Surface deformation monitoring method, terminal device and computer readable storage medium

Abstract

The present invention discloses a method for monitoring surface deformation, a terminal device and a computer-readable storage medium, wherein the method comprises acquiring SAR images of a to-be-monitored area at different monitoring times to obtain multiple SAR images; constructing a small baseline set based on the multiple SAR images, and performing phase unwrapping on the multiple SAR images in the small baseline set after differential interference to obtain multiple phase unwrapping maps; judging whether the unwrapping phases of the multiple phase unwrapping maps meet preset conditions, and if not, updating the corresponding phase unwrapping maps; and determining the deformation sequence of the to-be-monitored area based on the updated phase unwrapping maps. The present invention can make full use of redundant phase unwrapping maps generated based on multi-scene SAR images, automatically identify and correct unwrapping phases with unwrapping errors and null values, provide accurate surface deformation time series and complete surface deformation field, and overcome the influence of temporal and spatial incoherence on monitoring results.

Description

Technical Field

[0001] The present invention discloses a surface deformation monitoring method, terminal equipment and computer-readable storage medium, and specifically relates to a high-precision surface deformation monitoring method, terminal equipment and computer-readable storage medium based on SBAS-InSAR phase recovery and correction technology, and belongs to the technical field of surface deformation monitoring. Background Art

[0002] Remote sensing technology based on SAR images can be effectively applied to the investigation, monitoring and early warning of geological disasters such as earthquakes, landslides, volcanoes, ground fissures, etc. At present, there are two main types of SAR technology, including offset tracking based on intensity information and SAR interferometry (InSAR) based on phase.

[0003] The SAR offset tracking technology based on intensity information uses the intensity information between two SAR images and estimates the offset in the range and azimuth directions based on the cross-correlation technology. It can monitor large gradient deformation, but the measurement accuracy is above the meter level, and it is difficult to accurately monitor small gradient deformation. The phase-based SAR interferometry method uses two SAR antennas (or one antenna for repeated observations) to obtain two singular and complex images with a certain viewing angle difference in the same area. After performing DEM phase removal, filtering, phase unwrapping, atmospheric phase removal and other steps on the phase obtained by interfering the two images, the observation of small surface deformation is achieved.

[0004] SBAS-InSAR technology is based on the phase-based SAR interferometry method. According to the imaging time and orbit vector of multi-view SAR images, a small baseline set (SBAS) that meets a certain temporal and spatial baseline threshold is generated. The redundant observations provided by the small baseline set are used for adjustment to obtain the deformation time series of the surface with millimeter-level accuracy. The key step in SBAS-InSAR technology is phase unwrapping, which aims to restore the phase wrapped between [-ππ] after SAR image interference to the true phase relative to the reference point according to the Itoh hypothesis. However, in the process of phase unwrapping, due to the influence of surface vegetation and other decoherence factors, there may be errors in the unwrapped phase. When the decoherence phenomenon is serious, it affects the accuracy of InSAR monitoring results. Summary of the invention

[0005] The purpose of this application is to provide a surface deformation monitoring method, terminal equipment and computer-readable storage medium to solve the technical problem of serious decoherence phenomenon existing in the prior art, resulting in low deformation monitoring accuracy.

[0006] A first aspect of the present invention provides a method for monitoring ground deformation, comprising:

[0007] Acquire SAR images of the area to be monitored at different monitoring times to obtain multiple SAR images;

[0008] A small baseline set is constructed according to the multiple SAR images, and phase unwrapping is performed on the multiple SAR images in the small baseline set after differential interference to obtain multiple phase unwrapping images;

[0009] Determine whether the unwrapped phases of the multiple phase unwrapping graphs meet a preset condition, and if not, update the corresponding phase unwrapping graphs;

[0010] The deformation sequence of the area to be monitored is determined according to the updated phase unwrapping map.

