InSAR temporal deformation estimation methods, electronic devices and readable storage media

Abstract

This invention discloses an InSAR temporal deformation estimation method, electronic device, and readable storage medium. It filters and retains high-reliability temporal deformation pixels and high-reliability annual average deformation rate pixels from the temporal deformation data and annual average deformation rate data in the estimation results. This invention fully considers the spatiotemporal correlation of decoherence errors in the small baseline set interferometric phase and temporal deformation, constructing the small baseline decoherence error covariance matrix and the temporal decoherence error covariance matrix respectively, and incorporating them into the temporal deformation and annual average deformation rate estimation processes as constraints. This effectively suppresses decoherence noise, improves the resolution and detail representation capability of the deformation map, and ultimately achieves the goal of improving the accuracy of temporal deformation results and annual average deformation rate estimation.

Description

Technical Field

[0001] This invention relates to the field of small baseline aggregate aperture radar interferometry technology, and in particular to an InSAR time-series deformation estimation method, electronic device, and readable storage medium. Background Technology

[0002] Interferometric Synthetic Aperture Radar (InSAR) technology, with its advantages of all-weather, all-time monitoring and high precision, has become an important means of monitoring surface deformation. Among them, Small Baseline Subset InSAR (SBAS-InSAR) technology, by constructing a short-spatial-temporal baseline interferogram network, can effectively suppress the effects of spatiotemporal decoherence and atmospheric delay, and has been widely used in fields such as mining area deformation monitoring, landslide monitoring, and urban land subsidence monitoring. However, in practical applications, due to the non-uniformity of InSAR data in some areas (i.e., uneven and large intervals between observations), and in complex surface environments, factors such as significant changes in the scattering characteristics of ground objects, excessively large deformation magnitudes, and seasonal freeze-thaw cycles can lead to severe interferometric phase decoherence, thus reducing the accuracy and reliability of deformation estimation results.

[0003] Traditional SBAS-InSAR methods typically treat the decoherence errors in each interferometric phase and temporal deformation within a small baseline set as independent. They suppress these errors by limiting the spatiotemporal baseline or by constructing a diagonal weight matrix based on coherence, variance, or Fisher information, while ignoring the temporal correlation of these errors. In reality, changes in surface scattering characteristics are temporally continuous; decoherence errors in adjacent time phases are not independently identically distributed but exhibit significant temporal correlation. However, traditional SBAS-InSAR methods often neglect the covariance of the interferometric phase and temporal deformation within the small baseline set when constructing the covariance matrix, simplifying it to a diagonal matrix in the estimation of temporal deformation and annual average deformation rate. This traditional inversion method uniformly fits deformation and noise signals, leading to reduced accuracy in InSAR temporal deformation estimation results, increased statistical errors in deformation maps, image blurring, and loss of deformation details, making it difficult to obtain high-precision, high-resolution deformation monitoring results for the study area. Summary of the Invention

[0004] To address the shortcomings of traditional SBAS-InSAR technology, which neglects the covariance of small baseline set interferometric phase and temporal deformation when constructing the covariance matrix, and simplifies it to a diagonal matrix in the estimation of temporal deformation and annual average deformation rate, resulting in reduced accuracy of InSAR temporal deformation estimation results, increased statistical error of deformation map, image blurring, and loss of deformation details, this application proposes an InSAR temporal deformation estimation method, electronic device, and readable storage medium.

[0005] The method includes: an InSAR temporal deformation estimation method, which filters and retains high-reliability temporal deformation pixels and high-reliability annual average deformation rate pixels from the temporal deformation data and annual average deformation rate data in the estimation result; wherein, the filtering steps of the high-reliability temporal deformation pixels and the high-reliability annual average deformation rate pixels include: preprocessing the temporal SAR data covering the study area to obtain temporal InSAR data; calculating the corresponding small baseline set temporal unwrapped interferometric phase variance based on the small baseline set temporal unwrapped interferometric phase map in the temporal InSAR data; and calculating the time-series InSAR... Based on the temporal coherence map of the small baseline set in the data, a correlation coefficient calculation model for the temporal interferometric phase of the small baseline set is constructed, and the correlation coefficient between each unwrapped interferometric phase within the small baseline set is calculated. According to the correlation coefficient between each unwrapped interferometric phase within the small baseline set and the variance of the temporal unwrapped interferometric phase of the small baseline set, a temporal decoherence error covariance matrix of the small baseline set is constructed. Based on the temporal decoherence error covariance matrix of the small baseline set, the temporal uncertainty and annual mean deformation rate uncertainty of the temporal InSAR data are calculated. Based on the temporal uncertainty and annual mean deformation rate uncertainty, corresponding temporal uncertainty mask files and annual mean deformation rate mask files are generated. Based on the temporal uncertainty mask files and the annual mean deformation rate uncertainty mask files, the temporal deformation data and annual mean deformation rate data in the temporal InSAR data are masked, and high-reliability temporal deformation pixels and high-reliability annual mean deformation rate pixels are selected and retained according to mask thresholds.

