## 2 Methodology  
### Selection of PS/SDF/DS Target Points  
The time series difference interferometric phase diagram was acquired according to the interference pair combination principle of PS-InSAR technique and SBAS-InSAR technique. A set of high-coherence candidates (PS and SDF points) were chosen preliminarily based on the amplitude deviation threshold method [17]. The main process of the method includesobtaining the amplitude deviation value \(D_{A}\) of the image pixel and setting a threshold value \(T_{A}\) of the amplitude deviation index. If \(D_{A}<T_{A}\), the pixel was determined as the high coherence candidate. Otherwise, it was a non-high CPs. The calculation formula of \(D_{A}\) can be expressed as  
\[D_{A}=\frac{\sigma_{A}}{m_{A}} \tag{1}\]  
where \(\sigma_{A}\) and \(m_{A}\) are the mean and variance of amplitude.  
The phase information of these candidates was calculated. Moreover, a phase stability index was calculated according to phase error statistical characteristics. The candidates were screened for the second time to determine the ultimate high CPs.  
\[\gamma_{x}=\frac{1}{N}\left|\sum_{i=1}^{N}\exp\left\{\sqrt{-1}\left(\phi_{x,i} -\overline{\phi}_{x,i}-\hat{\phi}_{top,x,i}\right)\right\}\right| \tag{2}\]  
where \(N\) is the quantity of interference images, \(\overline{\phi}_{x,i}\) is the spatial correlation phase in interference phase, and \(\hat{\phi}_{top,x,i}\) is the estimated value of topographic phase.  
The selection of DS points is composed of homogeneous pixel recognition and phase optimization [19; 20]. The former process is to recognize the distributed target pixels which have similar scattering properties with the sample data as the homogeneous pixel set based on the hypothesis testing. Similar to the comment above mentioned selection process of high CPs, after acquisition of the differential interferograms from a single master image interferometric combination or a multiple master image interferometric combination, the homogeneous pixel points were recognized by the FaSHIPS algorithm [22]. The fundamental of the FaSHPS algorithm is to solve the confidence interval from the hypothesis test problem that arises during homogeneous pixel recognition. This can be expressed as follows  
\[P\left\{\mu(p)-z_{1-a/2}\cdot 0.52\cdot\mu(p)/\sqrt{N\cdot L}<\overline{A}<\mu(p) +z_{1-a/2}\cdot 0.52\cdot\mu(p)/\sqrt{N\cdot L}\right\}=1-a \tag{3}\]  
where \(\mu(p)\) is the expectation of time series strength of the pixel \(p\). \(P\{\cdot\}\) refers to probability and \(z_{1-a/2}\) is the \(1-\alpha/2\) quantile of standard normal distribution.  
The pixels in the adjacent region with average strength in the interval were recognized as homogeneous pixels of the reference point P. Additionally, homogeneous pixels of each pixel in the SAR images were recognized. Homogeneous pixels with a quantity higher than the given threshold were chosen as the initial DS candidate. Furthermore, phases of DS points were optimized by the covariance matrix eigenvalue decomposition algorithm. The DS points with good coherence and high SNR were screened by setting a threshold for the degree of fitting among interferometric phases before and after phase optimization [23].