[0011] Preferably, judging whether the unwrapped phases of the plurality of phase unwrapping graphs meet a preset condition specifically includes:

[0012] Determining, from the plurality of SAR images in the small baseline set, a plurality of image groups whose baselines meet preset requirements, each of the image groups including two SAR images;

[0013] It is determined whether the unwrapped phase of the phase unwrapped graph corresponding to the two SAR images in each group is a non-null value.

[0014] Preferably, updating the corresponding phase unwrapping diagram specifically includes:

[0015] Taking the middle monitoring time of the two SAR images as the center, obtaining phase unwrapping images corresponding to a plurality of SAR images within a set time distance from the center;

[0016] Determining an average phase change rate according to phase unwrapping graphs corresponding to a plurality of SAR images within a set time distance from the center;

[0017] The average phase change rate is used to complete the unwrapped phase corresponding to the null value, and the corresponding phase unwrapping diagram is updated.

[0018] Preferably, the average phase change rate is determined according to a first formula, which is:

[0019]

[0020] Where V is the average phase change rate, ph l is the unwrapped phase in the lth phase unwrapping diagram, ΔT l is the monitoring time interval between the two SAR images corresponding to the lth phase unwrapping map, and N is the total number of phase unwrapping maps corresponding to the multiple SAR images within a set time length from the center.

[0021] Preferably, using the average phase change rate to complete the unwrapped phase corresponding to the null value specifically includes:

[0022] multiplying the average phase change rate and the monitoring time interval of the two SAR images;

[0023] The unwrapped phase corresponding to the null value is completed using the result of the multiplication.

[0024] Preferably, after updating the corresponding phase unwrapping diagram, the method further includes:

[0025] constructing multiple phase closed loops based on the multiple SAR images in the small baseline set to determine the phases with unwrapping errors;

[0026] removing the unwrapping error from the corresponding phase;

[0027] Correspondingly, according to the updated phase unwrapping diagram, the deformation sequence of the area to be monitored is determined, specifically:

[0028] The deformation sequence of the area to be monitored is determined according to the multiple phase unwrapping images after the unwrapping errors are removed.

[0029] Preferably, constructing multiple phase closed loops based on multiple SAR images in the small baseline set to determine the phase with unwrapping error specifically includes:

[0030] constructing a phase closed loop using three of the multiple SAR images in the small baseline set to obtain multiple phase closed loops;

[0031] The residual of each phase closed loop is obtained, and combined with the preset residual threshold, the phase with unwrapping error in the corresponding phase unwrapping graph is determined.

[0032] Preferably, obtaining the residual of each phase closed loop specifically includes:

[0033] The residual error of each phase closed loop is determined according to a second formula, wherein the second formula is:

[0034]

[0035] In the formula, is the residual of the phase closure loop constructed by the SAR images acquired at the i, j, and k monitoring times, is the unwrapped phase between the SAR image acquired at the i-th monitoring time and the SAR image acquired at the j-th monitoring time, is the unwrapped phase between the SAR image acquired at the jth monitoring time and the SAR image acquired at the kth monitoring time, is the unwrapped phase between the SAR image acquired at the kth monitoring time and the SAR image acquired at the ith monitoring time.

[0036] Preferably, removing the unwrapping error from the corresponding phase specifically includes:

[0037] Considering the unwrapping error as a gross error, and determining an estimate of the gross error;

[0038] constraining the estimation of the gross error to a preset integer multiple;

[0039] The constrained gross error estimates are removed from the corresponding phases.

[0040] A second aspect of the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0041] A third aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0042] Compared with the prior art, the surface deformation monitoring method, terminal device and computer-readable storage medium of the present invention have the following beneficial effects:

[0043] The surface deformation monitoring method and terminal device of the present invention can make full use of the redundant phase unwrapping map generated by multi-view SAR images through the SBAS (Small Baseline Subset) strategy, automatically identify and correct the unwrapping phase with unwrapping errors and null values, provide accurate surface deformation time series and complete surface deformation field, and overcome the influence of time and space incoherence on SBAS-InSAR monitoring results, especially the influence of long-term incoherence causing disconnection of the adjustment network. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the flow of the surface deformation monitoring method of the present invention;