[0006] Furthermore, the process of constructing the solution model for the temporal interferometric phase correlation coefficient of the small baseline set includes: obtaining the temporal coherence of the small baseline set through the temporal coherence map of the small baseline set, and constructing the solution model for the temporal interferometric phase correlation coefficient of the small baseline set based on the temporal coherence of the small baseline set and the long-period coherence of the study area.

[0007] Furthermore, the time series uncertainty is the time series decoherence error covariance matrix. The square root of a diagonal element is expressed as: Timing uncertainty ,in, The time-series incoherence error covariance matrix is... It is a diagonal matrix operator.

[0008] Furthermore, the calculation process for the annual average deformation rate uncertainty includes: Obtain the variance-covariance matrix of the annual average deformation rate , in, c is an offset constant; For SAR observation time, ( ) T For matrix transpose, Find the inverse of a matrix. The time-series incoherence error covariance matrix; according to Obtain the uncertainty of the annual average deformation rate .

[0009] Furthermore, the process of obtaining the time uncertainty mask file and the annual average deformation rate mask file includes setting a time uncertainty mask threshold based on the time uncertainty corresponding to the time deformation data, setting a deformation rate mask threshold based on the annual average deformation rate uncertainty corresponding to the annual average deformation rate data, assigning a value of 1 to pixels in the time uncertainty data and the annual average deformation rate data that are lower than the corresponding mask threshold, and assigning a null value NaN to pixels that are higher than the corresponding mask threshold, so as to generate the time uncertainty mask file and the annual average deformation rate uncertainty mask file.

[0010] Furthermore, the method also includes an atmospheric error correction process, which includes: acquiring external atmospheric auxiliary data corresponding to the time of SAR image acquisition; calculating the atmospheric delay field corresponding to each SAR image based on the external atmospheric auxiliary data; constructing a time-series atmospheric phase error based on the atmospheric delay field; and using the time-series atmospheric phase error to perform atmospheric error correction on the time-series deformation data.

[0011] Furthermore, the process of acquiring time-series deformation data includes: constructing an SBAS-InSAR time-series deformation optimization model based on the augmented Lagrange multiplier alternating direction method, L1 regularization, L2 regularization, and the small baseline set decoherence error covariance matrix, and solving the SBAS-InSAR time-series deformation optimization model to acquire time-series deformation data.

[0012] Furthermore, the process of acquiring the annual average deformation rate data includes: constructing an SBAS-InSAR annual average deformation rate estimation model based on the generalized least squares method and the temporal decoherence error covariance matrix, and solving the SBAS-InSAR annual average deformation rate estimation model to obtain the annual average deformation rate data.

[0013] This application also proposes an electronic device comprising a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the method described in any of the preceding claims.

[0014] This application also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the above-described embodiments.

[0015] The present invention provides an InSAR temporal deformation estimation method based on decoherence covariance constraints. This method fully considers the spatiotemporal correlation of decoherence errors in the small baseline set interferometric phase and temporal deformation. It constructs the small baseline decoherence error covariance matrix and the temporal decoherence error covariance matrix respectively, and introduces them into the temporal deformation estimation process as constraints. This effectively suppresses decoherence noise, improves the resolution and detail representation capability of the deformation map, and ultimately achieves the goal of improving the accuracy of temporal deformation results and annual average deformation rate estimation. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating one embodiment of the method of the present invention; Figure 2 This is a schematic diagram of one embodiment of the electronic device of the present invention. Detailed Implementation

[0017] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0018] It should be understood that the following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0019] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0020] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The illustrations only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0021] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0022] In the technical field to which this invention pertains, SBAS-InSAR is a time-series InSAR data processing technique based on short-spatial-time baseline constraints. Specifically, it extracts temporal deformation information of the Earth's surface by constructing an interferogram network composed of short-spatial-time baseline interferometric pairs. Coherence is used to characterize the correlation between two original SAR images. The correlation coefficient is used to characterize the correlation between the decoherence errors contained in the two interferometric phases. The augmented Lagrange multiplier alternating direction method is an iterative method for solving constrained optimization problems. By alternately updating different variables, it achieves a step-by-step solution to complex problems.

