A Fusion Method for Temporal InSAR Landslide Deformation Monitoring Based on the Difference in Deformation Rates

Abstract

The present invention proposes a fusion method for time-series InSAR landslide deformation monitoring based on the difference in deformation rates, which includes acquiring and preprocessing synthetic aperture radar images of the monitoring area to obtain processed SAR image set data. Then, Stacking-InSAR is used to obtain the linear deformation rate, PS-InSAR is used to extract the high-precision deformation of PS points, and SBAS-InSAR is used to obtain the non-linear deformation rate. Coordinates are generated based on the deformation rates of each technical data point in combination with orbital and geometric information, and the creep, acceleration, and impending-sliding deformation areas are divided by ArcGIS and K-Means clustering. After that, the new images within the regional boundary are processed by SBAS-InSAR to obtain dynamic deformation information. Finally, the deformation rates of the three monitoring technologies are weighted and averaged. It effectively improves the accuracy of landslide deformation monitoring in complex terrains and environments, overcomes the errors caused by the vector monitoring point density and deformation rate problems in single interferometric measurement, and provides strong scientific support for accurately monitoring and analyzing landslide evolution, determining the scope, predicting trends, and early warning.

Description

Technical Field

[0001] The present invention relates to a fusion method for time - series InSAR landslide deformation monitoring based on the difference in deformation rates, belonging to the field of landslide deformation monitoring. Background Art

[0002] At present, the monitoring methods for landslide deformation include total station measurement method, crack meter measurement method, GPS measurement, and synthetic aperture radar differential interferometry (InSAR).

[0003] Among them, total station measurement can simultaneously perform angle (horizontal angle and vertical angle) measurement and distance measurement. In landslide deformation monitoring, by setting up a station at a control point with known coordinates and observing the monitoring points on the landslide body, information such as the horizontal angle, vertical angle, and inclined distance of the monitoring points can be obtained. Then, according to the trigonometric function relationship, the three - dimensional coordinates of the monitoring points are calculated. After multiple measurements and comparing the coordinate changes of the monitoring points at different times, the displacement conditions of the monitoring points in the horizontal and vertical directions can be obtained. This method can measure both horizontal and vertical displacements and comprehensively understand the deformation state of the landslide body. However, this method requires line - of - sight between the monitoring points and the control points, which may be restricted in areas with complex terrain, dense vegetation, or building obstructions.

[0004] Crack meter measurement is to monitor the changes in the width, depth, length, etc. of the surface cracks of the landslide body based on the crack meter. It usually installs sensors on both sides of the crack. When the crack changes, the sensor senses this change and converts it into an electrical signal or other measurable signals. This method has high precision and can accurately measure the minute changes in parameters such as crack width. Some high - precision crack meters can measure crack changes at the millimeter level or even smaller, providing a guarantee for timely detection of landslide hazards. However, it can only monitor the changes in the cracks themselves and cannot reflect the overall deformation of the landslide body. If relying solely on the crack meter, the deformation of other parts of the landslide body may be ignored, resulting in an incomplete assessment of the landslide hazard. Moreover, its installation position is relatively limited and needs to be installed at the crack. And if the crack position changes or new cracks appear, it may be necessary to reinstall or adjust the crack meter.

[0005] GPS measurement uses GPS satellite signal receiving equipment to measure the three - dimensional coordinates of the monitoring points and obtains the deformation amount of the slope surface by comparing the elevation data at different times. It has the characteristics of high precision and high efficiency, can achieve large - area and long - distance real - time dynamic monitoring, is not restricted by the line - of - sight condition, and can obtain horizontal and vertical displacement information simultaneously. However, GPS signals may be affected by terrain, building, etc. obstructions, and the accuracy will decrease in some complex environments, and the equipment cost is relatively high.

[0006] Differential Interferometric Synthetic Aperture Radar (D-InSAR) is a space-to-ground observation technology for information, which plays an important role in fields such as surface deformation monitoring. Stacking-InSAR technology is a temporal InSAR technology that uses a weighted average method to calculate the surface deformation rate. The data processing flow is relatively simple and efficient, and it can obtain the surface deformation characteristics of a large area in a short time. Since only linear superposition estimation is used to obtain the deformation rate, the Stacking-InSAR technology only extracts linear deformation information. This technology has good applicability in areas with obvious linear deformation trends, but in complex environments, it still needs to be combined with other means for further research. Persistent Scatterer Interferometry (PS-InSAR) is a technology for monitoring surface deformation using Synthetic Aperture Radar (SAR) data. It monitors the small deformations of the surface by identifying point targets with stable scattering characteristics in the time series, that is, persistent scatterers. PS-InSAR is suitable for large-scale long-time series ground deformation monitoring of landslides. The radar scattering characteristics remain stable over a long time, and the measurement accuracy is high. However, it is difficult to monitor the surface deformation in areas with complex terrain and high vegetation coverage density. The Small Baseline Subset Interferometry (SBAS-InSAR) technology reduces the influence of spatio-temporal decorrelation by selecting a set of interferometric pairs with small baselines, thereby realizing the monitoring of surface deformation. Compared with the PS-InSAR technology, the SBAS-InSAR technology increases the time sampling rate and spatial density of the observation data, and is not restricted by the linear deformation model. It can better recover the non-linear deformation characteristics of the surface and obtain the regional time series ground deformation. However, the processing flow of SBAS-InSAR is relatively complex, the computational amount is large, and the requirement for baseline selection is high. If the baseline is selected improperly, it may introduce errors and affect the accuracy of deformation monitoring. Aiming at the complex object scattering characteristics (uneven vegetation coverage) and deformation rate difference characteristics (measurement accuracy of different deformation rates in the same measurement area) of the landslide body, neither the Stacking-InSAR, PS-InSAR nor SBAR-InSAR technology can solve the monitoring accuracy error caused by the difference in the density of vector monitoring points brought about by the complex geological conditions of the landslide, as well as the method defects of adopting different solution technologies according to the deformation rate for adaptive zoning.