[0045] Figure 2 It is a comparison diagram of simulation data between the method of the present invention and the existing method;

[0046] Figure 3 A time-space baseline distribution map of a small baseline set for high-precision inversion of landslide surface deformation provided by an example of the present invention;

[0047] Figure 4 The unwrapped phase recovery and correction Sentinel-1 partial surface deformation map of the Mianshawan landslide from January 4, 2019 to March 25, 2022 provided by the example of the present invention;

[0048] Figure 5The unwrapped phase recovery and correction Sentinel-1 surface deformation time series diagram of the Mianshawan landslide from January 4, 2019 to March 25, 2022 provided for the example of the present invention. DETAILED DESCRIPTION

[0049] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0050] like Figure 1 As shown, the surface deformation monitoring method of the embodiment of the present invention includes:

[0051] Step 1: Acquire SAR images of the area to be monitored at different monitoring times to obtain multiple SAR images.

[0052] In the embodiment of the present invention, in order to facilitate the registration of multiple SAR images, while acquiring multiple SAR images of the area to be monitored, digital elevation model (DEM) data of the area to be monitored is also acquired, which is referred to as DEM data.

[0053] Then all SAR images are processed into single-view complex images.

[0054] In an embodiment of the present invention, for SAR images from the same orbit, the changes in the time baseline, the spatial baseline and the Doppler center frequency are considered to determine the main image in the orbit data set, and all SAR images in the data set are co-registered to the main image to obtain multiple registered SAR images.

[0055] The subsequent steps are all based on the registered SAR images.

[0056] Step 2: construct a small baseline set based on multiple SAR images, and perform phase unwrapping on multiple SAR images in the small baseline set after differential interference to obtain multiple phase unwrapping images, specifically including:

[0057] SAR images that meet a preset time baseline threshold and a preset space baseline threshold are obtained and a small baseline set is constructed; for example, SAR images that are smaller than a preset time baseline threshold and a preset space baseline threshold can be obtained and a small baseline set can be constructed.

[0058] Differential interference, multi-viewing, DEM phase removal, adaptive filtering, phase unwrapping, etc. are performed on the multiple SAR images in the small baseline set to obtain multiple phase unwrapping maps, which include redundant unwrapping phases.

[0059] Step 3: determine whether the unwrapped phases of the multiple phase unwrapping diagrams meet the preset conditions. If not, update the corresponding phase unwrapping diagrams, specifically:

[0060] Step 31: Determine multiple image groups whose baselines meet preset requirements from multiple SAR images in the small baseline set, each image group includes two SAR images.

[0061] The preset requirement in the embodiment of the present invention may be the shortest time baseline, or a time baseline that is less than a preset baseline threshold, preferably the shortest time baseline. The shortest time baseline is usually 12 days.

[0062] Step 32: determine whether the unwrapped phases of the phase unwrapping graphs corresponding to the two SAR images in each group meet a preset condition; if not, update the corresponding phase unwrapping graphs.

[0063] In the embodiment of the present invention, it is determined whether the unwrapped phases of the phase unwrapped graphs corresponding to the two SAR images in each group meet the preset conditions, specifically:

[0064] It is determined whether the unwrapped phase of the phase unwrapping graph corresponding to the two SAR images in each group is a non-null value. The null value in the embodiment of the present invention indicates that the coherence of the point is poor and the unwrapped phase is unreliable. Therefore, before the phase unwrapping step, the pixel is masked and the phase unwrapping is not performed, so the phase is null.

[0065] Furthermore, the method for updating the corresponding phase unwrapping diagram in the embodiment of the present invention is:

[0066] (a) Taking the middle monitoring time of the two SAR images corresponding to the null value as the center, obtain the phase unwrapping maps corresponding to multiple SAR images within the set time length from the center. Specifically: taking the middle monitoring time of the two SAR images corresponding to the null value as the center, open a window with each set time length before and after in time, for example, 30 days, 50 days, 60 days, etc., and then obtain the phase unwrapping maps corresponding to multiple SAR images within each set time length window before and after.