[0023] In this invention, L1 represents the L1 norm, which is used to constrain the sparsity of parameters, thus helping to preserve mutation features and suppress anomalous noise; L2 represents the L2 norm, which is used to constrain the smoothness and stability of the results, thus helping to suppress random noise.

[0024] The external atmospheric auxiliary data involved in this invention include atmospheric reanalysis data, numerical weather prediction data, GACOS tropospheric delay products, or other data that can characterize the atmospheric delay state at the time of SAR image acquisition.

[0025] This invention discloses an InSAR temporal deformation estimation method. To address the shortcomings in the technical problem, this embodiment filters and retains high-reliability temporal deformation pixels from the temporal deformation data and high-reliability annual average deformation rate pixels from the annual average deformation rate data in the estimation results, thereby improving the accuracy of the final results.

[0026] The innovations of this embodiment include the selection steps for high-reliability time-series deformation pixels and the high-reliability annual average deformation rate pixels. Specifically, the scheme includes: preprocessing the time-series SAR data covering the study area to obtain time-series InSAR data. This preprocessing step mainly involves performing SBAS-InSAR preprocessing on the time-series SAR data covering the study area. The InSAR data obtained by this invention includes various types of data; this embodiment mainly utilizes data such as the small baseline set time-series unwrapped interferometric phase map and the small baseline set time-series coherence map. In subsequent steps, the variance of the small baseline set time-series unwrapped interferometric phase is calculated based on the joint probability density function of the small baseline set time-series unwrapped interferometric phase map; a correlation coefficient calculation model for the small baseline set time-series interferometric phase is constructed based on the small baseline set time-series coherence map in the time-series InSAR data, and the correlation coefficient between each unwrapped interferometric phase within the small baseline set is calculated; based on the correlation coefficient between each unwrapped interferometric phase within the small baseline set and the variance of the small baseline set time-series unwrapped interferometric phase, a small baseline set time-series decoherence error covariance matrix is ​​constructed; based on the small baseline set time-series decoherence error covariance matrix, the time-series uncertainty of the time-series InSAR data is calculated. The time-series uncertainty and the annual average deformation rate uncertainty are used to generate time-series uncertainty mask files and annual average deformation rate mask files, respectively. Based on the time-series uncertainty mask files and the annual average deformation rate uncertainty mask files, the time-series deformation data and annual average deformation rate data in the time-series InSAR data are masked. Data that meet the mask requirements in the time-series deformation data and annual average deformation rate data are used as high-reliability time-series deformation pixels and high-reliability annual average deformation rate pixels. High-reliability time-series deformation pixels and high-reliability annual average deformation rate pixels are selected and retained according to the mask threshold.

[0027] Based on the above embodiments, this application further improves upon the above. The process of constructing the solution model for the temporal interferometric phase correlation coefficient of the small baseline set includes: obtaining the temporal coherence of the small baseline set through the temporal coherence map of the small baseline set, and constructing the solution model for the temporal interferometric phase correlation coefficient of the small baseline set based on the temporal coherence of the small baseline set and the long-period coherence of the study area.

[0028] Based on one or more of the above embodiments, this application further improves upon the above, wherein the timing uncertainty is the timing decoherence error covariance matrix. The square root of a diagonal element is expressed as: Timing uncertainty ,in, The time-series incoherence error covariance matrix is... It is a diagonal matrix operator.

[0029] Based on one or more of the above embodiments, this application further improves upon the above, wherein the calculation process for the annual average deformation rate uncertainty includes: Obtain the variance-covariance matrix of the annual average deformation rate , in, c is an offset constant; For SAR observation time, ( ) T For matrix transpose, Find the inverse of a matrix. The time-series incoherence error covariance matrix; according to Obtain the uncertainty of the annual average deformation rate .