[0007] Landslides often occur under extremely complex geological conditions. Stacking-InSAR can provide large-area deformation rate information, which can be used to preliminarily determine the approximate scope and overall deformation trend of landslides, and give a macroscopic deformation background. PS-InSAR has good monitoring effects in areas with buildings or artificial facilities and areas with stable surface scattering characteristics, and can provide high-precision local deformation information of permanent scatterers. SBAS-InSAR can monitor areas with complex terrain and certain changes in vegetation cover, and can capture detailed information on non-linear deformation. Therefore, integrating Stacking-InSAR, PS-InSAR, and SBAS-InSAR technologies can make up for the limitations of single technologies, improve the accuracy and reliability of landslide monitoring, and have very important engineering significance for realizing the automatic selection of appropriate time-series differential interferometric measurement methods based on slope deformation rates. Summary of the Invention

[0008] To solve the above technical defects, the present invention provides a time-series InSAR landslide deformation monitoring fusion method based on deformation rate differences, which combines Stacking-InSAR, PS-InSAR, and SBAS-InSAR to monitor the measurement area and improve the measurement accuracy and efficiency.

[0009] Technical Solution: The present invention provides a time-series InSAR landslide deformation monitoring fusion method based on deformation rate differences, which includes the following steps:

[0010] Step 1: Obtain and preprocess the synthetic aperture radar images of the monitoring area to obtain the processed SAR image set data;

[0011] Step 2: Process the SAR image set data using the interferometric stacking method to obtain the linear deformation rate of the monitoring area; according to the preset high-speed deformation threshold, determine the high-speed deformation area of the interferometric stacking method in the monitoring area;

[0012] Step 3: Process the SAR image set data using the permanent scatterer synthetic aperture radar interferometry method to calculate the PS deformation rate of each data point in the monitoring area, and construct a PS deformation area based on the PS deformation rate and geographical position coordinate information of each data point;

[0013] Step 4: Perform interferometric processing on the SAR image set data using the small baseline subset synthetic aperture radar interferometry method to obtain the non-linear deformation rate of each data point in the monitoring area, and construct a non-linear deformation area based on the non-linear deformation rate and geographical position coordinate information of the data points;

[0014] Step 5: Determine the PS accelerated deformation area and the non-linear accelerated deformation area according to the PS deformation rate of the PS deformation area, the non-linear deformation rate of the non-linear deformation area, and the deformation rate threshold;

[0015] Step 6: According to the interference stacking method, the high-speed deformation area, PS accelerated deformation area, and non-linear accelerated deformation area are obtained, and the creep deformation area, accelerated deformation area, and pre-sliding deformation area of the monitoring area are obtained;

[0016] Step 7: Continuously obtain the synthetic aperture radar images of the monitoring area, and update the non-linear deformation rate of the pre-sliding deformation area according to the newly obtained synthetic aperture radar images of the monitoring area;

[0017] Step 8: According to the creep deformation area, accelerated deformation area, and pre-sliding deformation area, the linear deformation rate, PS deformation rate, and non-linear deformation rate are weighted and fused to obtain the time-series InSAR landslide deformation monitoring fusion result.

[0018] Further, in step 1, the synthetic aperture radar images of the monitoring area are obtained and preprocessed to obtain the processed SAR image set data, including:

[0019] Obtain M SAR image data of the Sentinel-1A satellite within the monitoring area, where M is greater than or equal to 25. Perform image registration, radiometric calibration, and geometric calibration on the obtained SAR image data, and incorporate the processed SAR image data into the set N to obtain the processed SAR image set data.

[0020] Further, in step 2, the interference stacking method is used to process the SAR image set data to obtain the linear deformation rate of the monitoring area, including:

[0021] Use the interference stacking method to perform stacking interference processing on the SAR image set data to obtain the landslide differential interference map;

[0022] Unwrap the landslide differential interference map to obtain the unwrapped phase, and obtain the unwrapped phase;

[0023] According to the relationship between the unwrapped phase and the surface deformation, perform weighted averaging to obtain the linear deformation rate of the area.

[0024] Further, the method for performing weighted averaging to obtain the linear deformation rate of the area according to the relationship between the unwrapped phase and the surface deformation includes:

[0025] Calculate the average deformation rate from the formula :

[0026]

[0027] where is the average deformation rate; n is the number of interference maps participating in the weighted average calculation; and are the time baseline of the i-th interference map and the phase value corresponding to it, respectively.

[0028] Further, according to a preset high-speed deformation threshold, determine the high-speed deformation area of the monitoring area by the InSAR method, including:

[0029] Integrate the linear deformation rate of the monitoring area and the corresponding geographical location information into a complete data set as the input data for cluster analysis, and perform standardization processing on the data.

[0030] Determine the preset high-speed deformation threshold according to the geological background of the study area and historical landslide monitoring data;

[0031] Through the geographic information system software, determine the area in the monitoring area where the linear deformation rate exceeds the preset high-speed deformation threshold as the high-speed deformation area by the InSAR method, and add the label of the high-speed deformation area by the InSAR method to this area.

[0032] Further, use the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) method to process the SAR image set data, calculate the PS deformation rate of the monitoring area, and construct the PS deformation area according to the PS deformation rate and geographical location coordinate information of the monitoring area, including:

[0033] Use the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) method to process the SAR image set data, calculate the PS points and PS deformation rate in the monitoring area, and screen the obtained PS points according to the coherence threshold C to obtain the screened PS points;

[0034] Organize the Persistent Scatterer deformation rate data of the screened PS points and the corresponding geographical location information into a complete data set, use the K-Means clustering method for cluster analysis, and determine the clusters with similar deformation rates and similar deformation characteristics as the deformation clusters;

[0035] Use the Delaunay triangulation algorithm and geographical location coordinates to construct a Delaunay triangulation network for each cluster obtained after cluster analysis, and perform grid correction according to the PS deformation rate and displacement direction. The corrected grid boundary is the boundary of the PS deformation area, and the PS deformation area is obtained.

[0036] Further, the coherence threshold C is taken as 0.2 ≤ C ≤ 0.4 when the vegetation in the monitoring area is dense, and 0.6 ≤ C ≤ 0.8 when the monitoring area is bare land and artificial building area.

