InSAR earth surface deformation monitoring method based on TGS image segmentation

Abstract

The invention discloses an InSAR earth surface deformation monitoring method based on TGS image segmentation, and the method comprises the following steps: S01, selecting a main image according to preset collection elements in different time periods, and setting an associated auxiliary image of a baseline threshold value; s02, a research area of the associated auxiliary image is determined and cut to construct an SBAS, a correlation coefficient corresponding to the cut associated auxiliary image is determined through calculation and differential interference, and SDPF points are extracted; s03, performing flat-ground phase removal, terrain phase removal and filtering processing on the interferometric phase diagram generated by the SBAS by using DEM data of a corresponding region; s04, unwrapping the processed differential interferometric phase diagram based on an InSAR picture differential interferometric phase unwrapping algorithm of TGS image segmentation; and S05, constructing an earth surface deformation monitoring model in a large-scale complex geographical environment, and estimating a linear deformation phase and a residual terrain phase based on the SDPF points. According to the invention, large-scale complex geographical environment surface deformation data precision acquisition is realized.

Description

Technical Field

[0001] The invention relates to the technical field of geographic monitoring, and in particular to an InSAR surface deformation monitoring method based on TGS image segmentation. Background Art

[0002] Among the means of monitoring surface deformation, traditional geodetic technologies, including GPS, Beidou, etc., are easily limited by low spatial resolution and high cost during the detection process. For example, MT-InSAR technology uses permanent scattering radar interference synthetic aperture radar (PS-InSAR), and selects time-stable points such as buildings and exposed rocks. These points have high stability and reflectivity in multiple SAR images. However, when using PS-InSAR in the existing technology, it is often not used in conjunction with the long-term time series prediction and processing analysis framework, and it cannot achieve the prediction of surface settlement of dense objects, and it does not consider the environmental factors that affect surface deformation. Therefore, it lacks a precision detection of surface deformation in large-scale complex geographical environments. Summary of the invention

[0003] The purpose of the present invention is to provide an InSAR surface deformation monitoring method based on TGS image segmentation to solve the above problems.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] An InSAR surface deformation monitoring method based on TGS image segmentation comprises the following steps:

[0006] S01, selecting a main image in different time periods according to preset acquisition elements, and setting associated auxiliary images of baseline thresholds;

[0007] S02, determining the research area of ​​the associated auxiliary image and clipping it to construct SBAS, and then determining the coherence coefficient corresponding to the clipped associated auxiliary image by calculating and differential interferometry, and extracting the SDPF point;

[0008] S03, using the DEM data of the corresponding area, removing the flat ground phase, removing the terrain phase and filtering the interferometric phase image generated by the SBAS;

[0009] S04, InSAR image differential interferometry phase unwrapping algorithm based on TGS image segmentation is used to unwrap the processed differential interferometry phase image;

[0010] S05. Construct a surface deformation monitoring model under large-scale complex geographical environment, and estimate the linear deformation phase and residual terrain phase based on SDPF points;

[0011] S06, removing the residual terrain phase and the linear deformation phase, and performing secondary unwrapping on the remaining phase;

[0012] S07. Calculate the surface shape variables based on the singular value decomposition method;

[0013] S08. Use filtering methods / based on accurate external data to remove non-surface deformation error parameters of atmospheric interference and orbit errors to generate a surface deformation time series.

[0014] S09. Use the leveling point monitoring data at the corresponding location to verify and correct the monitoring results based on the InSAR data.