[0030] Based on one or more of the above embodiments, this application further improves upon the above. The process of obtaining the time uncertainty mask file and the annual average deformation rate mask file includes: setting a time uncertainty mask threshold according to the time uncertainty corresponding to the time deformation data; setting a deformation rate mask threshold according to the annual average deformation rate uncertainty corresponding to the annual average deformation rate data; assigning a value of 1 to pixels in the time deformation data and annual average deformation rate data that are lower than the corresponding mask threshold; and assigning a value of NaN to pixels that are higher than the corresponding mask threshold, so as to generate the time uncertainty mask file and the annual average deformation rate uncertainty mask file.

[0031] Based on one or more of the above embodiments, this application further improves the method by including an atmospheric error correction process, which includes: acquiring external atmospheric auxiliary data corresponding to the time of SAR image acquisition; calculating the atmospheric delay field corresponding to each SAR image based on the external atmospheric auxiliary data; constructing a time-series atmospheric phase error based on the atmospheric delay field; and using the time-series atmospheric phase error to perform atmospheric error correction on the time-series deformation data.

[0032] Based on one or more of the above embodiments, this application further improves the process of acquiring time-series deformation data by: constructing an SBAS-InSAR time-series deformation optimization model based on the augmented Lagrange multiplier alternating direction method, L1 regularization, L2 regularization and the small baseline set decoherence error covariance matrix, and solving the SBAS-InSAR time-series deformation optimization model to acquire time-series deformation data.

[0033] Based on one or more of the above embodiments, this application further improves the process of acquiring the annual average deformation rate data, which includes: constructing an SBAS-InSAR annual average deformation rate estimation model based on the generalized least squares method and the temporal decoherence error covariance matrix, and solving the SBAS-InSAR annual average deformation rate estimation model to obtain the annual average deformation rate data.

[0034] Combination Figure 1 This embodiment further elaborates on the present application, and the method steps of this embodiment are as follows.

[0035] Step S101: Perform SBAS-InSAR preprocessing based on the temporal SAR data covering the study area to obtain temporal InSAR data, and further obtain the small baseline set temporal unwrapping interferometric phase map and the small baseline set temporal coherence map from the temporal InSAR data. Specifically, this refers to the research area covered. Scene SAR imagery, setting small baseline set interferometric phase connectivity For those satisfying the stated number of connections The SAR image and the first The SAR images are sequentially registered, multi-look, interferometric differential, filtered, de-flattened, de-terrained, and phase unwrapped to generate the first SAR image. A detangling interference phase diagram , and the first The coherence diagram corresponding to the unwrapped interference phase diagram , Coherence diagram It can be represented as: In the formula, and Let be a complex random variable that follows a circularly symmetric Gaussian distribution; The expectation operator is represented. The above unwrapped interferometric phase diagram and corresponding coherence diagram are arranged according to the temporal relationship to form the small baseline set temporal unwrapped interferometric phase diagram and the small baseline set temporal coherence diagram.

[0036] Step S102: Calculate the variance of the time-series unwrapped interferometric phase map of the small baseline set, including calculating the unwrapped interferometric phase variance of the distributed scatterer using the joint probability density function of the InSAR interferometric phase and calculating the unwrapped interferometric phase variance of the permanent scatterer based on the Cramer-Rhodes boundary.

[0037] The process specifically involves: determining the coherence of each unwrapped interferometric phase diagram. Divided into two intervals and .

[0038] When the coherence of the unwrapped interference phase diagram is When the corresponding pixel is treated as a distributed scatterer, the variance is calculated using the InSAR joint probability density function; when the coherence of the unwrapped interferometric phase map is... When the corresponding pixel is used as a permanent scatterer, the variance is calculated using the Cramer-Rhodes boundary.

[0039] Among them, the unwrapped interferometric phase of the distributed scatterer is calculated using the InSAR joint probability density function. The variance process is specifically represented as follows: , In the formula, For the first The unwrapped interferometric phase variance of the unwrapped interferometric phase diagram; This represents the expected value of the interference phase. Let be the joint probability density function of the interference phase, which is expressed as: , In the formula, ; To determine the number of views for multiple views of the unwrapped interferometric phase diagram; This is a modified Bessel function of the third kind; This is a gamma function.