[0037] Further, use the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method to perform interference processing on the SAR image set data, obtain the non-linear deformation rate of the monitoring area, and construct the non-linear deformation area according to the non-linear deformation rate and geographical location coordinate information of the data points, including:

[0038] Using small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) to perform interference processing on SAR image set data to obtain differential interferograms, and obtaining the non-linear deformation rate and deformation time series of each data point in the monitoring area through calculation,

[0039] Organize the non-linear deformation rates of each data point in the monitoring area and the corresponding geographical location information into a complete data set, and use the K-Means clustering method for clustering analysis. Determine the clusters with similar deformation rates and similar deformation characteristics as deformation clusters;

[0040] Use the Delaunay triangulation algorithm and geographical location coordinates to construct a Delaunay triangular network for each cluster obtained after clustering analysis, and perform grid correction according to the non-linear deformation rate and displacement direction. The corrected grid boundary is the boundary of the non-linear deformation area, and the non-linear deformation area is obtained.

[0041] Furthermore, step 5: Determine the PS accelerated deformation area and the non-linear accelerated deformation area according to the PS deformation rate in the PS deformation area, the non-linear deformation rate in the non-linear deformation area, and the deformation rate threshold, including:

[0042] If the PS deformation rate of the center point of each deformation area in the PS deformation area is greater than the deformation rate threshold I, determine that this area is the PS accelerated deformation area, and add the PS accelerated deformation area label to this area;

[0043] If the non-linear deformation rate of the center point of each deformation area in the non-linear deformation area is greater than the deformation rate threshold I, determine that this area is the non-linear accelerated deformation area, and add the non-linear accelerated deformation area label to this area.

[0044] Furthermore, step 6: Obtain the creep deformation area, the accelerated deformation area and the pre-sliding deformation area in the monitoring area according to the high-speed deformation area by the interference stacking method, the PS accelerated deformation area and the non-linear accelerated deformation area, including:

[0045] Define the area with only one of the labels of the high-speed deformation area by the interference stacking method, the PS accelerated deformation area label or the non-linear accelerated deformation area label as the creep deformation area;

[0046] Define the area with any two of the labels of the high-speed deformation area by the interference stacking method, the PS accelerated deformation area label or the non-linear accelerated deformation area label as the accelerated deformation area;

[0047] Define the area with all the labels of the high-speed deformation area by the interference stacking method, the PS accelerated deformation area label and the non-linear accelerated deformation area label as the pre-sliding deformation area.

[0048] Further, step 7: Continuously obtain synthetic aperture radar images of the monitoring area, and update the non-linear deformation rate of the pre-sliding deformation area according to the newly obtained synthetic aperture radar images of the monitoring area, including:

[0049] Continuously obtain synthetic aperture radar images of the monitoring area, screen the newly obtained synthetic aperture radar images of the monitoring area according to preset coherence conditions, mark the screened SAR image dataset as M1, incorporate the screened SAR image dataset M1 into the SAR image dataset, and mark the updated dataset as N1;

[0050] Continue to process the SAR image data in the pre-sliding deformation area using the small baseline set synthetic aperture radar interferometry technology for the updated dataset N1, and calculate the slope surface deformation rate and the signal-to-noise ratio SNR of the SBAS-InSAR processing in the pre-sliding deformation area B ;

[0051] The preset coherence condition is: perform image registration processing on the newly obtained SAR images, select a registered image as the master image and the coherence coefficient of each SAR image in the screened SAR image dataset M1. If m1 out of the m obtained coherence coefficients exceed the predetermined threshold Q, the registration result meets the preset coherence condition, where m1 > m * 0.6 and Q > 0.6.

[0052] Further, step 8: According to the creep deformation area, the accelerating deformation area, and the pre-sliding deformation area, perform weighted fusion on the linear deformation rate, the PS deformation rate, and the non-linear deformation rate to obtain the time-series InSAR landslide deformation monitoring fusion result, including:

[0053] Calculate the signal-to-noise ratio SNR of Stacking-InSAR through statistical analysis of the signals and noises of the landslide differential interferograms S ;

[0054] Calculate the signal-to-noise ratio SNR of PS-InSAR using the signal power and background noise power of the permanent scatterers R ;

[0055] Obtain the signal-to-noise ratio SNR of the SBAS-InSAR processing B ;

[0056] According to the signal-to-noise ratio SNR of Stacking-InSAR S 、the signal-to-noise ratio SNR of PS-InSAR R and the signal-to-noise ratio SNR of SBAS-InSAR B , perform weighted fusion on the linear deformation rate, the PS deformation rate, and the non-linear deformation rate to obtain the time-series InSAR landslide deformation monitoring fusion result.

[0057] Furthermore, the signal-to-noise ratio SNR of Stacking-InSAR is calculated by statistically analyzing the signals and noises of the landslide differential interferogram S , including:

[0058]

[0059] wherein, is the variance of the signal part, is the variance of the noise part,

[0060] The signal-to-noise ratio SNR of PS-InSAR is calculated by using the signal power of the permanent scatterer and the background noise power R , including:

[0061]

[0062] wherein, The signal power of the permanent scatterer is the background noise power;

[0063] The signal-to-noise ratio SNR of the SBAS-InSAR processing B The calculation method includes:

[0064]

[0065] wherein, is the coherence coefficient of the interferogram, and N is the number of independent samples participating in the averaging.

[0066] Furthermore, according to the signal-to-noise ratio SNR of Stacking-InSAR S , the signal-to-noise ratio SNR of PS-InSAR R and the signal-to-noise ratio SNR of SBAS-InSAR B , the linear deformation rate, the PS deformation rate and the non-linear deformation rate are weighted and fused to obtain the fusion result of the time-series InSAR landslide deformation monitoring, including:

[0067] The weighted average formula in the accelerated deformation area is:

[0068] (i = 1, 2, 3, j = 1, 2, 3, i ≠ j)

[0069]

[0070]

[0071] wherein, W 1 , W 2 and W 3They are the linear deformation rate, PS deformation rate, and non-linear deformation rate obtained by Stacking-InSAR, PS-InSAR, and SBAS-InSAR respectively. 、 and are their weighting coefficients;

[0072] The weighted average formula in the pre-sliding deformation area is:

[0073]

[0074] where W 1 、W 2 and W 3 are the linear deformation rate, PS deformation rate, and non-linear deformation rate obtained by Stacking-InSAR, PS-InSAR, and SBAS-InSAR respectively. 、 and are their weighting coefficients, and

[0075]

[0076]

[0077]

[0078] Furthermore, the method further includes:

[0079] Repeat steps 7 to 8 until there are no newly acquired synthetic aperture radar images of the monitoring area.