[0015] Preferably, during the execution of step S04, a TGS of the InSAR image and the differential interferometry phase map needs to be established, and the steps include:

[0016] S41. In the InSAR image and the differential interferometric phase map, a non-overlapping sub-region of n×n pixels is used as the original object to determine a first-order subset object and a second-order subset object, where:

[0017] The first-order subset objects include a complete InSAR image of the target area collected at a certain moment and a complete differential interferometry phase map;

[0018] The second-order subset object represents a set of InSAR images of the same target area acquired within a period of time and a set of differential interferometric phase maps obtained after interferometric processing of the InSAR images;

[0019] S42, InSAR image correlation coefficient c is proposed to reflect the coherence ij , vegetation coverage fvc ij , terrain slope sl ij 、Slope direction ij , signal-to-noise ratio SNR ij Equivalent characteristic attributes and center position p ij As the connotation element of InSAR image compatible particles, the local correlation coefficient c i ' j , registration error e ij , mutual information m ij , center position p i ' j Equal attributes as the connotation elements of compatible particles in differential interference phase diagram;

[0020] S43, based on the feature vector X ij =[c ij ,fvc ij ,sl ij ,as ij ,snr ij ,p ij ,…] and X i 'j =[c i ' j ,e ij ,m ij ,p i ' j ,…] similarity measure defines the distance measurement function and the adaptive compatible proposition radius to establish compatible relations.

[0021] Preferably, the step S04 further comprises designing a set of segmentation rules for large-scale complex geographical environment InSAR images and differential interferometry phase images under TGS during the execution process, and the steps include:

[0022] S44. Establish a set of similarity measurement functions as the basis for designing image compatible particle merging rules:

[0023]

[0024]

[0025] Among them, SIM ij,mn , SIM i ' j,mn are the similarity measure vectors between the elements with coordinate positions (i, j) and (m, n) in the InSAR image and the differential interferometric phase map TGS, respectively. DIS and DIS′ are the distance function sets for specific features.

[0026] S45. Based on the similarity measure, the image compatible particle merging rule set is:

[0027]

[0028] Wherein, the subscripts u and v are the numbers of compatible particles in the row and column directions of the image, respectively, and D and D′ are the threshold sets of the distance measurement of compatible image particles of InSAR image and differential interferometry phase image, respectively.

[0029] Preferably, the step S04 performs unwrapping using a differential interferometer phase unwrapping algorithm, including:

[0030] S46, it is proposed to use a method combining a transform domain and mutual information to perform sub-pixel level registration of sub-images, wherein the transform domain includes FFT and DFT;

[0031] S47, after completing the sub-image registration, a global optimization algorithm is used to merge the sub-images to reduce the phase error introduced by the merging.

[0032] Preferably, in the phase error processing performed in step S47, in order to prevent phase jumps between adjacent Burst interferograms in the InSAR single-view complex image using the interferometric broadband mode, an improved enhanced spectral diversity method is used for fine alignment based on sub-pixel alignment, so that the image alignment accuracy in both range and azimuth is not less than 0.001 pixel.

[0033] Preferably, in the process of segmenting the InSAR image in step S04, the low-quality sub-images are denoised and unwrapped based on the neighborhood pixel values ​​to restore the noise pixel information, and then the boundary areas of the unwrapped phase sub-images are spliced ​​using a global optimization algorithm to obtain the final true phase.

[0034] Preferably, the processing method for restoring the noise pixel information from the neighborhood pixel values ​​includes Kalman filtering and decision tree prediction.

[0035] In the above technical scheme, the present invention provides an InSAR surface deformation monitoring method based on TGS image segmentation, which has the following beneficial effects: using compatible granular merging to design large-scale complex geographical environment image segmentation rules in compatible granularity space (TGS), improving InSAR image precise registration and differential interferometry phase precise unwrapping in InSAR large-scale complex geographical environment surface deformation monitoring, thereby obtaining accurate large-scale complex geographical environment surface deformation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0037] Figure 1 A schematic diagram of a large-scale complex geographical environment InSAR image registration technology route provided by an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of a large-scale complex geographical environment differential interferometry phase unwrapping technology route provided by an embodiment of the present invention;

[0039] Figure 3 A flowchart of an embodiment of the present invention is provided. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] like Figure 1-3 As shown, an InSAR surface deformation monitoring method based on TGS image segmentation includes:

[0042] Embodiment 1

[0043] based on Figure 3 It can be seen that the method includes the following steps:

[0044] S01, selecting a main image in different time periods according to preset acquisition elements, and setting associated auxiliary images of baseline thresholds;