[0067] (b) Determine the average phase change rate based on the phase unwrapping diagrams corresponding to multiple SAR images within a set time distance from the center.

[0068] In a specific embodiment, the average phase change rate is determined by using an interference pattern stacking technique to obtain the average phase change rate:

[0069]

[0070] In formula (1), V is the average phase change rate, ph l is the unwrapped phase in the lth phase unwrapping diagram, ΔT lis the monitoring time interval between the two SAR images corresponding to the lth phase unwrapping image, and N is the total number of phase unwrapping images corresponding to multiple SAR images within a set time length from the center.

[0071] (c) According to the average phase change rate, the unwrapped phase corresponding to the null value is completed, and the corresponding phase unwrapping diagram is updated, specifically:

[0072] The average phase change rate is multiplied by the monitoring time interval of the two SAR images corresponding to the null value, and the result of the multiplication is used to complete the unwrapped phase corresponding to the null value.

[0073] All phase unwrapping graphs are traversed until all the shortest time unwrapping phases are completed to obtain the updated phase unwrapping graph.

[0074] Step 4: Determine the deformation sequence of the area to be monitored according to the updated phase unwrapping map.

[0075] Quasi-accuracy detection (QUAD) is a traditional geodetic data processing method that effectively identifies and corrects gross errors by establishing an error estimation model. QUAD can calculate the true error of the unwrapped phase by selecting the quasi-accuracy interferometric phase, and then determine the phase containing the unwrapped gross error based on the phenomenon of true error stratification and bunching. Finally, the estimated unwrapping error is constrained to an integer multiple of 2π, and then the phase containing the unwrapping error is corrected and the empty phase is restored. However, the selection of the quasi-accuracy phase of QUAD starts from m+2 (m is the number of SAR images) and iterates until the position of the true error stratification is equal to the number of quasi-accuracy phases, which has a high time complexity. And when the value of a certain unwrapping error is large, the position of the true error stratification will be determined incorrectly, resulting in the unstable phase being mistakenly selected as the quasi-stable phase, reducing the correction effect of the unwrapping error and even generating additional unwrapping errors.

[0076] To further improve the deformation monitoring accuracy, in another embodiment, after step 3, the method further includes:

[0077] (A) Multiple phase closed loops are constructed based on multiple SAR images in a small baseline set to determine the phase with unwrapping errors. Specifically:

[0078] A phase closure loop is constructed using three of the multiple SAR images in the small baseline set, and multiple phase closure loops are obtained.

[0079] Exemplarily, it is assumed that a SAR image is acquired at each of three time points i, j, and k, and the three SAR images form a phase closed loop in time. is the unwrapped phase between the SAR image acquired at the i-th monitoring time and the SAR image acquired at the j-th monitoring time, is the unwrapped phase between the SAR image acquired at the jth monitoring time and the SAR image acquired at the kth monitoring time, is the unwrapped phase between the SAR image acquired at the kth monitoring time and the SAR image acquired at the ith monitoring time. Each unwrapped phase is composed of the same multiple components. Take as an example to explain its components. Depend on Equal parts. Indicates the deformation of the surface in the line of sight; represents the residual orbit error introduced by the inaccurate sensor position; is the external DEM error used for terrain correction; is the unwrapping error on the phase unwrapping diagram; is the atmospheric error on the phase unwrapped diagram; Indicates noise.

[0080] The residual of each phase closed loop is obtained, and combined with the preset residual threshold, the phase with unwrapping error is determined from the completed phase unwrapping graph.

[0081] In the embodiment of the present invention, the residual of the phase closed loop is as shown in formula (2):

[0082]

[0083] In formula (2), is the residual of the phase closure loop constructed by the SAR images acquired at the i, j, and k monitoring times, is the deformation phase component in the closed loop residual, is the atmospheric phase residual, is the residual orbit error, is the DEM phase component, is the unwrapping error component, is random noise, In fact, in the closed loop residual part, the atmospheric phase residual component and the deformation component are always zero, the DEM phase component and the noise component show randomness, the residual orbit error component shows a trend term distribution, and the contribution to the closed loop residual is usually less than 1 radian. The phase unwrapping error component is spatially expressed as a jump of 2π integer cycles. Therefore, π is set as the residual threshold of the phase closed loop, and the phase closed loop corresponding to the residual greater than the threshold is considered to have an unwrapping error. Then, the phase with an unwrapping error is determined by the intersection of multiple phase closed loops, and the remaining phases are considered to have no unwrapping error and are used as the initial quasi-stable phases.