[0080] Beneficial effects: The present invention has the following advantages compared with the prior art:

[0081] (1) The present invention fuses the results of three monitoring methods, Stacking-InSAR, PS-InSAR, and SBAS-InSAR, so as to meet the measurement accuracy and efficiency requirements of different slope deformation rate areas within the same measurement area, realize the automatic selection of appropriate time-series differential interferometry methods according to the slope deformation rate, and effectively improve the measurement accuracy and efficiency.

[0082] (2) Stacking-InSAR improves the signal-to-noise ratio by superimposing multiple interferograms, can effectively extract small deformation signals, PS-InSAR can accurately measure permanent scatterers with high coherence and obtain high-precision deformation amounts, and SBAS-InSAR can use interferogram pairs of small baseline subsets to obtain continuous deformation information in a large area. The combination of the three can comprehensively and accurately obtain the deformation information of the landslide body at different scales, including both the overall deformation trend and local subtle changes.

[0083] (3) The combined use of Stacking-InSAR, PS-InSAR, and SBAS-InSAR can complement each other and adapt to more complex monitoring environments. Stacking-InSAR can improve the utilization rate of data through the fusion of multi-source data, and can obtain effective deformation information as much as possible in different environments. PS-InSAR can play a good role in urban areas or regions with stable interference conditions, and SBAS-InSAR has good adaptability to large-scale mountainous areas, vegetation-covered areas, etc.

[0084] (4) Stacking-InSAR can comprehensively analyze multi-temporal data as a whole, so as to better understand the mechanism of landslide deformation, improve the prediction ability of the development trend of landslide deformation, and provide a more scientific basis for disaster warning and prevention. Through the long-term monitoring of permanent scatterers by PS-InSAR, the long-term trend of landslide deformation can be obtained; the time series analysis method of SBAS-InSAR can capture the stage changes of deformation. The combined monitoring can combine the advantages of the three technologies in time series and spatial analysis, and more accurately analyze the spatio-temporal characteristics of landslide deformation. Description of the Drawings

[0085] Figure 1 is the flowchart of the method of the present invention;

[0086] Figure 2 is the schematic diagram of the regional division of the present invention. Detailed Embodiments

[0087] The present invention will be further described below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0088] Embodiment 1:

[0089] As Figure 1 shown is the flowchart block diagram of the method of the present invention. It can be seen from Figure 1 that the fusion method for monitoring landslide deformation by time-series InSAR according to different deformation rates provided by the present invention comprises the following steps:

[0090] Step 1: Obtain and preprocess the synthetic aperture radar images of the monitoring area to obtain the processed SAR image set data;

[0091] Step 2: Process the SAR image set data by using the interference stacking method to obtain the linear deformation rate of the monitoring area; according to the preset high-speed deformation threshold, determine the high-speed deformation area of the interference stacking method in the monitoring area;

[0092] Step 3: Process the SAR image set data using the persistent scatterer synthetic aperture radar interferometry method to calculate the PS deformation rates of each data point in the monitoring area, and construct a PS deformation area based on the PS deformation rates and geographical location coordinate information of each data point;

[0093] Step 4: Perform interferometric processing on the SAR image set data using the small baseline subset synthetic aperture radar interferometry method to obtain the non-linear deformation rates of each data point in the monitoring area, and construct a non-linear deformation area based on the non-linear deformation rates and geographical location coordinate information of the data points;

[0094] Step 5: Determine the PS accelerated deformation area and the non-linear accelerated deformation area based on the PS deformation rate of the PS deformation area, the non-linear deformation rate of the non-linear deformation area, and the deformation rate threshold;

[0095] Step 6: Based on the high-speed deformation area, the PS accelerated deformation area, and the non-linear accelerated deformation area obtained by the interferometric stacking method, obtain the creep deformation area, the accelerated deformation area, and the pre-sliding deformation area in the monitoring area;

[0096] Step 7: Continuously acquire synthetic aperture radar images of the monitoring area, and update the non-linear deformation rate of the pre-sliding deformation area according to the newly acquired synthetic aperture radar images of the monitoring area;

[0097] Step 8: Based on the creep deformation area, the accelerated deformation area, and the pre-sliding deformation area, perform weighted fusion on the linear deformation rate, the PS deformation rate, and the non-linear deformation rate to obtain the time-series InSAR landslide deformation monitoring fusion result.

[0098] Implementation principle: This embodiment fuses the results of three monitoring methods, namely Stacking-InSAR, PS-InSAR, and SBAS-InSAR, so as to meet the measurement accuracy and efficiency requirements of different slope deformation rate areas within the same measurement area, realize the automatic selection of the appropriate time-series differential interferometry method according to the slope deformation rate, and effectively improve the measurement accuracy and efficiency.

[0099] Embodiment 2:

[0100] As Figure 1 shown in the method flow block diagram of the present invention, it can be seen that the fusion method for monitoring landslide deformation by time-series InSAR provided by the present invention is as follows: Figure 1 It can be seen that the fusion method for monitoring landslide deformation by time-series InSAR provided by the present invention is as follows:

[0101] (1) Obtain M SAR image data, where M is greater than or equal to 25, and the SAR images in the image set are the image data obtained by the Sentinel-1A satellite observing the same monitoring area;

[0102] (2) The SAR image set data in step (1) is processed by stacking interferometry (Stacking-InSAR), where the spatial baseline is selected as 120 m and the temporal baseline is 60 d as the thresholds.