[0045] S02, determining the research area of ​​the associated auxiliary image and clipping it to construct the SBAS, and then determining the coherence coefficient corresponding to the clipped associated auxiliary image by calculating and differential interferometry, and extracting the SDPF point;

[0046] S03, using the DEM data of the corresponding area, removing the flat ground phase, removing the terrain phase and filtering the interferometric phase image generated by the SBAS;

[0047] S04, InSAR image differential interferometry phase unwrapping algorithm based on TGS image segmentation is used to unwrap the processed differential interferometry phase image;

[0048] S05. Construct a surface deformation monitoring model under large-scale complex geographical environment, and estimate the linear deformation phase and residual terrain phase based on SDPF points;

[0049] S06, removing the residual terrain phase and the linear deformation phase, and performing secondary unwrapping on the remaining phase;

[0050] S07. Calculate the surface shape variables based on the singular value decomposition method;

[0051] S08. Use filtering methods / based on accurate external data to remove non-surface deformation error parameters of atmospheric interference and orbit errors to generate a surface deformation time series.

[0052] S09. Use the leveling point monitoring data at the corresponding location to verify and correct the monitoring results based on the InSAR data.

[0053] In the above technology, when performing sub-pixel registration and phase unwrapping on sub-images, a multi-threaded parallel processing method is proposed. Based on the above image registration and phase unwrapping algorithms, the SBAS-InSAR technology architecture is proposed to extract large-scale complex geographical environment surface deformation information to finally calculate the LOS direction surface deformation of each pixel point.

[0054] Embodiment 2

[0055] Combination Figure 1-2 As shown, during the execution of step S04, a TGS of the InSAR image and the differential interferometric phase map needs to be established, and the steps include:

[0056] S41. In the InSAR image and the differential interferometric phase map, a non-overlapping sub-region of n×n pixels is used as the original object to determine a first-order subset object and a second-order subset object, where:

[0057] The first-order subset objects include a complete InSAR image of the target area collected at a certain moment and a complete differential interferometric phase map;

[0058] The second-order subset object represents the InSAR image set of the same target area acquired within a period of time and the differential interferometric phase map set obtained after the interferometric processing of the InSAR image pair;

[0059] S42, InSAR image correlation coefficient c is proposed to reflect the coherence ij , vegetation coverage fvc ij , terrain slope sl ij 、Slope direction ij , signal-to-noise ratio SNR ij Equivalent characteristic attributes and center position p ij As the connotation element of InSAR image compatible particles, the local correlation coefficient c i ' j , registration error e ij , mutual information m ij , center position p i ' j Equal attributes as the connotation elements of compatible particles in differential interference phase diagram;

[0060] S43, based on the feature vector X ij =[c ij ,fvc ij ,sl ij ,as ij ,snr ij ,p ij ,…] and X i ' j =[c i ' j ,eij ,m ij ,p i ' j ,…] similarity measure defines the distance measurement function and the adaptive compatible proposition radius to establish compatible relations.

[0061] Furthermore, step S04 also includes designing a set of segmentation rules for large-scale complex geographical environment InSAR images and differential interferometry phase images under TGS during the execution process, and the steps include:

[0062] S44. Establish a set of similarity measurement functions as the basis for designing image compatible particle merging rules:

[0063]

[0064] Among them, SIM ij,mn , SIM i ' j,mn are the similarity measure vectors between the elements with coordinate positions (i, j) and (m, n) in the InSAR image and the differential interferometric phase map TGS, respectively. DIS and DIS′ are the distance function sets for specific features.

[0065] S45. Based on the similarity measure, the image compatible particle merging rule set is:

[0066]

[0067] Wherein, the subscripts u and v are the numbers of compatible particles in the row and column directions of the image, respectively, and D and D′ are the threshold sets of the distance measurement of compatible image particles of InSAR image and differential interferometry phase image, respectively.