[0040] The phase variance of the unwrapped interference of a permanent scatterer is calculated based on the Cramer-Rhodes boundary, and its expression is as follows: , Step S103: Based on the temporal coherence of the small baseline set and the long-period coherence of the study area, a correlation coefficient calculation model for the temporal interferometric phases of the small baseline set is constructed, and the correlation coefficient calculation model is used to calculate the correlation coefficient between each unwrapped interferometric phase within the small baseline set, specifically: One untangled interference phase and the One untangled interference phase The correlation coefficient calculation model between them is expressed as follows: , In the formula A small positive constant is used to ensure Keep non-negative; This is a function to find the maximum value. subscript Indicates the observation time of the SAR image, satisfying ; The coherence between SAR images at corresponding observation times; To study the long-period coherence of the region.

[0041] Step S104: Based on the obtained variance of the time-series unwrapped interferometric phases of the small baseline set and the correlation coefficients between each unwrapped interferometric phase, construct the covariance matrix of the decoherence error of the small baseline set, specifically as follows: The discocoherence covariance matrix of a small baseline set can be expressed as: , In the formula, This indicates the number of interference phase connections set in step S101. The covariance matrix between the generated small baseline sets.

[0042] Based on the unwrapped interferometric phase variance obtained in step S102 and the correlation coefficient obtained in step S103, the first [value] in the small baseline set covariance matrix is ​​[value]. The covariance matrix of the small baseline set is represented as follows: , A decoherence error propagation model for SBAS-InSAR is further constructed, and the temporal decoherence error covariance matrix is ​​obtained based on the decoherence error propagation model. Specifically, a temporal propagation model of decoherence error for SBAS-InSAR is constructed based on least squares, and its expression is as follows: , In the formula, Design matrix for SBAS-InSAR; The weight matrix of SBAS-InSAR; the weight matrix It is a diagonal matrix or a covariance matrix. When, the weight matrix The error time-series propagation model that characterizes the correlation of decoherence error in small baseline sets, taking into account the correlation of decoherence error in small baseline sets, is expressed as follows: , In the formula, This is the covariance matrix of the temporal discoherence error. To ensure the stability of the model solution and to make... To satisfy the positive definiteness requirement, It can be represented as: In the formula, To calculate the eigenvalues ​​of the matrix; It is an identity matrix.

[0043] Step S105: Based on the time-series decoherence error covariance matrix, calculate the time-series uncertainty and annual average deformation rate uncertainty of the time-series InSAR deformation results. Specifically, the time-series uncertainty is calculated based on the time-series decoherence error covariance matrix obtained in step S104. It is determined that it is the temporal decoherence error covariance matrix. The square root of the diagonal element is expressed as: , Based on the generalized least squares method and the aforementioned time-series decoherence error covariance matrix, the annual average deformation rate uncertainty is calculated, and its expression is as follows: , In the formula, c is an offset constant; For SAR observation time; , The annual average deformation rate uncertainty is given.

[0044] Step S106: Based on the time uncertainty and the annual average deformation rate uncertainty, set the time mask threshold and the deformation rate mask threshold respectively, and generate the time mask file and the annual average deformation rate mask file based on the time mask threshold and the deformation rate mask threshold respectively.

[0045] Specifically, based on the time uncertainty and the annual average deformation rate uncertainty, a time-series mask threshold and a deformation rate mask threshold are set within a certain numerical range. In this embodiment, the range is set to [2 mm, 3 mm], which is determined according to the actual image. Pixels below the corresponding mask threshold are assigned a value of 1, and pixels above the corresponding mask threshold are assigned a null value NaN, generating the corresponding time-series mask file and the annual average deformation rate mask file. By comparing the mask results under different mask thresholds with the original deformation results, the optimal mask threshold and the corresponding mask file are determined.

[0046] Step S107: Based on the augmented Lagrange multiplier alternating direction method, L1 regularization, L2 regularization, and the small baseline set incoherence error covariance matrix, a SBAS-InSAR time-series deformation optimization model is constructed, and the time-series deformation results are obtained by solving the SBAS-InSAR time-series deformation optimization model. Specifically, based on the augmented Lagrange multiplier alternating direction method, L1 and L2 regularization are introduced, and the small baseline set incoherence covariance is used to construct the SBAS-InSAR time-series deformation optimization model, the expression of which is: st In the formula, The unwrapped interferometric phase vector in SBAS-InSAR ; For time-series deformation phase ; Time difference matrix ; For scaled dual variables; Penalty parameters for controlling the strength of constraint enforcement; For L1 regularization; For L2 regularization; and These are the L1 and L2 regularization parameters.

