## 3 Methods
### Sbas\(-\)Ipta  
SBAS is a differential interferometry method proposed by [PERSON] [46] that can efficiently obtain continuous deformation data. It first divides all SAR images into short baseline subsets according to temporal and spatial baselines and then performs interferometry on a single subset. The minimum cost flow unwrapping method is used to perform phase unwrapping, and the observation equation is established with high-coherence points. Finally, the singular value decomposition (SVD) [47] method is used to obtain the minimum norm solution of the velocity vector. The integral can obtain the deformation over each time period. It uses multiple master images to reduce the amount of SAR data required and can effectively weaken the influence of decoherence and topographic changes by controlling the temporal and spatial baselines [48].  
The IPTA processing module designed by [PERSON] in Gamma software ([[https://www.gamma-rs.ch/](https://www.gamma-rs.ch/)]([https://www.gamma-rs.ch/](https://www.gamma-rs.ch/)), accessed on 10 October 2023) can independently set up multiple master images or a single master image and reduce the error interference via regression analysis and multiple iterations to obtain surface deformation [43]. The basic idea is to select a target point with stable backscattering characteristics and obtain the linear deformation, DEM difference, and residual of the target point via one-dimensional or two-dimensional regression analysis. After the DEM error is corrected, the atmospheric delay in the residual is separated from the nonlinear deformation by time-space domain filtering, so that the deformation time-series data of each target point can be obtained. This technique gradually weakens the interference of decoherence noise, atmospheric delay, and DEM error in the results through multiple iterations [43; 49]. Unlike SBAS, IPTA only performs interference processing and time-series analysis on permanent scatterer targets with high-coherence points and cannot obtain the complete range and shape of landslides. Therefore, when SBAS and IPTA technologies are combined, they can learn from each other and obtain more accurate results for continuous deformation. Both of these methods are relatively mature at present, and their specific theories will not be described in this paper.  
The specific processing flow of SBAS\(-\)IPTA is as follows:  
* Select the master image and resample the slave image into the master image space; the master image will try to select the image with less vegetation coverage and the date in the middle.
* According to the imaging quality and quantity of different images, set an appropriate threshold temporal and spatial baselines for differential interference; combine external DEM data to remove the terrain phase of the interferometric phase.
* Use the adaptive filtering method to filter the interference image; the filtering window is usually 32 or 64.
* Use the minimum cost flow (MCF) method for phase unwrapping [50].
* Select high-quality interferometry pairs and perform baseline refinement, re-interferometry, filtering, unwrapping, etc.
* Select permanent scatterer points (PS) with high coherence and obtain a set of differential interference points [51];
* Select a stable reference point (usually buildings) for iterative regression analysis; decompose the deformation phase, elevation correction phase, and residual phase; and iterate until there is no obvious phase jump in the residual phase.
* Separate the atmospheric error phase from the residual phase by using the spatial-temporal domain filtering method.
* Establish an observation equation using the acquired high-coherence points and use singular-value decomposition (SVD) to obtain the deformation rate and time-series results [47].