## 4 Results
### Real Data  
101 Sentinel-1A interferometric wide-swath (IW) SAR images were used to further evaluate the proposed CPPCA method. These images were acquired from January 2017 to February 2020 over the Handan City, Hebei Province, China. The image acquired on 27 September 2019 was firstly selected as the master image. As the images were acquired in terrain observation by progressive scans (TOPS) mode, the most widely used enhanced spectral diversity (ESD) [40] method was carefully carried out to register other images to the master. To facilitate the discussion, the registered images were cropped to a relatively small size (approximately 1000 pixels in azimuth direction and 3000 pixels in range direction). The 1-arc seconds digital elevation model (DEM) provided by the shuttle radar topography  
Figure 2: The RMSEs (**a**) and the execution times (**b**) under different SAR stack dimensions. mission (SRTM) was used to estimate the topographic phase contributions. It must be noted that the topography induced phase signals were removed from the registered images before phase reconstruction. The average amplitude map with respect to the registered images is presented in Figure 3a. It can be observed that the upper part of the study area is dominated by rugged mountains which cause strong layover/foreshortening effect. On the other hand, the lower part is relatively flat, where a small county is situated. To examine the interferometric coherence of the input data, an overall coherence map is calculated by averaging each off-diagonal element's magnitude of each pixel's coherence matrix, which is represented in Figure 3b. Clearly, the interferometric coherence is well preserved over the county region covered by artificial architectures. However, the rest areas, in different degrees, are severely influenced by the decorrelation phenomenon. In these areas, the density of output products could be extremely low if traditional MT-InSAR techniques are applied to the original SAR data. For the purpose of deep investigation, the two methods were applied to the SAR stack constructed by the first \(N\) images (\(N=21,31,41,51,61,71,81,91,101\)). Following the idea of [19], a SHP identification process based on Kolmogorov-Smirnov (KS) test was introduced into the phase reconstruction process to improve the signal-to-noise ratio of the observation sample vectors. The corresponding window size was set to \(15\times 45\). The SHP number with respect to each pixel was recorded and the corresponding results are illustrated in Figure 4. As can be seen from the figure, the number of SHPs associated with each pixel decreased gradually as the stack dimension arises. It indicated the improvement of the SHP identification accuracy, as more samples were available for the KS test. In the areas covered by artificial architectures, where the PS points were densely distributed, small SHP numbers (less than 50) were observed. Figure 3: (**a**) The average SAR amplitude; (**b**) the average coherence map overlaid on (**a**). In order to evaluate the reliability of the both methods, the PGoF values with respect to each phase reconstruction result were calculated. The PGoF maps from the EVD and the CPPCA methods are demonstrated in the first column and the second column of Figure 5, respectively. It can be observed that each PGoF map was highly correlated to the average coherence map. In addition, the overall magnitude of each PGoF map was greater than that of the coherence map (Figure 3b). Consequently, it can be considered that the interferometric coherence of the input data was improved by the phase reconstruction process. The last column of Figure 5 presents the PGoF differences between the EVD and the CPPCA results. Obviously, the PGoF difference on each pixel was approximately equal to zero. Therefore, it can be concluded that the proposed method was as reliable as the traditional EVD method.