A permafrost area deformation monitoring method based on space-time constraints and related equipment

Abstract

The application is suitable for the technical field of frozen soil monitoring, and provides a frozen soil area deformation monitoring method based on space-time constraints and related equipment. The monitoring method comprises: processing SAR image data of a target frozen soil area to obtain an interferogram; modeling target pixels in the interferogram based on interannual variation of frozen soil deformation to obtain a deformation model, and processing the interferogram through the deformation model to obtain all phase values of the target pixels; taking the target pixels and other target pixels in a window centered on the target pixels as a pixel coherent group and establishing an observation equation, determining the weight of each phase value of the pixel coherent group based on spatial correlation; then solving the time sequence deformation result of the target pixels by using the weight; and integrating the time sequence deformation results of all the target pixels to obtain the deformation result of the target frozen soil area. The monitoring method can effectively improve the precision of frozen soil area deformation monitoring.

Description

Technical Field

[0001] This application relates to the field of permafrost monitoring technology, and in particular to a method and related equipment for monitoring deformation in permafrost regions based on spatiotemporal constraints. Background Technology

[0002] Affected by global warming and human activities, permafrost is undergoing a dramatic and rapid degradation trend. Changes in surface deformation in permafrost regions are the most direct and external manifestation of this degradation trend; therefore, monitoring surface deformation in permafrost regions is beneficial for deepening our understanding of the freeze-thaw cycle and degradation processes of permafrost. Traditional permafrost surface deformation monitoring typically relies on field measurements; while this yields high-quality monitoring results, it is time-consuming, labor-intensive, and limited to single-point or small-scale operations. Synthetic Aperture Radar Interferometry (InSAR), as an emerging space-based Earth observation technology, offers advantages such as all-weather, all-time observation, high accuracy, and wide coverage, and has been widely applied to large-scale surface deformation monitoring in permafrost regions.

[0003] In InSAR deformation monitoring, a reasonable phase deformation model is crucial for removing redundant phases and extracting high-precision surface deformation. However, the deformation process of permafrost surfaces is complex due to the freeze-thaw cycle. Currently, scholars both domestically and internationally have developed six models for monitoring surface deformation in permafrost regions: the cubic square deformation model, the periodic deformation model, the piecewise Stefan permafrost deformation model, the permafrost deformation model considering environmental factors, the deformation model based on the permafrost thawing process, and the piecewise elevation deformation model. Most of these models divide permafrost deformation into long-term and seasonal components, assuming that these two components remain constant throughout the study period. This ignores the interannual variations in long-term and seasonal permafrost deformation, which does not reflect objective reality and affects the accuracy of long-term InSAR deformation monitoring. Furthermore, existing InSAR studies on permafrost surface deformation monitoring are based on independent pixel-by-pixel calculations at single points, neglecting the correlation between deformations at spatially adjacent points, resulting in low monitoring accuracy in permafrost regions.

[0004] Application content

[0005] This application provides a method and related equipment for monitoring deformation in permafrost regions based on spatiotemporal constraints, which can solve the problem of low monitoring accuracy in permafrost deformation monitoring.

[0006] In a first aspect, embodiments of this application provide a method for monitoring deformation in permafrost regions based on spatiotemporal constraints, the monitoring method comprising:

[0007] SAR image data of the target permafrost region are processed to obtain Q-scene interferograms;

[0008] For each target pixel in the interferogram that meets the preset coherence condition, a model is built based on the interannual variation of permafrost deformation in the target permafrost region to obtain the deformation model of the target pixel. The deformation model of the target pixel is then used to process each interferogram to obtain the phase value of the target pixel in all interferograms. The phase value contains only the phase values ​​of high-frequency deformation, low-frequency deformation and noise.

[0009] For each target pixel, the target pixel and other target pixels within the window centered on the target pixel are grouped into a pixel coherence group. An observation equation is established based on all phase values ​​corresponding to the pixel coherence group. Based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group, the weight of each phase value corresponding to the pixel coherence group is determined. Then, using the weight of each phase value and the observation equation, the temporal deformation result of the target pixel is solved.

[0010] The temporal deformation results of all target pixels are integrated to obtain the deformation results of the target permafrost region.

[0011] Optionally, the deformation model is:

[0012]

[0013] Where, d f (t) represents the deformation model value of the f-th target pixel at time t relative to the reference time, where the reference time is the acquisition time of the first image in the SAR image data, f = 1, 2, ..., F, where F represents the total number of target pixels, j represents the j-th permafrost year, j = 1, 2, 3, 4, ..., n, where n represents the total number of permafrost years covered by the SAR image acquisition time range, and t represents the t-th time, t = 1, 2, ..., N, where N represents the total number of images in the SAR image data. This represents a constant term indicating the permafrost deformation of the f-th target pixel over the previous j-years. This represents the long-term deformation rate of the f-th target pixel in the j-th permafrost year. and Both represent the seasonal deformation parameters of the f-th target pixel in the j-th permafrost year, and T represents the seasonal deformation period.

[0014] Optionally, each interferogram is processed using a deformation model of the target pixel to obtain the phase value of the target pixel in all interferograms, including:

[0015] Obtain the unwrapped interferogram data for each interferogram scene;

[0016] Establish the phase equation based on the unwrapped interferogram data:

[0017]

[0018] in, Let represent the unwrapped interferogram data of the i-th interferogram, i = 1, 2, ..., Q, where Q represents the total number of interferograms, λ represents the radar wavelength, and d f (t A ) indicates that the f-th target pixel is at t A The deformation model value at time d relative to the reference time. f (t B This indicates that the f-th target pixel is in t B The deformation model value at time B relative to the reference time, f = 0, 1, 2, ..., F, where F represents the total number of target pixels, and B ⊥,i Let δ be the vertical baseline length of the i-th interferogram. i Z represents the elevation residual of the f-th target pixel, θ is the radar incident angle, and r is the line-of-sight slant range. The residual phase in the i-th interferogram consists of atmospheric delay, high-frequency deformation, and noise.

[0019] The phase equation is solved using the least squares method to obtain the unknown parameters X of the deformation model of the f-th target pixel. f The elevation residual δ of the f-th target pixel f Z;

[0020] Unknown parameters [] T Represents the matrix transpose operation;

[0021] Using the unknown parameters of the deformation model of the f-th target pixel and the elevation residual δ of the f-th target pixel f Z calculates the phase equation to obtain the phase value of the f-th target pixel in the i-th interferogram, which contains only high-frequency deformation, low-frequency deformation, and noise.