[0084] Then determine whether the initial quasi-stable phase is a null value. If it is null, it is regarded as a non-quasi-stable phase, and the rest are regarded as the final quasi-stable phase.

[0085] (B) The unwrapping error is removed from the corresponding phase to obtain the phase unwrapping image after removing the unwrapping error.

[0086] Since the unwrapping error is usually large in value and non-randomly distributed, the unwrapping error is treated as a gross error and then the estimate of the gross error is determined;

[0087] Assuming that it is finally determined that there are b gross errors in the n unwrapped phases, we can obtain b n-dimensional unit vectors e j =(0,…,0,1,0,…,0) T , corresponding to the jth observed phase has a gross error, that is, the jth component is 1, and the rest are 0, C b =(e1,…,e b ), then the observation equation containing gross errors can be expressed by the following model:

[0088]

[0089] In formula (3), A is an n×m-dimensional coefficient matrix with rank m; is the estimate of the m-dimensional unknown parameter; is the gross error estimate; L is the n-dimensional observation phase vector; V is the observation residual. Using the least squares method, the gross error estimate can be obtained from formula (3):

[0090]

[0091] In formula (4), is the estimate of the gross error, C b is a matrix composed of multiple phase unwrapping graphs with unwrapping errors, C b The transposed matrix of , P is the observation weight matrix, which is the unit matrix here, R is the adjustment factor, and L is the n-dimensional observation phase vector.

[0092] The unwrapping error will only appear in multiples of 2π. The gross error estimate obtained by equation (4) is constrained to be a multiple of 2π, and the constrained gross error estimate is removed from the corresponding phase.

[0093] Accordingly, step 4 is specifically as follows:

[0094] According to the multiple phase unwrapping images after removing the unwrapping errors, the deformation sequence of the area to be monitored is determined, specifically, the cumulative deformation field of the area to be monitored is determined by using rank-deficient free network adjustment, and the deformation sequence is extracted therefrom:

[0095] Determine whether the unwrapped phase after restoration and correction in the above steps is empty. If it is empty, ignore the observation value, that is, ignore the empty unwrapped phase. The remaining unwrapped phases are calculated by formula (5) to obtain the surface time deformation series.

[0096]

[0097] In the formula, is the deformation sequence, A + is the pseudo-inverse of the coefficient matrix, L is the processed unwrapped phase that is not empty, is the date surface deformation obtained by SAR image, unw1 is the phase value of the phase unwrapping map, m is the number of SAR images, and s is the number of unwrapped phases that are not empty.

[0098] A second aspect of the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0099] A third aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0100] In order to verify the effectiveness of the method and terminal device of the present invention, the method of the present invention will be described in detail below with specific simulation experiments and examples.

[0101] Simulation data comparison experiment

[0102] First, the surface deformation time series of a single point is simulated, which consists of trend deformation and linear deformation (ts = (-2t + cos (10t) * t) / 4; t∈ (0, 100]), with a total of 100 time nodes, where ts i represents the cumulative deformation at the i-th time point. The four time points are used as the time baseline threshold to generate a small baseline set, and the cumulative deformation is subtracted to obtain 390 unwrapped phases, ph i,i+k Represents the unwrapped phase between the i+kth time point (k≤4) and the ith time point. Add random noise, gross error (2π integer times) and null value to the unwrapped phase, and then perform direct adjustment, quasi-calibration recovery and correction of the unwrapped phase and post-adjustment.