[0047] Based on time difference uncertainty and time difference coefficient The logarithmic form of is used to determine the regularization parameters; where, the th is... The L1 and L2 regularization parameters corresponding to each time difference phase are expressed as follows: , , In the formula, and These are fixed parameters and can be determined through experimental results; For the first The time difference uncertainty corresponding to each time difference phase is expressed as: , Step S108: Based on the external atmospheric auxiliary data corresponding to the acquisition time of each SAR image, estimate the atmospheric delay field corresponding to each SAR image; construct the temporal atmospheric phase error based on the atmospheric delay field, and use the temporal atmospheric phase error to perform atmospheric error correction on the temporal InSAR deformation results, specifically: acquire the temporal deformation results. For each observation time, external atmospheric auxiliary data is used. A reference date and reference pixel are selected for the time-series observations, and the temporal atmospheric delay field of each observation time relative to the reference date and reference pixel is calculated. Specifically, the acquisition time of the first SAR image is used as the reference date. Pixels at each observation time The atmospheric delay at that point is expressed as: , In the formula, In time Pixels The integral absolute single-path tropospheric delay along the satellite's line of sight, in meters; For reference pixels in time Tropospheric delay; This refers to the radar wavelength.

[0048] Based on the temporal deformation results obtained in step S107 and the temporal atmospheric delay results obtained in step S108, the two are differentially processed to eliminate the influence of atmospheric delay on the temporal deformation results, thereby obtaining the deformation results after temporal atmospheric correction.

[0049] Step S109: Based on the generalized least squares method and the temporal decoherence error covariance matrix, construct the SBAS-InSAR annual average deformation rate estimation model, and obtain the annual average deformation rate by solving the SBAS-InSAR annual average deformation rate estimation model. Specifically, based on the temporal decoherence error covariance matrix obtained in step S104, construct the SBAS-InSAR annual average deformation rate estimation model using the generalized least squares method. Its expression is: , In the formula, .

[0050] Step S110: Based on the time-series uncertainty mask file and the annual average deformation rate uncertainty mask file, perform masking processing on the time-series deformation results and the annual average deformation rate results, remove low-reliability pixels, and retain high-reliability time-series deformation pixels and annual average deformation rate pixels. Specifically, according to the time-series deformation results obtained in step S108 and the time-series uncertainty mask file obtained in step S106, perform masking processing on the time-series deformation results, retain deformation pixels with a mask value of 1, and remove deformation pixels with a mask value of NaN to obtain high-reliability time-series deformation pixels; similarly, according to the annual average deformation rate results obtained in step S109 and the annual average deformation rate mask file obtained in step S106, perform masking processing on the annual average deformation rate results, retain deformation rate pixels with a mask value of 1, and remove deformation rate pixels with a mask value of NaN to obtain high-reliability annual average deformation rate pixels.

[0051] This application also proposes an electronic device including a memory and a processor. The memory stores a computer program that, when executed by the processor, performs the methods described in any of the preceding embodiments. An electronic device according to embodiments of this disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), a hard disk, flash memory, etc. The processor may be a central processing unit (CPU) or other forms of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to run the computer-readable instructions stored in the memory, causing the electronic device to perform all or part of the steps of the methods described in the foregoing embodiments of this disclosure. Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0052] like Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. It illustrates a structural schematic diagram suitable for implementing the electronic device in the embodiment of the present disclosure. Figure 2 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein. Figure 2 As shown, electronic devices may include processing units (such as central processing units, graphics processing units, etc.) that can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) or programs loaded from storage devices into random access memory (RAM). The RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus. Typically, the following devices can be connected to the I / O interface: input devices, such as sensors or visual information acquisition devices; output devices, such as displays; storage devices, such as magnetic tapes or hard disks; and communication devices. Communication devices allow electronic devices to exchange data wirelessly or via wired communication with other devices (such as edge computing devices).

[0053] This application also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the above-described embodiments.

[0054] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, all or part of the steps of the methods of embodiments of this disclosure are performed.