[0022] Alternatively, the observation equation is:

[0023]

[0024] in, [] T This represents the matrix transpose operation. The central target pixel in the pixel coherence group contains only the phase values ​​of high-frequency deformation, low-frequency deformation, and noise in the first interferogram. The central target pixel is the target pixel at the center of the window. The phase value of the central target pixel in the pixel coherence group represents the i-th interferogram, which contains only high-frequency deformation, low-frequency deformation, and noise. The phase value of the central target pixel in the pixel coherence group represents the phase value of the Q-th interferogram containing only high-frequency deformation, low-frequency deformation, and noise, where i = 1, 2, ..., Q, and Q represents the total number of interferograms. This indicates that the phase value of the k-th target pixel in the pixel coherence group in the first interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This indicates that the phase value of the k-th target pixel in the pixel coherence group in the i-th interferogram contains only high-frequency deformation, low-frequency deformation, and noise. The phase value of the k-th target pixel in the pixel coherence group in the Q-scene interferogram contains only high-frequency deformation, low-frequency deformation, and noise, where k = 0, 1, 2, ..., K, and K represents the total number of target pixels in the pixel coherence point group. This indicates that the phase value of the Kth target pixel in the pixel coherence group in the first interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This indicates that the phase value of the Kth target pixel in the pixel coherence group in the i-th interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This indicates that the phase value of the Kth target pixel in the pixel coherence group in the Q-th interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This represents the spatial coefficient matrix of the center target pixel in the pixel coherence group. This represents the spatial coefficient matrix of the k-th target pixel in the pixel coherence group. Let l = [def] represent the spatial coefficient matrix of the Kth target pixel in the pixel coherence group. 0 G 11 G 12 ,……,G t1 G t2 ,……,G N1 G N2 ] T , l represents the time-series deformation and gradient change parameter results, def 0 This represents the temporal deformation of the central target pixel in a pixel coherence group. G 11 denoted by , , represents the gradient change parameter of the deformation of the central target pixel in the pixel coherence group at time 1 in the range direction; , , denotes the temporal deformation of the central target pixel in the pixel coherence group at time 1; and , , denotes the range direction. G 12 The gradient change parameter represents the deformation of the central target pixel in the pixel coherence group in the azimuth direction at time 1, where y represents the azimuth direction. G t1 The parameter represents the gradient change of the deformation of the central target pixel in the pixel coherence group at time t in the distance direction. tThis represents the temporal deformation of the central target pixel in the pixel coherence group at time t. G t2 The gradient change parameter represents the deformation of the central target pixel in the pixel coherence group in the azimuth direction at time t, where t = 1, 2, ..., N, and N represents the total number of images in the SAR image data. G N1 The parameter representing the gradient change of the deformation of the central target pixel in the pixel coherence group at time N in the distance direction is denoted as def. N This represents the temporal deformation of the central target pixel in the pixel coherence group at time N. G N2 Let C represent the gradient change parameter of the deformation of the central target pixel in the pixel coherence group in the azimuth direction at time t, and let C represent the coefficient matrix.

[0025] Optionally, based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group, the weight of each phase value corresponding to the pixel coherence group is determined, including:

[0026] Through mutation function Calculate the distance h in the i-th interferogram. k The variogram value γ of the target pixel pair i (h k );

[0027] Where s = 1, 2, ..., R i (h), R i (h) represents the number of target pixels at a distance h in the i-th interferogram, i = 1, 2, ..., Q, where Q represents the total number of interferograms, x0 represents the coordinates of the center target pixel in the pixel coherence group, and x0 + h k The distance h between the center target pixels of the pixel coherence group is represented by the value h. k The target pixel P of the pixel coherence group k coordinates This represents the phase value of the central target pixel in the i-th interferogram of the pixel coherence group. The spatial location is represented as x0+h k The target pixel P of the pixel coherence group k The phase value in the i-th interferogram;

[0028] Through formula For a distance of h k The variogram value γ of the target pixel pair in the i-th interferogram i (h k Fit the data;

[0029] Where b0, b1, and b2 are all unknown parameters of the fitted function;

[0030] Through the formula:

[0031]

[0032] Calculate the weights of the phase values;

[0033] in, The weight represents the phase value of the k-th target pixel in the i-th interferogram, where i = 1, 2, ..., Q, Q represents the total number of interferograms, and k = 1, 2, ..., K, K represents the total number of target pixels in the pixel coherence point group.

[0034] Optionally, using the weights of each phase value and the observation equation, the temporal deformation results of the target pixel are solved, including:

[0035] Using the weights of each phase value and the observation equation, the temporal deformation and gradient change parameters l of the target pixel are obtained as follows:

[0036]

[0037] Where C represents the coefficient matrix, The central target pixel in the pixel coherence group contains only the phase values ​​of high-frequency deformation, low-frequency deformation, and noise in the first interferogram. The central target pixel is the target pixel at the center of the window. The phase value of the central target pixel in the pixel coherence group represents the i-th interferogram, which contains only high-frequency deformation, low-frequency deformation, and noise. The phase value of the central target pixel in the pixel coherence group represents the phase value of the Q-th interferogram containing only high-frequency deformation, low-frequency deformation, and noise, where i = 1, 2, ..., Q, and Q represents the total number of interferograms. This indicates that the phase value of the k-th target pixel in the pixel coherence group in the first interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This indicates that the phase value of the k-th target pixel in the pixel coherence group in the i-th interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This represents the phase value of the k-th target pixel in the pixel coherence group, which contains only high-frequency deformation, low-frequency deformation, and noise in the Q-scene interferogram. k = 0, 1, 2, ..., K, where K represents the total number of target pixels in the pixel coherence point group. This indicates that the phase value of the Kth target pixel in the pixel coherence group in the first interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This indicates that the phase value of the Kth target pixel in the pixel coherence group in the i-th interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This indicates that the phase value of the Kth target pixel in the pixel coherence group in the Q-th interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This represents the spatial coefficient matrix of the center target pixel in the pixel coherence group. This represents the spatial coefficient matrix of the k-th target pixel in the pixel coherence group. Let [ ] represent the spatial coefficient matrix of the Kth target pixel in the pixel coherence group. T This represents the calculation of the transpose of a matrix, where W represents the weight matrix:

[0038]

[0039] Among them, W 0 W k W K Each represents a parameter of the matrix containing the weights:

[0040]

[0041] in, The weight representing the phase value of the k-th target pixel in the pixel coherence group in the first interferogram. The weight representing the phase value of the k-th target pixel in the pixel coherence group in the i-th interferogram. The weight representing the phase value of the k-th target pixel in the pixel coherence group in the Q-scene interferogram;

[0042] Temporal deformation results of target pixels def 0 for:

[0043]

[0044] Here, l is a 3N*1 matrix, where the first to Nth parameters are the temporal deformation results, and the other parameters are gradient change parameters. The first parameter in the result l representing the temporal deformation and gradient change parameters of the target pixel. The t-th parameter in the result l of the temporal deformation and gradient change parameters of the target pixel is represented. The Nth parameter in the result l of the temporal deformation and gradient change parameters of the target pixel is represented by t = 1, 2, ..., N, where N represents the total number of images in the SAR image data.