[0068] Specifically, after obtaining the similarity measurement value vector set, the optimal threshold set D * and D′ * The selection of is the key to whether the image compatible particle merging and image segmentation rules are reasonable. This project intends to use the coherence difference of adjacent image regions as the basis for evaluating the segmentation effect. It is believed that the greater the coherence difference, the better the segmentation effect. Based on the multidimensional characteristics of image compatible particles and this evaluation basis, it is intended to use multidimensional space optimization algorithms such as conjugate gradient to search for the optimal threshold set D * and D′ *

[0069] Secondly, step S04 executes a differential interferometry phase unwrapping algorithm for unwrapping, including:

[0070] S46, it is proposed to use a method combining transform domain and mutual information to perform sub-pixel level registration of sub-images, wherein the transform domain includes FFT and DFT;

[0071] S47, after completing the sub-image registration, a global optimization algorithm is used to merge the sub-images to reduce the phase error introduced by the merging.

[0072] Furthermore, in the phase error processing performed in step S47, in order to prevent phase jumps between adjacent Burst interferograms in the InSAR single-view complex image using the broadband mode of interferometric measurement, an improved enhanced spectral diversity method is used for fine alignment based on sub-pixel alignment, so that the image alignment accuracy in both range and azimuth is not less than 0.001 pixels.

[0073] Furthermore, in the process of segmenting the InSAR image in step S04, the noise pixel information is restored based on the neighborhood pixel values ​​to perform denoising and unwrapping processing on the low-quality sub-image, and then a global optimization algorithm is used to splice the boundary area of ​​the unwrapped phase sub-image to obtain the final true phase.

[0074] It should be noted that the processing methods for restoring the noise pixel information from the neighborhood pixel values ​​include Kalman filtering and decision tree prediction.

[0075] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0079] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0080] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps of the method in the above embodiments, and the electronic device specifically includes the following contents:

[0081] Processor, memory, communications interface and bus;

[0082] Wherein, the processor, memory, and communication interface communicate with each other via the bus;

[0083] The processor is used to call the computer program in the memory, and when the processor executes the computer program, all the steps in the method in the above embodiment are implemented.

[0084] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps of the method in the above embodiments are implemented.

[0085] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Although the embodiment of this specification provides the method operation steps described in the embodiment or flow chart, more or less operation steps can be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way of executing the order of many steps, and does not represent the only execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same one or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The present invention is described with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the process and / or box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment. In the description of this specification, the description of the reference term "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification.

[0087] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradicting each other. The above is only an embodiment of the embodiment of this specification and is not intended to limit the embodiment of this specification. For those skilled in the art, the embodiment of this specification may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiment of this specification shall be included in the scope of the claims of the embodiment of this specification.

Claims

1. An InSAR surface deformation monitoring method based on TGS image segmentation, characterized in that: The following steps are involved: S01, selecting a main image in different time periods according to preset acquisition elements, and setting associated auxiliary images of baseline thresholds; S02, determining the research area of ​​the associated auxiliary image and clipping it to construct SBAS, and then determining the coherence coefficient corresponding to the clipped associated auxiliary image by calculating and differential interferometry, and extracting SDPF points; S03, using the DEM data of the corresponding area, removing the flat ground phase, removing the terrain phase and filtering the interferometric phase image generated by the SBAS; S04, InSAR image differential interferometry phase unwrapping algorithm based on TGS image segmentation is used to unwrap the processed differential interferometry phase image; S05. Construct a surface deformation monitoring model under large-scale complex geographical environment, and estimate the linear deformation phase and residual terrain phase based on SDPF points; S06, removing the residual terrain phase and the linear deformation phase, and performing secondary unwrapping on the remaining phase; S07. Calculate the surface shape variables based on the singular value decomposition method; S08. Use filtering methods / based on precise external data to remove non-surface deformation error parameters caused by atmospheric interference and orbital errors to generate a surface deformation time series. S09. Use the leveling point monitoring data at the corresponding location to verify and correct the monitoring results based on the InSAR data.