[0055] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0056] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0057] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0058] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0059] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0060] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0061] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0062] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0063] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0064] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0065] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for estimating temporal deformation in InSAR, characterized in that, The measurement effect is improved by filtering and retaining high-reliability time-series deformation pixels in time-series deformation data and high-reliability annual average deformation rate pixels in annual average deformation rate data. The screening steps for the high-reliability time-series deformation pixels and the high-reliability annual average deformation rate pixels include: Preprocess the temporal SAR data covering the study area to obtain temporal InSAR data; Calculate the corresponding small baseline set time-series unwrapped interferometric phase variance based on the small baseline set time-series unwrapped interferometric phase map in the time-series InSAR data; Based on the temporal coherence map of the small baseline set in the temporal InSAR data, a model for calculating the correlation coefficient of the temporal interferometric phase of the small baseline set is constructed, and the correlation coefficient between each unwrapped interferometric phase within the small baseline set is calculated. Based on the correlation coefficients between the unwrapped interference phases within the small baseline set and the variance of the time-series unwrapped interference phases of the small baseline set, a time-series decoherence error covariance matrix of the small baseline set is constructed. Based on the time-series decoherence error covariance matrix of the small baseline set, the time-series uncertainty and annual deformation rate uncertainty of the time-series InSAR data are calculated. Based on the aforementioned time uncertainty and annual average deformation rate uncertainty, a time uncertainty mask file and an annual average deformation rate mask file are generated respectively. Based on the time-series uncertainty mask file and the annual average deformation rate uncertainty mask file, the time-series deformation data and annual average deformation rate data in the time-series InSAR data are masked, and high-reliability time-series deformation pixels and high-reliability annual average deformation rate pixels are selected and retained according to the mask threshold.

2. The method according to claim 1, characterized in that, The process of constructing the solution model for the temporal interferometric phase correlation coefficient of the small baseline set includes: obtaining the temporal coherence of the small baseline set through the temporal coherence map of the small baseline set, and constructing the solution model for the temporal interferometric phase correlation coefficient of the small baseline set based on the temporal coherence of the small baseline set and the long-period coherence of the study area.

3. The method according to claim 1, characterized in that, The time uncertainty is the time discoherence error covariance matrix. The square root of a diagonal element is expressed as: Timing uncertainty ,in, The time-series incoherence error covariance matrix is... It is a diagonal matrix operator.

4. The method according to claim 1, characterized in that, The process for calculating the uncertainty of the annual average deformation rate includes: Obtain the variance-covariance matrix of the annual average deformation rate , in, c is an offset constant; For SAR observation time, ( ) T For matrix transpose, Find the inverse of a matrix. The time-series incoherence error covariance matrix; according to Obtain the uncertainty of the annual average deformation rate .

5. The method according to claim 1, characterized in that, The process of obtaining the time-series uncertainty mask file and the annual average deformation rate mask file includes: setting a time-series mask threshold according to the time-series deformation data corresponding to the time-series uncertainty; setting a deformation rate mask threshold according to the annual average deformation rate data corresponding to the annual average deformation rate uncertainty; assigning a value of 1 to pixels in the time-series deformation data and the annual average deformation rate data that are lower than the corresponding mask threshold; and assigning a null value NaN to pixels that are higher than the corresponding mask threshold, so as to generate the time-series uncertainty mask file and the annual average deformation rate uncertainty mask file.

6. The method according to claim 1, characterized in that, The method further includes an atmospheric error correction process, which includes: acquiring external atmospheric auxiliary data corresponding to the time of SAR image acquisition; calculating the atmospheric delay field corresponding to each SAR image based on the external atmospheric auxiliary data; constructing a time-series atmospheric phase error based on the atmospheric delay field; and using the time-series atmospheric phase error to perform atmospheric error correction on the time-series deformation data.

7. The method according to claim 1, characterized in that, The process of acquiring time-series deformation data includes: constructing an SBAS-InSAR time-series deformation optimization model based on the augmented Lagrange multiplier alternating direction method, L1 regularization, L2 regularization, and the small baseline set decoherence error covariance matrix, and solving the SBAS-InSAR time-series deformation optimization model to acquire time-series deformation data.

8. The method according to claim 1, characterized in that, The process of acquiring the annual average deformation rate data includes: constructing an SBAS-InSAR annual average deformation rate estimation model based on the generalized least squares method and the temporal decoherence error covariance matrix, and solving the SBAS-InSAR annual average deformation rate estimation model to obtain the annual average deformation rate data.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method according to any one of claims 1-8.

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