[0045] Secondly, embodiments of this application provide a permafrost deformation monitoring device based on spatiotemporal constraints, comprising:

[0046] The data processing module processes the SAR image data of the target permafrost area to obtain a Q-scene interferogram;

[0047] The phase value acquisition module is used to model each target pixel in the interferogram that meets the preset coherence conditions based on the interannual variation of permafrost deformation in the target permafrost region, obtain the deformation model of the target pixel, and process each interferogram using the deformation model of the target pixel to obtain the phase value of the target pixel in all interferograms; the phase value contains only the phase values ​​of high-frequency deformation, low-frequency deformation and noise.

[0048] The solution module is used to treat each target pixel separately, treat the target pixel and other target pixels within the window centered on the target pixel as a pixel coherence group, establish observation equations based on all phase values ​​corresponding to the pixel coherence group, and determine the weight of each phase value corresponding to the pixel coherence group based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group; then, using the weight of each phase value and the observation equation, solve for the temporal deformation result of the target pixel.

[0049] The integration module is used to integrate the temporal deformation results of all target pixels to obtain the deformation results of the target permafrost region.

[0050] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned method for monitoring deformation in permafrost regions based on spatiotemporal constraints.

[0051] Fourthly, embodiments of this application provide a computer-readable medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the above-described method for monitoring deformation in permafrost regions based on spatiotemporal constraints.

[0052] The above-mentioned solution in this application has the following beneficial effects:

[0053] In the embodiments of this application, SAR image data of the target permafrost region is processed to obtain Q-scene interferograms. Then, for each target pixel in the interferogram that meets the preset coherence conditions, the target pixel is modeled based on the interannual variation of permafrost deformation in the target permafrost region to obtain the deformation model of the target pixel. The deformation model of the target pixel is then used to process each interferogram to obtain the phase value of the target pixel in all interferograms. Then, for each target pixel, the target pixel and other target pixels within the window centered on the target pixel are taken as a pixel coherence group. An observation equation is established based on all phase values ​​corresponding to the pixel coherence group. Based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group, the weight of each phase value corresponding to the pixel coherence group is determined. Then, the temporal deformation result of the target pixel is solved using the weight of each phase value and the observation equation. Finally, the temporal deformation results of all target pixels are integrated to obtain the deformation result of the target permafrost region. Among them, the establishment of deformation modeling for target pixels based on the interannual variation of permafrost deformation in the target permafrost region takes into account the interannual variation of long-term and seasonal deformation in the permafrost region, which can improve the actual accuracy of the deformation model and is conducive to obtaining highly accurate phase values. The establishment of observation equations to solve the temporal deformation by combining the target pixel and other target pixels in the window centered on the target pixel takes into account the spatial correlation of deformation. Based on the spatial coherence between target pixels, the weights of the phase values ​​are obtained and the observation equations are solved, which can increase the accuracy of the temporal deformation results and effectively improve the accuracy of deformation monitoring in the permafrost region.

[0054] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart illustrating a spatiotemporal constraint-based permafrost deformation monitoring method according to an embodiment of this application;

[0057] Figure 2 This is a schematic diagram of a pixel coherence group provided in an embodiment of this application;

[0058] Figure 3 A schematic diagram of the variation function and its fitting curve provided in an embodiment of this application;

[0059] Figure 4A graph showing the deformation results of a landform site provided in an embodiment of this application and the deformation results of a permafrost deformation monitoring method based on spatiotemporal constraints;

[0060] Figure 5 A schematic diagram of the structure of a permafrost deformation device based on spatiotemporal constraints provided in an embodiment of this application;

[0061] Figure 6 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0062] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0063] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0064] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0065] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0066] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0067] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0068] To address the issue of low accuracy in existing permafrost deformation monitoring, this application provides a permafrost deformation monitoring method based on spatiotemporal constraints. This method processes SAR image data of the target permafrost region to obtain Q-scene interferograms. Then, for each target pixel in the interferogram that meets preset coherence conditions, a deformation model is built based on the interannual variation of permafrost deformation in the target permafrost region. This model is then used to process each interferogram to obtain the phase value of the target pixel across all interferograms. For each target pixel, the target pixel and other target pixels within a window centered on it are grouped into a pixel coherence group. An observation equation is established based on all phase values ​​corresponding to the pixel coherence group. Based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group, the weight of each phase value is determined. Using the weight of each phase value and the observation equation, the temporal deformation result of the target pixel is solved. Finally, the temporal deformation results of all target pixels are integrated to obtain the deformation result of the target permafrost region. Among them, the establishment of deformation modeling for target pixels based on the interannual variation of permafrost deformation in the target permafrost region takes into account the interannual variation of long-term and seasonal deformation in the permafrost region, which can improve the actual accuracy of the deformation model and is conducive to obtaining highly accurate phase values. The establishment of observation equations to solve the temporal deformation by combining the target pixel and other target pixels in the window centered on the target pixel takes into account the spatial correlation of deformation. Based on the spatial coherence between target pixels, the weights of the phase values ​​are obtained and the observation equations are solved, which can increase the accuracy of the temporal deformation results and effectively improve the accuracy of deformation monitoring in the permafrost region.

[0069] The following is an exemplary description of the spatiotemporal constraint-based permafrost deformation monitoring method provided in this application.

[0070] like Figure 1 As shown, the spatiotemporal constraint-based deformation monitoring method for permafrost regions provided in this application includes the following steps:

[0071] Step 11: Process the SAR image data of the target permafrost area to obtain the Q-scene interferogram.

[0072] The aforementioned target permafrost region is the area where permafrost deformation monitoring is required; the aforementioned Synthetic Aperture Radar (SAR) image dataset can be obtained by observing the target permafrost region using SAR satellites.

[0073] For example, the SAR image data mentioned above can be image data from the Sentinel-1 satellite, covering the permafrost region from Wudaoliang to Tuotuohe on the Qinghai-Tibet Plateau (coverage range: 92.33°E-93.76°E, 34.00°N-35.74°N), with SAR image orbit number and frame number 150 and 475 respectively, and a time range from October 11, 2017 to September 20, 2021, which is four permafrost years.

[0074] In some embodiments of this application, the SAR image data described above can be registered, ensuring a registration accuracy better than 1 / 1000 pixel. Interferometric processing is performed on interferometric pairs with a temporal baseline less than 36 days and a spatial baseline less than 150m to ultimately obtain a common-Q interferogram.

[0075] Step 12: For each target pixel in the interferogram that meets the preset coherence condition, model the target pixel based on the interannual variation of permafrost deformation in the target permafrost region to obtain the deformation model of the target pixel. Then, process each interferogram using the deformation model of the target pixel to obtain the phase value of the target pixel in all interferograms.