2. The InSAR surface deformation monitoring method based on TGS image segmentation according to claim 1 is characterized in that: During the execution of step S04, a TGS of the InSAR image and the differential interferometric phase map needs to be established, and the steps include: S41. In the InSAR image and the differential interferometric phase map, a non-overlapping sub-region of n×n pixels is used as the original object to determine a first-order subset object and a second-order subset object, where: The first-order subset objects include a complete InSAR image of the target area collected at a certain moment and a complete differential interferometry phase map; The second-order subset object represents a set of InSAR images of the same target area acquired within a period of time and a set of differential interferometric phase maps obtained after interferometric processing of the InSAR images; S42, InSAR image correlation coefficient c is proposed to reflect the coherence ij , vegetation coverage fvc ij , terrain slope sl ij 、Slope direction ij , signal-to-noise ratio SNR ij Equivalent characteristic attributes and center position p ij As the connotation element of InSAR image compatible particles, the local correlation coefficient c i ' j , registration error e ij , mutual information m ij , center position p i ' j Equal attributes as the connotation elements of compatible particles in differential interference phase diagram; S43, based on the feature vector X ij =[c ij ,fvc ij ,sl ij ,as ij ,snr ij ,p ij ,…] and X i ' j =[c i ' j ,e ij ,m ij ,p i ' j ,…] similarity measure defines the distance measurement function and the adaptive compatible proposition radius to establish compatible relations.

3. The InSAR surface deformation monitoring method based on TGS image segmentation according to claim 1 is characterized in that: The step S04 also includes designing a set of segmentation rules for large-scale complex geographical environment InSAR images and differential interferometry phase images under TGS during the execution process, and the steps include: S44. Establish a set of similarity measurement functions as the basis for designing image compatible particle merging rules: Among them, SIM ij,mn , SIM i ' j,mn are the similarity measure vectors between the elements with coordinate positions (i, j) and (m, n) in the InSAR image and the differential interferometric phase map TGS, respectively. DIS and DIS′ are the distance function sets for specific features. S45. Based on the similarity measure, the image compatible particle merging rule set is: Wherein, the subscripts u and v are the numbers of compatible particles in the row and column directions of the image, respectively, and D and D′ are the threshold sets of the distance measurement of compatible image particles of InSAR image and differential interferometry phase image, respectively.

4. The InSAR surface deformation monitoring method based on TGS image segmentation according to claim 1 is characterized in that: The step S04 executes a differential interferometer phase unwrapping algorithm to perform unwrapping, including: S46, it is proposed to use a method combining a transform domain and mutual information to perform sub-pixel level registration of sub-images, wherein the transform domain includes FFT and DFT; S47, after completing the sub-image registration, a global optimization algorithm is used to merge the sub-images to reduce the phase error introduced by the merging.

5. The InSAR surface deformation monitoring method based on TGS image segmentation according to claim 4 is characterized in that: In the phase error processing performed in step S47, in order to prevent phase jumps between adjacent Burst interferograms in the InSAR single-view complex image using the interferometric broadband mode, an improved enhanced spectral diversity method is used to perform fine alignment on the basis of sub-pixel alignment, so that the image alignment accuracy in both range and azimuth is not less than 0.001 pixel.

6. The InSAR surface deformation monitoring method based on TGS image segmentation according to claim 1 is characterized in that: In the process of segmenting the InSAR image in step S04, the low-quality sub-images are denoised and unwrapped based on the neighborhood pixel values ​​to restore the noise pixel information, and then the global optimization algorithm is used to splice the boundary areas of the unwrapped phase sub-images to obtain the final real phase.

7. The InSAR surface deformation monitoring method based on TGS image segmentation according to claim 6 is characterized in that: The processing method for restoring the noise pixel information from the neighborhood pixel value includes Kalman filtering and decision tree prediction.

8. An electronic 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 program, the steps of the InSAR surface deformation monitoring method based on TGS image segmentation according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the InSAR surface deformation monitoring method based on TGS image segmentation described in any one of claims 1 to 7 are implemented.

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