[0103] (3) The unwrapping of the landslide differential interferogram obtained in step (2) is carried out to obtain the phase change, and the regional average deformation rate is obtained by weighted averaging according to the unwrapped phase. The signal-to-noise ratio SNR is calculated by statistical analysis of the signal and noise of the landslide differential interferogram. S ;

[0104]

[0105]

[0106] where, is the average deformation rate; n is the number of interferograms participating in the weighted average calculation; and are the temporal baseline of the i-th interferogram and its corresponding phase value respectively, is the variance of the signal part obtained by Stacking-InSAR, is the variance of the noise part;

[0107] (4) The deformation rate results interpreted by Stacking-InSAR and the corresponding geographical location information are integrated into a complete data set as the input data for cluster analysis, and the data is standardized to eliminate the influence of different data magnitudes.

[0108] The deformation rate is the average deformation rate of the ground surface that can be calculated according to the relationship between the phase change and the ground surface deformation after the phase unwrapping of the differential interferogram by using Stacking-InSAR to obtain the phase change at different times.

[0109] The SAR image has certain geographical coordinate information when it is acquired, usually based on satellite orbit parameters and the Earth model for positioning. After generating the interferogram, the interferogram can be geocoded to correspond to the actual geographical coordinate system.

[0110] According to the geological background of the study area and the previous similar landslide monitoring data, etc., the threshold of the deformation rate is determined. The deformation rate exceeding -10 mm per year (that is, the absolute value of the deformation quantity exceeds 10 mm, because the deformation of the landslide is downward, so it is a negative value, and its absolute value exceeds 10 mm) is taken as the rapid deformation area, and exceeding -15 mm per year is taken as the high-speed deformation area. This part is used as the high-speed deformation area of the stacking interferometry method for subsequent analysis.

[0111] (5) Through the Geographic Information System (ArcGIS) software, the deformation rate data interpreted by Stacking-InSAR (which is the result of "performing weighted average to obtain the regional average deformation rate" in step 3) is classified and displayed, with different colors representing different degrees of deformation. In the deformation rate map interpreted by Stacking-InSAR, the deformation rate in red generally exceeds -10 mm, and different colors will be shown in the deformation map. The high-speed deformation area is determined by the threshold of the deformation rate determined in step 4; the part where the deformation rate exceeds the threshold is determined as the high-speed deformation area. In the Geographic Information System (ArcGIS) software, red indicates the area with a high deformation rate, orange indicates the area with significant deformation, and green indicates the area with a low deformation rate and relative stability. The obtained high-speed deformation area (deformation rate exceeding -15 mm per year) is marked as ; the value of n represents the number of divided accelerated deformation areas;

[0112] (6) The Persistent Scatterer Synthetic Aperture Radar Interferometry (PS-InSAR) method is used to process the SAR image set data obtained in step 1, the slope deformation rate in the monitoring area is calculated, and the obtained PS points are screened according to the coherence threshold C, and the signal-to-noise ratio SNR is calculated using the signal power and background noise power of the persistent scatterers R ;

[0113] The coherence threshold C takes 0.2 ≤ C ≤ 0.4 in the case of dense vegetation in the monitoring area, and 0.6 ≤ C ≤ 0.8 in the case of bare land and artificial building areas in the monitoring area;

[0114] In areas with dense vegetation, the radar signal may be blocked or scattered by the leaves, branches, etc. of plants, which will lead to signal attenuation and multipath effects (i.e., the signal is reflected back to the radar from different paths); these factors will reduce the coherence of the PS points. Therefore, in this environment, the coherence threshold is set relatively low (usually between 0.2 and 0.4) to adapt to the vegetation influence and still be able to screen out effective PS points. In bare land and artificial building areas, the ground surface is relatively flat, or artificial facilities such as buildings and roads will generate strong and stable radar reflection signals. The PS points in these areas usually have higher coherence because there are no large amounts of plants or obstacles blocking the signal. Therefore, the radar signal is more likely to be reflected back to the radar system, and the reflected signal is more stable;

[0115] The signal-to-noise ratio SNR R is:

[0116]

[0117] where is the signal power of the persistent scatterer, is the background noise power;

[0118] (7) K-Means clustering is an unsupervised learning algorithm commonly used to partition a dataset into K different clusters. In landslide monitoring, the K-Means clustering method is used to perform clustering analysis on the deformation rate data and corresponding geographical location information obtained through PS-InSAR and SBAB-InSAR technologies. The aim is to group pixel points with similar deformation rates and geographical distribution characteristics into one category, and divide the data points into regions with relatively intense deformation activities and stable regions;

[0119] First, randomly select K points from the dataset as the initial cluster centers. For each data point (including geographical coordinates and deformation rate), calculate its distance from all K cluster centers. The commonly used distance metric is the Euclidean distance:

[0120]

[0121] where , and are the coordinates and deformation rate of the cluster center;

[0122] Assign each data point to the cluster center closest to it. The specific operation is to select a cluster label for each point such that the Euclidean distance between the point and the cluster center is the smallest;

[0123]

[0124] Once all points have been assigned to the corresponding clusters, recalculate the center of each cluster. The new cluster center is the mean of all points within the cluster. For the k-th cluster, its new centroid is:

[0125]

[0126] where, is the number of points in the k-th cluster, is the set of all points belonging to cluster k;

[0127] Continue to iterate the above steps until the cluster centers no longer change or reach the preset maximum number of iterations. After clustering, each cluster is a set of point clusters obtained according to their deformation rate distribution and geographical distribution;

[0128] The deformation region is derived from the deformation cluster. After the pixel point set in a deformation cluster undergoes Delaunay triangulation and mesh correction, the deformation region is obtained, and the determined deformation region contains some or all of the pixel points in the deformation cluster. During the K-Means clustering process, each deformation cluster has a center. This center is obtained by calculating the average value of the geographical location coordinates (usually two-dimensional or three-dimensional space coordinates) of all pixel points within the cluster.