[0076] The aforementioned preset coherence condition can be: average coherence greater than 0.85 and minimum coherence greater than 0.3. That is, if the average coherence of a pixel in the generated Q-scene interferogram is greater than 0.85 and the minimum coherence is greater than 0.3, then the pixel is identified as the target pixel. The coherence can be understood with reference to the following example: Assume that the master and slave SAR images (i.e., two images in the SAR image data) that generate the i-th interferogram were acquired at times t and t, respectively. A and t B Then, the coherence of pixel K in the i-th interferogram refers to the coherence of that pixel in time t. A and t B Timing coherence. For example, the target pixel mentioned above can be understood as an image element in the interferogram that maintains high correlation with reference objects (such as specific plants, soil, and other geographical elements) throughout the entire time range; the phase value mentioned above is a phase value that only includes high-frequency deformation, low-frequency deformation, and noise.

[0077] In some embodiments of this application, the target pixel is modeled based on the interannual variation of permafrost deformation in the target permafrost region, and the unwrapped interferogram data of each interferogram is obtained. Then, a phase equation is established for the unwrapped interferogram data of each interferogram using the deformation model of the target pixel, and the phase equation is solved using the least squares method to obtain the unknown parameters and elevation residuals of the deformation model. The phase equation is then calculated using the unknown parameters and elevation residuals of the deformation model to obtain the phase value containing only high-frequency deformation, low-frequency deformation and noise.

[0078] It is worth mentioning that the deformation modeling of target pixels based on the interannual variation of permafrost deformation in the target permafrost region takes into account the interannual variation of long-term and seasonal deformation in the permafrost region, which can improve the actual accuracy of the deformation model. By establishing the phase equation for the unwrapped interferogram data and removing the redundant phase, high-accuracy phase values ​​can be obtained.

[0079] Step 13: For each target pixel, the target pixel and other target pixels within the window centered on the target pixel are treated as a pixel coherence group. An observation equation is established based on all phase values ​​corresponding to the pixel coherence group. Based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group, the weight of each phase value corresponding to the pixel coherence group is determined. Then, using the weight of each phase value and the observation equation, the temporal deformation result of the target pixel is solved.

[0080] In some embodiments of this application, based on the spatial deformation similarity theory, a temporal deformation function model is constructed for the pixel coherence group, and an observation equation is established for the phase values ​​of all target pixels in each interferogram of the pixel coherence group using the temporal deformation function model. The variogram function is used to calculate the phase values ​​of multiple target pixels in each interferogram, obtaining the variogram value of the phase value. The weight of the phase value is then calculated using a formula. Finally, the temporal deformation result of the target pixel is solved using the weight of each phase value and the observation equation.

[0081] It is worth mentioning that by establishing the observation equation by combining the phase values ​​of the target pixel and other target pixels within the window centered on the target pixel, the spatial correlation of deformation is taken into account. Since the distance between the central target pixel and different target pixels is different, the deformation correlation between the central target pixel and different target pixels is different. By calculating the phase value according to the variogram, the weight of the phase value can be obtained, and the observation equation can be solved, which can increase the accuracy of the temporal deformation results of the target pixel.

[0082] Step 14: Integrate the temporal deformation results of all target pixels to obtain the deformation results of the target permafrost region.

[0083] In some embodiments of this application, the deformation results of the target permafrost region can be obtained by integrating the temporal deformation results of all target pixels onto the same image.

[0084] It is worth mentioning that the deformation modeling of target pixels based on the interannual variation of permafrost deformation in the target permafrost region takes into account the interannual variation of long-term and seasonal deformation in the permafrost region, which can improve the actual accuracy of the deformation model and facilitate the obtaining of highly accurate phase values. By jointly establishing observation equations with the target pixel and other target pixels within the window centered on the target pixel to solve the temporal deformation, the spatial correlation of deformation is taken into account. Based on the spatial coherence between target pixels, the weights of the phase values ​​are obtained and the observation equations are solved, which can increase the accuracy of the temporal deformation results and effectively improve the accuracy of deformation monitoring in the permafrost region.

[0085] The specific steps of step 12 described above will be illustrated below with reference to specific embodiments.

[0086] In some embodiments of this application, the specific implementation process of step 12 above includes the following steps:

[0087] Step 12.1: For each target pixel in the interferogram that meets the preset coherence conditions, model the target pixel based on the interannual variation of permafrost deformation in the target permafrost region to obtain the deformation model of the target pixel.

[0088] The deformation model is as follows:

[0089]

[0090] Where, d f (t) represents the deformation model value of the f-th target pixel at time t relative to the reference time, where the reference time is the acquisition time of the first image in the SAR image data, f = 1, 2, ..., F, where F represents the total number of target pixels, j represents the j-th permafrost year, j = 1, 2, 3, 4, ..., n, where n represents the total number of permafrost years covered by the acquisition time range of the SAR image (i.e., the SAR image data in step 11), and t represents the t-th time, t = 1, 2, ..., N, where N is the total number of images in the SAR image data. This represents a constant term indicating the permafrost deformation of the f-th target pixel over the previous j-years. This represents the long-term deformation rate of the f-th target pixel in the j-th permafrost year. and Both represent the seasonal deformation parameters of the f-th target pixel in the j-th permafrost year, and T represents the seasonal deformation period.

[0091] In some embodiments of this application, the aforementioned seasonal deformation cycle is set to one year.

[0092] It is worth mentioning that the deformation model provided in this application is a deformation model constructed based on a piecewise periodic model for the interannual variation of permafrost deformation of the target pixel. This model can improve the actual accuracy of the deformation model and is conducive to obtaining highly accurate phase values.

[0093] Step 12.2: Obtain the unwrapped interferogram data for each interferogram scene and establish the phase equation.

[0094] Differential interferometry is applied to each interferogram scene using a Global Digital Elevation Model (GDM) to obtain differential interferograms. Then, a two-dimensional frequency domain filter is applied to the differential interferograms using the Goldstein filtering algorithm (a type of interferogram filtering algorithm). Finally, two-dimensional phase unwrapping is performed using the minimum network cost flow method, removing pixels with coherence below 0.3 during the unwrapping process. The unwrapped interferogram data for the i-th interferogram scene is then obtained.

[0095] Establish the phase equation based on the unwrapped interferogram data:

[0096]

[0097] in, Let represent the unwrapped interferogram data of the i-th interferogram, i = 1, 2, ..., Q, where Q represents the total number of interferograms, λ represents the radar wavelength of the radar that acquired the SAR image, and d f (t A ) indicates that the f-th target pixel is at t A The deformation model value at time d relative to the reference time. f (t B ) indicates that the f-th target pixel is at t B The deformation model value at time step B relative to the reference time step, f = 1, 2, ..., F, where F represents the total number of target pixels, and B... ⊥，i Let δ be the vertical baseline length of the i-th interferogram. f Z represents the elevation residual of the f-th target pixel, θ represents the radar incident angle of the radar acquiring the SAR image, and r is the line-of-sight slant range. The residual phase in the i-th interferogram consists of atmospheric delay, high-frequency deformation, and noise.