[0103] Figure 2It is a comparison chart of simulated data between the method of the present invention and the existing method. Wherein Ts1 is a simulated single-point time deformation sequence, which consists of two parts: a linear term and a trend term. A redundant unwrapping phase is generated for the simulated time series according to the SBAS strategy, and a phase unwrapping error (including null value) is randomly added. Ts2 is a time deformation sequence obtained by redundant unwrapping phase adjustment without phase recovery and correction; Ts3 is a time deformation sequence obtained by redundant unwrapping phase adjustment corrected by quasi-calibration; Ts4 is a time deformation sequence obtained by redundant unwrapping phase adjustment restored and corrected by the method of the present invention. From Figure 2 It can be found that the time deformation sequence obtained by the method proposed in the present invention is basically consistent with the simulated time deformation sequence, and the obtained timing curve has smaller jitter and higher accuracy.

[0104] Example

[0105] This example selects the Mianshawan landslide located in the Jinsha River Basin of my country. The Baihetan Hydropower Station is the second cascade hydropower station developed in the lower reaches of the Jinsha River. On April 22, 2021, the Baihetan Hydropower Station began to store water, and the water level rose from 660.35 meters at the beginning to 916.51 meters. The rapid rise in the water level in the reservoir area destroyed the stability of the Mianshawan slope and induced the occurrence of the landslide.

[0106] The SAR data used in the experiment are 94 Sentinel-1 data covering the Mianshawan landslide in the Jinsha River Basin from January 4, 2019 to March 25, 2022. Since the landslide surface is covered with dense vegetation, severe signal decoherence leads to null values ​​and errors in the unwrapped phase of the SAR image. The improved SBAS-InSAR phase recovery and correction technology of the present invention is used to perform high-precision time series solution on the landslide.

[0107] First, the SAR image acquired on October 1, 2020 was selected as the master image, and the remaining images were selected as slave images, and all the slave images were accurately registered to the master image. Secondly, the interference pairs were combined by setting the temporal and spatial baseline threshold based on the accurately registered SAR images, and then the obtained interference pairs were subjected to phase interferometry, multi-viewing, DEM phase removal, adaptive filtering, phase unwrapping and other processes to obtain the phase unwrapping map. After the redundant unwrapped phase was recovered and corrected, the high-precision time series of the landslide surface was inverted using the rank-deficient free network adjustment.

[0108] Figure 3 The figure shows the time-space baseline distribution diagram of the interference pairs selected by the present invention for landslide surface inversion. The time baseline is set to 40 days, the space baseline is set to 150 meters, and 94 SAR images constitute 262 interference pairs.

[0109] Figure 4The figure shows the surface cumulative deformation map of the Mianshawan landslide part of the Sentinel-1SAR image calculated by the method proposed in this invention compared with the starting date (January 4, 2019). The traditional and existing SBAS-InSAR methods cannot obtain a complete deformation field due to the influence of unwrapping errors and null values. Figure 4 It can be seen that the method proposed in the present invention accurately and completely obtains the cumulative deformation field of the Mianshawan landslide, which was severely affected by decoherence from January 4, 2019 to March 25, 2022, and its maximum cumulative deformation reaches 25 cm.

[0110] Figure 5 The figure shows the surface deformation time series of the Mianshawan landslide in Sentinel-1SAR images from January 4, 2019 to March 25, 2022 calculated by the method proposed in the present invention. The method proposed in the present invention uses Sentinel-1SAR images to accurately invert the temporal evolution characteristics of the surface deformation of the Mianshawan landslide, and successfully captures the accelerated process of the surface deformation of the landslide affected by the water storage in the reservoir area, which improves the accuracy of surface deformation inversion compared with traditional methods.

