting pixels with low phase dispersion and the corresponding threshold was set to 0.3. Overall, 763,843 pixels were picked out. Next, a reference network with respect to such pixels was constructed based on Delaunay triangulation. In total, the network contained 2,289,832 arcs. The local derivative based searching method [41] was used to inverse the double-differenced annual deformation rates and DEM errors. The ensemble phase coherence (EPC) [17] was used to screen unreliable estimates on each arcs. The threshold of EPC was set to 0.6, leading to a deletion of 38,998 arcs. The iterative reweighted least squares based integration method [42] was used to recover the absolute estimates on each pixel. The resulting annual deformation rate and DEM error maps are represented in Figure 7b,e. As can be seen from the deformation rate map, except for a very small portion of regions (e.g., the place indicated by the white arrow), no obvious ground surface displacements took place during the period of image acquisition. To some extent, a nonlinear trend seemed to be presented in the DEM error map. This is believed to be induced by the relevance of ephemeris data. The reconstructed stack from the conventional EVD method was processed as well. The corresponding results are presented in Figure 7a,d. Figure 5: The PGoF maps from the EVD method (**a**) and the CPPCA method (**b**) under different SAR stack dimensions. The corresponding difference maps are listed in (**c**). The corresponding difference maps were generated, which are shown in Figure 7.c.f. It can be clearly observed that the results from the both methods were almost identical to each other in the vast majority of regions. Hence the reliability of the proposed CPPCA method was further validated. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input-Data Dimension** & **CVD (min)** & **CPPCA (min)** \\ \hline
21 & 10.029 & 5.32 \\
31 & 24.555 & 8.125 \\
41 & 42.646 & 12.861 \\
51 & 69.8 & 14.172 \\
61 & 96.812 & 16.196 \\
71 & 137.523 & 20.621 \\
81 & 191.422 & 20.79 \\
91 & 214.924 & 23.763 \\
101 & 276.427 & 28.431 \\ \hline \hline \end{tabular}
\end{table}