[0098] It is worth mentioning that removing pixels with coherence below 0.3 during the unwrapping process is to avoid the influence of low-coherence pixels on unwrapping. By obtaining unwrapped interferogram data and establishing a phase equation, redundant phase can be easily removed, and accurate phase values ​​can be obtained.

[0099] Step 12.3: Solve the phase equation using the least squares method to obtain the unknown parameters of the deformation model of the f-th target pixel and the elevation residual δ of the f-th target pixel.f Z:

[0100]

[0101] in, Xf represents the unknown parameters of the deformation model for the f-th target pixel, [] T This represents the transpose operation of a matrix, where B is the coefficient matrix and P is the identity matrix.

[0102] Using the unknown parameters of the deformation model corresponding to the f-th target pixel and the elevation residual δ of the f-th target pixel f Z calculates the phase equation to obtain the phase value of the f-th target pixel in the i-th interferogram, which contains only high-frequency deformation, low-frequency deformation, and noise.

[0103] Specifically, from the unwrapped interferogram data of the i-th interferogram mentioned above... Subtract the low-frequency deformation phase of the f-th target pixel from the middle and elevation residual phase Obtain the residual phase

[0104] Residual phase Including atmospheric delay phase High-frequency deformable phase and noise are subjected to time high-pass filtering and spatial low-pass filtering to separate the atmospheric delayed phase.

[0105] Again, the unwrapped interferogram data of the i-th interferogram. Perform the calculation, subtracting the elevation residual phase from its phase equation. and atmospheric delay phase Preserve low-frequency deformation phase High-frequency deformation phase and noise are used to obtain the phase value of the f-th target pixel in the i-th interferogram, which contains only high-frequency deformation, low-frequency deformation and noise.

[0106] It is worth mentioning that by establishing a phase equation for the unwrapped interferogram data and removing redundant phases, highly accurate phase values ​​can be obtained.

[0107] The specific steps of step 13 described above will be illustrated below with reference to specific embodiments.

[0108] In some embodiments of this application, the specific implementation process of step 13 above includes the following steps:

[0109] Step 13.1: Based on the spatial deformation similarity theory, construct a temporal deformation function model for the pixel coherence group.

[0110] In some embodiments of this application, based on the spatial deformation similarity theory, regardless of the length of the temporal sequence, the deformation of spatially adjacent points is generally correlated and changes along a certain gradient within a local window. For each target pixel, the target pixel and other target pixels within the window centered on the target pixel are considered as a pixel coherence group, and a temporal deformation function model is constructed for the pixel coherence group:

[0111]

[0112] Among them, def k The term "def" represents the temporal deformation of the k-th target pixel in the pixel coherence group. 0 This represents the temporal deformation of the central target pixel in a pixel coherence group. The central target pixel is the target pixel that serves as the center of the window, k = 1, 2, ..., K, where K represents the total number of target pixels in the pixel coherence group. [] T This represents the matrix transpose operation. G 11 denoted by , , represents the gradient change parameter of the deformation of the central target pixel in the pixel coherence group at time 1 in the range direction; , , denotes the temporal deformation of the central target pixel in the pixel coherence group at time 1; and , , denotes the range direction. G 12 The gradient change parameter represents the deformation of the central target pixel in the pixel coherence group in the azimuth direction at time 1, where y represents the azimuth direction. G t1 The parameter represents the gradient change of the deformation of the central target pixel in the pixel coherence group at time t in the distance direction. t This represents the temporal deformation of the central target pixel in the pixel coherence group at time t. G t2 The gradient change parameter represents the deformation of the central target pixel in the pixel coherence group in the azimuth direction at time t, where t = 1, 2, ..., N, and N represents the total number of images in the SAR image data. G N1 The parameter representing the gradient change of the deformation of the central target pixel in the pixel coherence group at time N in the distance direction is denoted as def. N This represents the temporal deformation of the central target pixel in the pixel coherence group at time N. G N2 The gradient change parameter represents the deformation of the central target pixel in the pixel coherence group in the azimuth direction at time t. The spatial coefficient matrix representing the k-th target pixel in the pixel correlation group:

[0113]

[0114] Where, Δx k Δy represents the x-axis coordinate increment of the k-th target pixel in the pixel correlation group. k This represents the y-axis coordinate increment of the k-th target pixel in the pixel correlation group.

[0115] The above steps are illustrated below with reference to a specific embodiment.

[0116] In one embodiment of this application, a pixel coherence group is as follows: Figure 2 As shown, among a total of F target pixels, target pixel 0 is selected as the center, and all target pixels within a 3×3 window centered on target pixel 0 are selected: target pixel a, target pixel b, target pixel c, target pixel d, target pixel e, target pixel f, target pixel g, and target pixel h. These target pixels and target pixel 0 are then grouped into a pixel coherence point group.

[0117] Step 13.2: Establish the observation equation based on all phase values ​​corresponding to the pixel coherence group.

[0118] The above observation equation is:

[0119]

[0120] in, [] T This represents the matrix transpose operation. The central target pixel in the pixel coherence group contains only the phase values ​​of high-frequency deformation, low-frequency deformation, and noise in the first interferogram. The central target pixel is the target pixel at the center of the window. The phase value of the central target pixel in the pixel coherence group represents the i-th interferogram, which contains only high-frequency deformation, low-frequency deformation, and noise. The phase value of the central target pixel in the pixel coherence group represents the phase value of the Q-th interferogram containing only high-frequency deformation, low-frequency deformation, and noise, where i = 1, 2, ..., Q, and Q represents the total number of interferograms. This indicates that the phase value of the k-th target pixel in the pixel coherence group in the first interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This indicates that the phase value of the k-th target pixel in the pixel coherence group in the i-th interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This represents the phase value of the k-th target pixel in the pixel coherence group, which contains only high-frequency deformation, low-frequency deformation, and noise in the Q-scene interferogram. k = 0, 1, 2, ..., K, where K represents the total number of target pixels in the pixel coherence point group. This indicates that the phase value of the Kth target pixel in the pixel coherence group in the first interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This indicates that the phase value of the Kth target pixel in the pixel coherence group in the i-th interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This indicates that the phase value of the Kth target pixel in the pixel coherence group in the Q-th interferogram contains only high-frequency deformation, low-frequency deformation, and noise. This represents the spatial coefficient matrix of the center target pixel in the pixel coherence group. This represents the spatial coefficient matrix of the k-th target pixel in the pixel coherence group. Let l = [def] represent the spatial coefficient matrix of the Kth target pixel in the pixel coherence group. 0 G 11 G 12 , ..., G t1 G t2 , ..., G N1 G N2 ] T l represents the time-series deformation and deformation gradient change parameter results, and C represents the coefficient matrix:

[0121]

[0122] Each row and column of the coefficient matrix corresponds to an interferogram and a SAR image, respectively.

[0123] It is worth mentioning that the observation equation is established based on all phase values ​​corresponding to the pixel coherence group, which takes into account the spatial correlation of deformation of adjacent pixels and can increase the accuracy of temporal deformation results.