[0111] The surface deformation monitoring method and terminal device of the present invention can make full use of the redundant phase unwrapping map generated by the multi-scene SAR image through the SBAS (Small Baseline Subset) strategy, automatically identify and correct the unwrapping phase with unwrapping errors and null values, provide accurate surface deformation time series and complete surface deformation field, and overcome the influence of time and space incoherence on SBAS-InSAR monitoring results, especially the influence of long-term incoherence causing the disconnection of the adjustment network. After restoring and correcting the unwrapping phase, the slope deformation of landslides, ground subsidence, volcanoes, roadbed deformation, etc. can be obtained according to the heading angle, incident angle and terrain factors of the satellite, and the deformation characteristics can be accurately obtained. An improved method is provided for high-precision monitoring of landslides, ground subsidence, volcanoes, roadbed deformation, etc., and this work is of great significance for landslide monitoring.

[0112] The above are only a few embodiments of the present application and do not constitute any form of limitation to the present application. Although the present application is disclosed as above with preferred embodiments, it is not intended to limit the present application. Any technician familiar with the profession, without departing from the scope of the technical solution of the present application, using the technical content disclosed above to make slight changes or modifications are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A method for monitoring ground deformation, characterized in that: include: Acquire SAR images of the area to be monitored at different monitoring times to obtain multiple SAR images; A small baseline set is constructed according to the multiple SAR images, and phase unwrapping is performed on the multiple SAR images in the small baseline set after differential interference to obtain multiple phase unwrapping images; Determine whether the unwrapped phases of the multiple phase unwrapping graphs meet a preset condition, and if not, update the corresponding phase unwrapping graphs; Determining the deformation sequence of the area to be monitored according to the updated phase unwrapping map; Determining whether the unwrapped phases of the multiple phase unwrapping graphs meet a preset condition specifically includes: Determining, from the plurality of SAR images in the small baseline set, a plurality of image groups whose baselines meet preset requirements, each of the image groups including two SAR images; Determine whether the unwrapped phase of the phase unwrapped graph corresponding to the two SAR images in each group is a non-null value; Update the corresponding phase unwrapping diagram, including: Taking the middle monitoring time of the two SAR images as the center, obtaining phase unwrapping images corresponding to a plurality of SAR images within a set time distance from the center; Determining an average phase change rate according to phase unwrapping graphs corresponding to a plurality of SAR images within a set time distance from the center; Using the average phase change rate, the unwrapped phase corresponding to the null value is completed, and the corresponding phase unwrapping graph is updated; The average phase change rate is determined according to a first formula, which is: ， In the formula, is the average phase change rate, For the The unwrapped phase in the amplitude-phase unwrapping diagram, For the The monitoring time interval of the two SAR images corresponding to the phase unwrapping map, It is the total number of phase unwrapping images corresponding to the multiple SAR images within a set time distance from the center.

2. The method for monitoring ground deformation according to claim 1, characterized in that: The method of using the average phase change rate to complete the unwrapped phase corresponding to the null value specifically includes: multiplying the average phase change rate and the monitoring time interval of the two SAR images; The unwrapped phase corresponding to the null value is completed using the result of the multiplication.

3. The method for monitoring ground deformation according to claim 1 or 2, characterized in that: After updating the corresponding phase unwrapping diagram, it also includes: constructing multiple phase closed loops based on the multiple SAR images in the small baseline set to determine the phases with unwrapping errors; removing the unwrapping error from the corresponding phase; Correspondingly, according to the updated phase unwrapping diagram, the deformation sequence of the area to be monitored is determined, specifically: The deformation sequence of the area to be monitored is determined according to the multiple phase unwrapping images after the unwrapping errors are removed.

4. The method for monitoring ground deformation according to claim 3, characterized in that: Constructing multiple phase closed loops based on the multiple SAR images in the small baseline set to determine the phase with unwrapping error specifically includes: constructing a phase closed loop using three of the multiple SAR images in the small baseline set to obtain multiple phase closed loops; The residual of each phase closed loop is obtained, and combined with the preset residual threshold, the phase with unwrapping error in the corresponding phase unwrapping graph is determined.

5. The method for monitoring ground deformation according to claim 3, characterized in that: Removing the unwrapping error from the corresponding phase specifically includes: Considering the unwrapping error as a gross error, and determining an estimate of the gross error; constraining the estimation of the gross error to a preset integer multiple; The constrained gross error estimates are removed from the corresponding phases.

6. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images