[0129] (8) Use the Delaunay triangulation algorithm and geographical location coordinates to construct a Delaunay triangular mesh for the PS points in each cluster obtained after cluster analysis, and perform mesh correction according to the deformation rate and displacement direction of the PS. The boundary of the corrected mesh is the boundary of the deformation region, and the obtained deformation region is marked as ;

[0130] Extract the point cluster set in step (7). First, select any three non-collinear points to form a triangle, and then continuously add the remaining points to the triangular mesh while ensuring the empty circle condition. After each insertion of a new point, check whether the empty circle condition is violated. If it is violated, adjust the triangular mesh and reconstruct the relevant triangles; this ensures that each vertex of the triangle corresponds to a deformation point and the edges between triangles do not cross other points. Traverse all triangles and determine whether the deformation rate of its vertices is more than twice that of the other vertices or the included angle of the displacement direction is greater than 45°. If it exceeds, reselect the vertices or adjust the shape of the triangle. If it does not exceed, stop traversing. Compare the deformation rate of each triangle with that of adjacent triangles. If the deformation rates of adjacent triangles differ greatly, the shared edge may be the boundary of the deformation region. According to the edges in the triangular mesh, merge the boundaries of similar deformation regions to form a continuous boundary contour. The finally obtained deformation region boundary can be represented by a polygon, and this boundary will divide the landslide deformation region;

[0131] The empty circle condition refers to the Delaunay empty circle rule in computer graphics, which is an algorithm for generating high-quality triangular meshes. The Delaunay empty circle rule was proposed by the Russian mathematician Victor Delaunay in 1934, and its core idea is to ensure that no other points are contained within the circumcircle of the generated triangles.

[0132] (9) Process the SAR image set data obtained in step (1) using Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), and process the calculated deformation rate and deformation time series according to the K-Means clustering method and Delaunay triangulation algorithm (such as the methods in steps (7) and (8)). Construct a Delaunay triangular network for the PS points in each cluster obtained after cluster analysis, and perform grid correction according to the deformation rate and displacement direction of the PS. The boundary of the corrected grid is the boundary of the deformation area, and mark the obtained deformation area as and calculate the signal-to-noise ratio SNR of SBAS-InSAR B :

[0133]

[0134] where is the coherence coefficient of the interferogram, and N is the number of independent samples participating in the average;

[0135] (10) Take the center points of the deformation clusters in the deformation areas obtained in steps (8) and (9) as the maximum deformation points, and judge whether their deformation rates exceed the deformation rate threshold I, so as to determine whether their respective deformation areas are accelerating deformation areas, and mark the accelerating deformation areas as PS deformation areas and non-linear deformation areas ; and add corresponding labels in the corresponding areas.

[0136] In areas of rock or hard soil (such as mountains, rocky mountainous areas), the surface hardness is usually high, the speed of landslide activities is slow, and the deformation rate is low. The deformation rate threshold I is taken as -5 mm / a - 10 mm / a. For areas of soft soil or debris flow (such as mudstone, alluvial soil areas), in such areas, landslide activities are relatively frequent and fast, and the deformation rate threshold I is taken as -25 mm / a - 15 mm / a. In urban areas or artificial building areas, due to the influence of artificial facilities such as buildings and roads, the deformation is usually significant, and the deformation rate threshold I is taken as -10 mm / a - 15 mm / a. In forest or vegetation areas, due to the interference of vegetation on radar signals, the coherence is low. Therefore, a lower deformation rate threshold is usually required to ensure that small deformations can be detected. So the deformation rate threshold I is taken as -5 mm / a - 15 mm / a;

[0137] (11) Only occur , or The area where the three intersect (the area has only one label) is defined as the creep deformation area, the area where the two of them intersect (the area has two labels) is defined as the accelerated deformation area, and the area where the three intersect (the area has three labels) is defined as the pre-slip deformation area; the division diagram is as follows Figure 2 shown.

[0138] (12) The SAR image data obtained after step (1) are used to calculate the deformation rate and its corresponding signal-to-noise ratio (SNR) in the pre-slip deformation zone using SBAS-InSAR. B ; (Continuously acquire SAR image data: For example, the images acquired in step 1 are up to December 2023. The impact of this step is from January 2024 to the present.)

[0139] (13) If new synthetic aperture radar (SAR) image data is obtained in the monitoring area, these new data are screened. If the preset coherence condition is met, the screened SAR image data is marked as N1 and included in the SAR image data set of step (1). The updated set is recorded as N1. The screening condition is that the newly acquired SAR image data and any one of the subsequent SAR image set data in step (11) meet the preset coherence condition:

[0140] The preset coherence condition is: perform image registration processing on the newly acquired SAR image, select a registered image as the main image and the coherence coefficient of each SAR image in the newly acquired SAR image data set M1 in step (12), if m1 of the m coherence coefficients obtained exceed the predetermined threshold Q, then the registration result satisfies the preset coherence condition, m1>m*0.6, Q>0.6; registration refers to the matching of geographic coordinates of different image graphics obtained by different imaging methods in the same area, including three aspects of processing: geometric correction, projection transformation and unified scale. ‌

[0141] (14) The updated SAR image data are used to continue to use the small baseline integrated aperture radar interferometry (SBAS-InSAR) technology to calculate the deformation rate and its corresponding signal-to-noise ratio (SNR) in the pre-slip deformation zone. B ;

[0142] (15) The slope deformation results calculated by Stacking-InSAR, PS-InSAR and SBAS-InSAR in the creep deformation zone, accelerated deformation zone and pre-slip deformation zone are weighted averaged to obtain the deformation rate and time series deformation information in the deformation zone. The time series deformation information here refers to the deformation amount that increases at the monitoring point with time, which is the time-displacement curve obtained by SBAS interpretation.

[0143] If an accurate time displacement curve is required, the time-accumulative displacement sequences solved by Stacking-InSAR, PS-InSAR, and SBAS-InSAR are obtained respectively. The accumulative displacements at the same time point of each method are weighted and calculated according to the weighting coefficients of the following formula to obtain a new time-accumulative displacement sequence.

[0144] The weighted average formula in the accelerated deformation area for step 15 is:

[0145] (i = 1, 2, 3, j = 1, 2, 3, i ≠ j)

[0146]

[0147]

[0148] Among them, W 1 , W 2 and W 3 are the deformation rates obtained by Stacking-InSAR, PS-InSAR, and SBAS-InSAR respectively, , and are their weighting coefficients.