[0124] Step 13.3: Calculate the phase value of the target pixel using the variogram function, obtain the variogram value of the phase value, and then fit it.

[0125] Through mutation function Calculate the distance h in the i-th interferogram. k The variogram value γ of the target pixel pair i (h k );

[0126] Where s = 1, 2, ..., R i (h), R i (h) represents the number of target pixels at a distance h in the i-th interferogram, i = 1, 2, ..., Q, where Q represents the total number of interferograms, x0 represents the coordinates of the center target pixel in the pixel coherence group, and x0 + h k The distance h between the center target pixels of the pixel coherence group is represented by the value h. k The coordinates of the target pixel Pk in the pixel coherence group. This represents the phase value of the central target pixel in the pixel coherence group within the i-th scene interferogram. The spatial location is represented as x0+h k The target pixel P of the pixel coherence group k The phase value in the i-th interferogram;

[0127] Through formula For a distance of h k The variogram value γ of the target pixel pair in the i-th interferogram i (h k Fit the data.

[0128] Where b0, b1, and b2 are all unknown parameters of the fitted function.

[0129] The above steps are illustrated below with reference to a specific embodiment.

[0130] In one embodiment of this application, the variogram value and fitting result of one interferogram are as follows: Figure 3 As shown in the figure, the horizontal axis represents the distance between two target pixels (1ag distance h), and the vertical axis represents the variogram value.

[0131] Step 13.4: Calculate the variogram value to obtain the weight of the phase value.

[0132] Through the formula:

[0133]

[0134] Calculate the weights of the phase values.

[0135] in, The weight represents the phase value of the k-th target pixel in the i-th interferogram, where i = 1, 2, ..., Q, Q represents the total number of interferograms, and k = 1, 2, ..., K, K represents the total number of target pixels in the pixel coherence point group.

[0136] It is worth mentioning that the deformation correlation between the central target pixel and different target pixels is different when the distance between the central target pixel and different target pixels is different. The variogram can be used to calculate the phase value based on the correlation, obtain the weight of the phase value, and determine the weight of the observation equation.

[0137] Step 13.5 uses the weight of each phase value and the observation equation to solve for the temporal deformation result of the target pixel.

[0138] In some embodiments of this application, the temporal deformation and gradient change parameter result l of the target pixel is:

[0139]

[0140] Where W represents the weight matrix of the phase values:

[0141]

[0142] Among them, W 0 W k W K All represent the parameters of the matrix:

[0143]

[0144] in, The weight representing the phase value of the k-th target pixel in the pixel coherence group in the first interferogram. The weight representing the phase value of the k-th target pixel in the pixel coherence group in the i-th interferogram. The weight represents the phase value of the k-th target pixel in the Q-th interferogram of the pixel coherence group.

[0145] Temporal deformation results of target pixels def 0 for:

[0146]

[0147] The final calculated value of l is a 3N*1 matrix, where the first to N parameters are the temporal deformation results, and the other parameters are gradient change parameters. Specifically, The first parameter in the result l representing the temporal deformation and gradient change parameters of the target pixel. The t-th parameter in the result l of the temporal deformation and gradient change parameters of the target pixel is represented. The Nth parameter in the result l of the temporal deformation and gradient change parameters of the target pixel is represented by t = 1, 2, ..., N, where N represents the total number of images in the SAR image data.

[0148] It is worth mentioning that the deformation modeling of target pixels based on the interannual variation of permafrost deformation in the target permafrost region takes into account the interannual variation of long-term and seasonal deformation in the permafrost region, which can improve the actual accuracy of the deformation model and facilitate the obtaining of highly accurate phase values. By jointly establishing observation equations with the target pixel and other target pixels within the window centered on the target pixel to solve the temporal deformation, the spatial correlation of deformation is taken into account. Based on the spatial coherence between target pixels, the weights of the phase values ​​are obtained and the observation equations are solved, which can increase the accuracy of the temporal deformation results and effectively improve the accuracy of deformation monitoring in the permafrost region.

[0149] The above steps will be illustrated with a specific example below.

[0150] The comparison curves between the deformation results of the landform site and the deformation results of the permafrost deformation monitoring method based on spatiotemporal constraints provided in this application are shown in the figure below. Figure 4 As shown in the figure, GNSS represents the deformation results of the landform site, and the GNSS fit represents the fitted curve of the deformation results of the landform site, with a fitting coefficient R. 2 =0.86928. The root mean square error (RMS) of the periodic model curve shown in the figure is 8.5712 mm, the RMS of the piecewise periodic model (i.e., the deformation model mentioned above) is 8.074 mm, the RMS of the spatiotemporal constraint model is 7.007 mm, and the RMS of the solution method without a deformation model (InSAR-RAW) is 9.2895 mm. The horizontal axis in the figure represents the date, and the vertical axis represents the deformation result, in millimeters (mm).

[0151] It can be seen that the deformation results obtained by the piecewise periodic model are better than those of the traditional periodic model, with the root mean square error reduced by about 5.8%. This indicates that the deformation model that considers the interannual variation of long-term and seasonal deformation of permafrost is more consistent with the actual deformation of permafrost and helps to better remove redundant phases. At the same time, the root mean square error of the time-series deformation results obtained by the monitoring method provided in this application is the smallest, at only 7.007 mm, which is about 18.2% more accurate than the traditional periodic model. This shows that the deformation monitoring method for permafrost regions that combines the piecewise periodic model and spatial constraints obtains more accurate deformation results.

[0152] It can be seen that the spatiotemporal constraint-based permafrost deformation monitoring method provided in this application can increase the accuracy of time-series deformation results and effectively improve the precision of permafrost deformation monitoring.

[0153] like Figure 5 As shown, this application embodiment provides a permafrost deformation monitoring device based on spatiotemporal constraints. The permafrost deformation monitoring device 500 based on spatiotemporal constraints includes:

[0154] The data processing module 501 processes the SAR image data of the target permafrost area to obtain a Q-scene interferogram.

[0155] The phase value acquisition module 502 is used to model the target pixel based on the interannual variation of permafrost deformation in the target permafrost region for each target pixel that meets the preset coherence conditions in the interferogram, obtain the deformation model of the target pixel, and process each interferogram through the deformation model of the target pixel to obtain the phase value of the target pixel in all interferograms.

[0156] The solver module 503 is used to treat each target pixel separately, taking the target pixel and other target pixels within the window centered on the target pixel as a pixel coherence group, establishing an observation equation based on all phase values ​​corresponding to the pixel coherence group, and determining the weight of each phase value corresponding to the pixel coherence group based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group; then using the weight of each phase value and the observation equation, the temporal deformation result of the target pixel is solved.

[0157] The integration module 504 is used to integrate the temporal deformation results of all target pixels to obtain the deformation results of the target permafrost region.

[0158] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0159] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0160] like Figure 6 As shown, an embodiment of this application provides a terminal device, wherein the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 6 The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 executes the computer program D102 to implement the steps in any of the above method embodiments.

[0161] Specifically, when the processor D100 executes the computer program D102, it processes the SAR image data of the target permafrost region to obtain a Q-scene interferogram. Then, for each target pixel in the interferogram that meets the preset coherence conditions, it models the target pixel based on the interannual variation of permafrost deformation in the target permafrost region to obtain the deformation model of the target pixel. The deformation model of the target pixel is then used to process each interferogram to obtain the phase value of the target pixel in all interferograms. Then, for each target pixel, the target pixel and other target pixels within the window centered on the target pixel are treated as a pixel coherence group. An observation equation is established based on all phase values ​​corresponding to the pixel coherence group. Based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group, the weight of each phase value corresponding to the pixel coherence group is determined. Then, using the weight of each phase value and the observation equation, the temporal deformation result of the target pixel is solved. Finally, the temporal deformation results of all target pixels are integrated to obtain the deformation result of the target permafrost region. Among them, the establishment of deformation modeling for target pixels based on the interannual variation of permafrost deformation in the target permafrost region takes into account the interannual variation of long-term and seasonal deformation in the permafrost region, which can improve the actual accuracy of the deformation model and facilitate the obtaining of highly accurate phase values. The observation equation is established by combining the target pixel and other target pixels within the window centered on the target pixel, taking into account the spatial correlation of deformation. Based on the spatial coherence between target pixels, the weight of the phase value is obtained and the observation equation is solved, which can increase the accuracy of time-series deformation results and effectively improve the accuracy of deformation monitoring in the permafrost region.

[0162] The processor D100 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0163] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0164] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0165] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0166] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the device / terminal equipment for the spatiotemporally constrained permafrost deformation monitoring method, recording media, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0167] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0168] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0169] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A permafrost area deformation monitoring method based on space-time constraints, characterized in that, include: SAR image data of a target frozen earth region are processed to obtain Q a landscape interferogram; For each target pixel in the interferogram that meets the preset coherence condition, a model is created based on the interannual variation of permafrost deformation in the target permafrost region to obtain the deformation model of the target pixel. The deformation model of the target pixel is then used to process each interferogram to obtain the phase value of the target pixel in all interferograms. The phase value contains only high-frequency deformation, low-frequency deformation, and noise. For each target pixel, the target pixel and other target pixels within a window centered on the target pixel are grouped into a pixel coherence group. An observation equation is established based on all phase values ​​corresponding to the pixel coherence group. Based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group, the weight of each phase value corresponding to the pixel coherence group is determined. Then, using the weight of each phase value and the observation equation, the temporal deformation result of the target pixel is solved; The temporal deformation results of all target pixels are integrated to obtain the deformation results of the target permafrost region; The deformation model is: in, Indicates the first f Each target pixel in The deformation model value of a given time relative to a reference time, where the reference time is the acquisition time of the first image in the SAR image data. f =1,2,..., F , F Indicates the total number of target pixels. j Indicates the first One permafrost year, , This indicates the total number of years of permafrost covered by the area at the time the SAR image was acquired. t Indicates the first t At that moment, t =1,2,..., N , N This indicates the total number of images in the SAR image data. Indicates the first f Each target pixel is in front. A constant term for the annual permafrost deformation of permafrost. Indicates the first f The target pixel in the first The long-term deformation rate of permafrost over several years and All indicate the first f The target pixel in the first Seasonal deformation parameters of permafrost over several years T It indicates the seasonal deformation cycle.

2. The monitoring method according to claim 1, characterized in that, The process of processing each interferogram using the deformation model of the target pixel to obtain the phase value of the target pixel in all interferograms includes: Obtain the unwrapped interferogram data for each scene of the interferogram; A phase equation is established based on the unwrapped interferogram data: in, Indicates the first i The unwrapped interferogram data of the scene interferogram, i =1,2,..., Q , Q This represents the total number of the interferograms. Indicates the radar wavelength. Indicates the first f Each target pixel in The deformation model value at time 1 relative to the reference time. Indicates the first f Each target pixel in The deformation model value at time 1 relative to the reference time. f =1,2,..., F , F Indicates the total number of target pixels. For the first i The vertical baseline length of the interferogram. Indicates the first f Elevation residual of each target pixel The radar incident angle, The slant distance of the line of sight. For the first i The residual phase in the interferogram consists of atmospheric delay, high-frequency deformation, and noise. The phase equation is solved using the least squares method to obtain the first... f Unknown parameters of the deformation model for each target pixel and the f Elevation residual of each target pixel ; the unknown parameters , denotes the transposition operation of a matrix; Using the first f The unknown parameters of the deformation model of the first target pixel and the first f Elevation residual of each target pixel The phase equation is calculated to obtain the first... f The target pixel in the first i The interferogram contains only the phase values ​​of high-frequency deformation, low-frequency deformation, and noise. .

3. The monitoring method according to claim 2, characterized in that, The observation equation is: in, , This represents the matrix transpose operation. This indicates that the central target pixel of the pixel coherence group contains only the phase values ​​of high-frequency deformation, low-frequency deformation, and noise in the first interferogram. The central target pixel is the target pixel that serves as the center of the window. The central target pixel of the pixel coherence group is in the 1st... i The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. The central target pixel of the pixel coherence group is in the 1st... Q The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. i =1,2,..., Q , Q This represents the total number of the interferograms. , The first pixel coherence group represents the first pixel coherence group. k Each target pixel in the first interferogram contains only phase values ​​of high-frequency deformation, low-frequency deformation, and noise. The first pixel coherence group represents the first pixel coherence group. k The target pixel in the first i The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. The first pixel coherence group represents the first pixel coherence group. k The target pixel in the first Q The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. k =0,1,2,..., K , K This represents the total number of target pixels in the pixel coherence point group. , The first pixel coherence group represents the first pixel coherence group. K Each target pixel in the first interferogram contains only phase values ​​of high-frequency deformation, low-frequency deformation, and noise. The first pixel coherence group represents the first pixel coherence group. K The target pixel in the first i The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. The first pixel coherence group represents the first pixel coherence group. K The target pixel in the first Q The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. This represents the spatial coefficient matrix of the center target pixel in the pixel coherence group. The first pixel coherence group represents the first pixel coherence group. k The spatial coefficient matrix of each target pixel The first pixel coherence group represents the first pixel coherence group. K The spatial coefficient matrix of each target pixel , This represents the results of time-series deformation and gradient change parameters. This indicates the temporal deformation of the central target pixel in the pixel coherence group. , This represents the gradient change parameter of the deformation of the central target pixel in the pixel coherence group in the distance direction at the first time step. This indicates the temporal deformation of the central target pixel in the pixel coherence group at the first time step. Indicates the direction of distance. , This represents the gradient change parameter indicating the deformation of the central target pixel in the pixel coherence group in the azimuth direction at the first time step. Indicates direction or location. , The central target pixel of the pixel coherence group is in the 1st... t The gradient change parameter of the deformation in the distance direction at a given time. The central target pixel of the pixel coherence group is in the 1st... t Temporal distortion at each moment, , The central target pixel of the pixel coherence group is in the 1st... t The gradient change parameter of the deformation in the azimuth direction at a given time. t =1,2,..., N , N This indicates the total number of images in the SAR image data. , The central target pixel of the pixel coherence group is in the 1st... N The gradient change parameter of the deformation in the distance direction at a given time. The central target pixel of the pixel coherence group is in the 1st... N Temporal distortion at each moment, , The central target pixel of the pixel coherence group is in the 1st... t The gradient change parameter of the deformation in the azimuth direction at a given time. This represents the coefficient matrix.