[0149] The weighted average formula in the impending-sliding deformation area is:

[0150]

[0151] Among them, W 1 , W 2 and W 3 are the deformation data obtained by Stacking-InSAR, PS-InSAR, and SBAS-InSAR respectively, , and are their weighting coefficients, and

[0152]

[0153]

[0154]

[0155] (16) Repeat steps (13) to (15) until no new SAR image data is obtained. Inside the landslide body, the method fusion of landslide deformation in space and time is finally realized, and the long-term trend and short-term variation law of landslide deformation are analyzed.

[0156] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0157] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0158] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0160] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A time series InSAR landslide deformation monitoring fusion method based on deformation rate difference, characterized in that: The following steps are included: Step 1: Acquire and preprocess the synthetic aperture radar image of the monitoring area to obtain the processed SAR image set data; Step 2: Use the interferometric stacking method to process the SAR image set data to obtain the linear deformation rate of the monitoring area; determine the interferometric stacking method high-speed deformation area of ​​the monitoring area according to the preset high-speed deformation threshold; Step 3: Use the permanent scatterer synthetic aperture radar interferometry method to process the SAR image data set, calculate the PS deformation rate of each data point in the monitoring area, and construct the PS deformation zone based on the PS deformation rate and geographical location coordinate information of each data point; Step 4: Use the small baseline set synthetic aperture radar interferometry method to perform interference processing on the SAR image set data to obtain the nonlinear deformation rate of each data point in the monitoring area, and construct the nonlinear deformation area based on the nonlinear deformation rate and geographical location coordinate information of the data point; Step 5: Determine the PS accelerated deformation zone and the nonlinear accelerated deformation zone according to the PS deformation rate of the PS deformation zone, the nonlinear deformation rate of the nonlinear deformation zone, and the deformation rate threshold; Step 6: According to the high-speed deformation zone, PS accelerated deformation zone and nonlinear accelerated deformation zone of the interference stacking method, the creep deformation zone, accelerated deformation zone and pre-slip deformation zone of the monitoring area are obtained; Step 7: Continuously acquire synthetic aperture radar images of the monitoring area, and update the nonlinear deformation rate of the impending slip deformation zone according to the newly acquired synthetic aperture radar images of the monitoring area; Step 8: According to the creep deformation zone, accelerated deformation zone and pre-slip deformation zone, the linear deformation rate, PS deformation rate and nonlinear deformation rate are weighted fused to obtain the time series InSAR landslide deformation monitoring fusion results.

2. The time series InSAR landslide deformation monitoring fusion method based on deformation rate difference according to claim 1 is characterized in that: In step 1, the synthetic aperture radar image of the monitoring area is acquired and preprocessed to obtain the processed SAR image set data, including: Obtain M pieces of SAR image data from satellites within the monitoring area, where M is greater than or equal to 25, perform image registration, radiation correction, and geometric correction on the obtained SAR image data, merge the processed SAR image data into set N, and obtain the processed SAR image set data.

3. The time series InSAR landslide deformation monitoring fusion method based on deformation rate difference according to claim 1 is characterized in that: In step 2, the interferometric stacking method is used to process the SAR image set data to obtain the linear deformation rate of the monitoring area, including: The interference stacking method is used to perform stacking interference processing on the SAR image set data to obtain the landslide differential interference map; Unwrapping the landslide differential interference pattern to obtain the unwrapped phase; According to the relationship between the unwrapped phase and the surface deformation, the linear deformation rate of the area is obtained by weighted averaging; According to the relationship between the unwrapped phase and the surface deformation, the method of obtaining the linear deformation rate of the area by weighted average includes: The average deformation rate is calculated by the formula : ； in, is the average deformation rate; n is the number of interference patterns involved in the weighted average calculation; and are the time baseline of the i-th interference pattern and its corresponding phase value respectively; In step 2, according to a preset high-speed deformation threshold, the high-speed deformation zone of the monitoring area using the interferometric stacking method is determined, including: The linear deformation rate and corresponding geographical location information of the monitoring area are integrated into a complete data set as the input data for cluster analysis, and the data are standardized; Through geographic information system software, the area in the monitoring area where the linear deformation rate exceeds the preset high-speed deformation threshold is determined as the interference stacking method high-speed deformation zone, and the interference stacking method high-speed deformation zone label is added to the area.

4. The time series InSAR landslide deformation monitoring fusion method based on deformation rate difference according to claim 1 is characterized in that: Step 3: Use the permanent scatterer synthetic aperture radar interferometry method to process the SAR image data set, calculate the PS deformation rate of the monitoring area, and construct the PS deformation zone according to the PS deformation rate and geographic location coordinate information of the monitoring area, including: The SAR image data is processed by using the permanent scatterer synthetic aperture radar interferometry method to calculate the PS points and PS deformation rates in the monitoring area, and the obtained PS points are screened according to the coherence threshold C to obtain the screened PS points. The deformation rate data of permanent scatterers at the sieved PS points and the corresponding geographical location information were organized into a complete data set. The K-Means clustering method was used for cluster analysis, and clusters with similar deformation rates and similar deformation characteristics were identified as deformation clusters. The Delaunay triangulation algorithm and geographic location coordinates are used to construct a Delaunay triangulation network for the cluster clusters in each cluster obtained after cluster analysis, and the grid is corrected according to the deformation rate and displacement direction of PS. The corrected grid boundary is the boundary of the PS deformation zone, and the PS deformation zone is obtained.

5. The time series InSAR landslide deformation monitoring fusion method based on deformation rate difference according to claim 1 is characterized in that: Step 4: Use the small baseline set synthetic aperture radar interferometry method to perform interference processing on the SAR image set data to obtain the nonlinear deformation rate of the monitoring area, and construct the nonlinear deformation area based on the nonlinear deformation rate and geographic location coordinate information of the data points, including: The SAR image data is processed by small baseline synthetic aperture radar interferometry to obtain differential interferograms, and the nonlinear deformation rate and deformation time series of each data point in the monitoring area are obtained by solving the problem. The nonlinear deformation rate and corresponding geographical location information of each data point in the monitoring area are sorted into a complete data set, and the K-Means clustering method is used for cluster analysis. The clusters with similar deformation rates and similar deformation characteristics are identified as deformation clusters. The Delaunay triangulation algorithm and geographic location coordinates are used to construct a Delaunay triangulation network for the cluster clusters in each cluster obtained after cluster analysis, and the grid is corrected according to the nonlinear deformation rate and displacement direction. The corrected grid boundary is the nonlinear deformation zone boundary, and the nonlinear deformation zone is obtained.