4. The monitoring method of claim 1, wherein, The step of determining the weight of each phase value corresponding to the pixel coherence group based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group includes: By the variogram function The variogram function is computed for each pair of target pixels separated by a distance of i ;​​ in, s =1,2,..., ( h ), ( h ) indicates the first i The distance in the interferogram is h The logarithm of the target pixels, i =1,2,..., Q , Q This represents the total number of the interferograms. This indicates the coordinates of the center target pixel of the pixel coherence group. The distance between the center target pixel and the coherent group of pixels is... The target pixel of the pixel coherence group coordinates The central target pixel of the pixel coherence group is in the 1st... i Phase values ​​in the interferogram Indicates spatial location as The target pixel of the pixel coherence group In the i Phase values ​​in the interferogram; Through formula For the distance is Target pixel pair in the first i Variation function values ​​in the interferogram Perform fitting; in, , and All of these are unknown parameters of the fitted function; Through the formula: Calculate the weights of the phase values; in, Indicates the first k The target pixel in the first The weights of phase values ​​in the interferogram. i= 1,2,..., Q , Q This indicates the total number of interferograms. k= 1,2,..., K , K This represents the total number of target pixels in the pixel coherence point group.

5. The monitoring method according to claim 1, characterized in that, The step of solving for the temporal deformation result of the target pixel using the weight of each phase value and the observation equation includes: Using the weights of each phase value and the observation equation, the temporal deformation and gradient change parameters of the target pixel are obtained. l for: in, Represents the coefficient matrix. , This indicates that the central target pixel of the pixel coherence group contains only the phase values ​​of high-frequency deformation, low-frequency deformation, and noise in the first interferogram. The central target pixel is the target pixel that serves as the center of the window. The central target pixel of the pixel coherence group is in the 1st... i The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. The central target pixel of the pixel coherence group is in the 1st... Q The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. i =1,2,..., Q , Q This represents the total number of the interferograms. , The first pixel coherence group represents the first pixel coherence group. k Each target pixel in the first interferogram contains only phase values ​​of high-frequency deformation, low-frequency deformation, and noise. The first pixel coherence group represents the first pixel coherence group. k The target pixel in the first i The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. The first pixel coherence group represents the first pixel coherence group. k The target pixel in the first Q The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. k =0,1,2,..., K , K This represents the total number of target pixels in the pixel coherence point group. , The first pixel coherence group represents the first pixel coherence group. K Each target pixel in the first interferogram contains only phase values ​​of high-frequency deformation, low-frequency deformation, and noise. The first pixel coherence group represents the first pixel coherence group. K The target pixel in the first i The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. The first pixel coherence group represents the first pixel coherence group. K The target pixel in the first Q The interferogram only contains phase values ​​of high-frequency deformation, low-frequency deformation, and noise. This represents the spatial coefficient matrix of the center target pixel in the pixel coherence group. The first pixel coherence group represents the first pixel coherence group. k The spatial coefficient matrix of each target pixel The first pixel coherence group represents the first pixel coherence group. K The spatial coefficient matrix of each target pixel This represents the calculation of the transpose of a matrix. The matrix representing the weights: in, , , Each represents a parameter of the matrix containing the weights: in, The first pixel coherence group represents the first pixel coherence group. k The weights of the phase values ​​of each target pixel in the first interferogram. The first pixel coherence group represents the first pixel coherence group. k The target pixel in the first i The weights of phase values ​​in the interferogram. The first pixel coherence group represents the first pixel coherence group. k The target pixel in the first Q The weights of phase values ​​in the interferogram; The temporal deformation result of the target pixel for: in, l It's a 3 N A matrix of size *1 l Middle 1~ N One parameter represents the temporal deformation result, while the other parameters are gradient change parameters. This represents the temporal deformation and gradient change parameter results of the target pixel. l The first parameter in This represents the temporal deformation and gradient change parameter results of the target pixel. l The first in t One parameter, This represents the temporal deformation and gradient change parameter results of the target pixel. l The first in N One parameter, t =1,2,..., N , N This indicates the total number of images in the SAR imagery data.

6. A deformation monitoring device for permafrost regions based on spatiotemporal constraints, characterized in that, include: The data processing module processes the SAR image data of the target permafrost area to obtain... Q Interference diagram; The phase value acquisition module is used to model each target pixel that meets the preset coherence condition in the interferogram, based on the interannual variation of permafrost deformation in the target permafrost region, to obtain the deformation model of the target pixel, and to process each interferogram using the deformation model of the target pixel to obtain the phase value of the target pixel in all interferograms; the phase value is the phase value that only includes high-frequency deformation, low-frequency deformation and noise; The solution module is used to, for each target pixel, take the target pixel and other target pixels within a window centered on the target pixel as a pixel coherence group, establish an observation equation based on all phase values ​​corresponding to the pixel coherence group, and determine the weight of each phase value corresponding to the pixel coherence group based on the spatial correlation between the target pixel and other target pixels in the pixel coherence group. Then, using the weight of each phase value and the observation equation, the temporal deformation result of the target pixel is solved; The integration module is used to integrate the temporal deformation results of all target pixels to obtain the deformation results of the target permafrost region; The deformation model is: in, Indicates the first f Each target pixel in The deformation model value of a given time relative to a reference time, where the reference time is the acquisition time of the first image in the SAR image data. f =1,2,..., F , F Indicates the total number of target pixels. j Indicates the first One permafrost year, , This indicates the total number of years of permafrost covered by the area at the time the SAR image was acquired. t Indicates the first t At that moment, t =1,2,..., N , N This indicates the total number of images in the SAR image data. Indicates the first f Each target pixel is in front. A constant term for the annual permafrost deformation of permafrost. Indicates the first f The target pixel in the first The long-term deformation rate of permafrost over several years and All indicate the first f The target pixel in the first Seasonal deformation parameters of permafrost over several years T It indicates the seasonal deformation cycle.

7. 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, it implements the permafrost deformation monitoring method based on spatiotemporal constraints as described in any one of claims 1 to 5.

8. A computer-readable medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the spatiotemporal constraint-based permafrost deformation monitoring method as described in any one of claims 1 to 5.

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