6. The time series InSAR landslide deformation monitoring fusion method based on deformation rate difference according to claim 1 is characterized in that: Step 5: According to the PS deformation rate of the PS deformation zone, the nonlinear deformation rate of the nonlinear deformation zone, and the deformation rate threshold, the PS accelerated deformation zone and the nonlinear accelerated deformation zone are determined, including: If the PS deformation rate of the center point of each deformation zone in the PS deformation zone is greater than the deformation rate threshold I, the zone is determined to be a PS accelerated deformation zone, and a PS accelerated deformation zone label is added to the zone; If the nonlinear deformation rate of the center point of each deformation zone of the nonlinear deformation zone is greater than the deformation rate threshold I, the zone is determined to be a nonlinear accelerated deformation zone, and a nonlinear accelerated deformation zone label is added to the zone.

7. The time series InSAR landslide deformation monitoring fusion method based on deformation rate difference according to claim 1 is characterized in that: Step 6: According to the high-speed deformation zone, PS accelerated deformation zone and nonlinear accelerated deformation zone of the interference stacking method, the creep deformation zone, accelerated deformation zone and pre-slip deformation zone of the monitoring area are obtained, including: A region having only one of the labels of the high-speed deformation region of the interference stacking method, the PS accelerated deformation region, or the nonlinear accelerated deformation region is defined as a creep deformation region; An area having only any two of the labels of the high-speed deformation area of ​​the interference stacking method, the PS accelerated deformation area, or the nonlinear accelerated deformation area is defined as an accelerated deformation area; All regions with the interference stacking method high-speed deformation zone label, PS accelerated deformation zone label and nonlinear accelerated deformation zone label are defined as pre-slip deformation zones.

8. The time series InSAR landslide deformation monitoring fusion method based on deformation rate difference according to claim 1 is characterized in that: Step 7: Continuously obtain synthetic aperture radar images of the monitoring area, and update the nonlinear deformation rate of the pre-slip deformation zone according to the newly obtained synthetic aperture radar images of the monitoring area, including: Continuously acquire synthetic aperture radar images of the monitoring area, screen the newly acquired synthetic aperture radar images of the monitoring area according to the preset coherence condition, mark the screened SAR image dataset as M1, incorporate the screened SAR image dataset M1 into the SAR image data set, and mark the updated set as N1; The updated dataset N1 continues to use the small baseline synthetic aperture radar interferometry technique to process the SAR image data in the pre-slip deformation zone, and calculates the slope deformation rate in the pre-slip deformation zone and the signal-to-noise ratio (SNR) of SBAS-InSAR processing. B ; The preset coherence condition is: perform image registration processing on the newly acquired SAR image, select a registered image as the main image and the coherence coefficient of each SAR image in the screened SAR image data set M1, if m1 coherence coefficients among the m coherence coefficients obtained exceed the preset threshold Q, then the registration result meets the preset coherence condition, wherein m1>m*0.6, Q>0.

6.

9. The time series InSAR landslide deformation monitoring fusion method based on deformation rate difference according to claim 1 is characterized in that: Step 8: According to the creep deformation zone, accelerated deformation zone and pre-slip deformation zone, the linear deformation rate, PS deformation rate and nonlinear deformation rate are weighted fused to obtain the time series InSAR landslide deformation monitoring fusion results, including: The signal-to-noise ratio (SNR) of Stacking-InSAR is calculated by statistical analysis of the signal and noise of the landslide differential interferogram. S ; Calculation of PS-InSAR signal-to-noise ratio (SNR) using signal power and background noise power of permanent scatterers R ; According to the signal-to-noise ratio (SNR) of Stacking-InSAR S , PS-InSAR signal-to-noise ratio (SNR) R and the signal-to-noise ratio (SNR) of SBAS-InSAR B , weighted fusion of linear deformation rate, PS deformation rate and nonlinear deformation rate is performed to obtain the fusion results of time series InSAR landslide deformation monitoring; The signal-to-noise ratio (SNR) of Stacking-InSAR is calculated by statistical analysis of the signal and noise of the landslide differential interferogram. S ,include: ； in, is the variance of the signal part, is the variance of the noise part, Calculation of PS-InSAR signal-to-noise ratio (SNR) using signal power and background noise power of permanent scatterers R ,include: ； in, Signal power of permanent scatterers, is the background noise power; The signal-to-noise ratio (SNR) of the SBAS-InSAR process B The calculation methods include: ； in, is the coherence coefficient of the interference pattern, N is the number of independent samples involved in the average; According to the signal-to-noise ratio (SNR) of Stacking-InSAR S , PS-InSAR signal-to-noise ratio (SNR) R and the signal-to-noise ratio (SNR) of SBAS-InSAR B , weighted fusion of linear deformation rate, PS deformation rate and nonlinear deformation rate is performed to obtain the fusion results of time series InSAR landslide deformation monitoring, including: The weighted average formula in the accelerated deformation zone is: i=1,2,3,j=1,2,3,i≠j; ； ； Among them, W1, W2 and W3 are the linear deformation rate, PS deformation rate and nonlinear deformation rate obtained by Stacking-InSAR, PS-InSAR and SBAS-InSAR respectively. , and are their weighting coefficients; The weighted average formula in the pre-slip deformation zone is: ； Where W1, W2 and W3 are the linear deformation rate, PS deformation rate and nonlinear deformation rate obtained by Stacking-InSAR, PS-InSAR and SBAS-InSAR respectively. , and are the weighting coefficients of linear deformation rate, PS deformation rate and nonlinear deformation rate, respectively, and ； ； 。 10. The time series InSAR landslide deformation monitoring fusion method based on deformation rate difference according to claim 1 is characterized in that: The method further comprises: Repeat steps 7 and 8 until there is no new synthetic aperture radar image of the monitoring area.

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