Multi-platform InSAR covariance constrained slope three-dimensional deformation monitoring method

Abstract

The invention provides a slope three-dimensional deformation monitoring method based on multi-platform InSAR covariance constraint. The slope three-dimensional deformation monitoring method based on multi-platform InSAR covariance constraint comprises the steps of determining a synchronization time window of multi-platform InSAR processing; obtaining an InSAR time sequence interferogram of each platform; errors existing in the InSAR time sequence interferograms of all the platforms are corrected; calculating the variance of the corrected InSAR time sequence interferogram of each platform; calculating a coherence coefficient between the interferograms; calculating the average deformation rate uncertainty and the accumulated deformation uncertainty of the LOS direction in the synchronization time window of each platform; calculating an average deformation rate and an accumulated deformation in the LOS direction in the synchronization time window of each platform; geocoding and resampling are carried out on a multi-platform InSAR processing result; a multi-platform InSAR slope three-dimensional deformation calculation model is established; a multi-platform InSAR joint variance-covariance matrix is constructed; constructing a three-dimensional direction error transfer model; and high-precision slope three-dimensional deformation is extracted based on the established improved multi-platform InSAR slope three-dimensional deformation calculation model. The purpose of improving the precision of a slope three-dimensional deformation monitoring result is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of slope monitoring, and in particular relates to a method for three-dimensional deformation monitoring of slopes with covariance constraints of multi-platform InSAR. Background Art

[0002] Geological disasters such as landslides and collapses caused by slope instability pose a serious threat to people's lives, property and infrastructure safety. Real-time, long-term and three-dimensional monitoring of slope deformation is a key link in disaster prevention and mitigation, and is of great significance for protecting people's lives and property, maintaining infrastructure safety and promoting economic development.

[0003] Interferometric Synthetic Aperture Radar (InSAR) technology can perform long-distance, large-scale, high-resolution and high-precision radar line-of-sight (LOS) deformation measurement on slopes, and the monitoring accuracy can reach the millimeter level at most. Currently, InSAR technology can be applied on three platforms: spaceborne SAR, airborne SAR and ground-based SAR. Combining the LOS deformations of different observation geometries of multiple platforms can achieve three-dimensional deformation monitoring of slopes. With the continuous development of multi-platform InSAR technology and the increase in the number of interferometric SAR satellites, the quality, quantity and short revisit period of multi-platform InSAR data can be fully guaranteed, and combining multi-platform InSAR can meet the high-frequency and near-real-time dynamic monitoring of slope three-dimensional deformation.

[0004] However, it is difficult for InSAR processing to completely remove multi-source random errors such as atmospheric errors and decoherence errors, resulting in different degrees of multi-source residuals in the LOS direction in both time and space for the observations of each platform. And the multi-source residuals in the LOS direction will accumulate errors in the time-space domain, and lead to a reduction in the monitoring accuracy of slope three-dimensional deformation through error transfer. Specifically, there are different degrees of missed detections and false detections in slope deformation monitoring, which greatly reduces the reliability of multi-platform InSAR joint monitoring of slope deformation. Therefore, it is of great practical significance to consider the error transfer of multi-platform InSAR to evaluate the uncertainty of the slope three-dimensional deformation monitoring results, and then improve the monitoring accuracy of slope three-dimensional deformation to ensure the accuracy and timeliness of disaster warnings. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention provides a method for three-dimensional deformation monitoring of slopes with covariance constraints of multi-platform InSAR, which at least partially solves the problem of low accuracy of the slope three-dimensional deformation monitoring results existing in the prior art.

[0006] The embodiments of the present disclosure provide a method for three-dimensional deformation monitoring of slopes with covariance constraints of multi-platform InSAR, including:

[0007] Determine the synchronous time window for multi-platform InSAR processing according to the observation period of multi-track spaceborne SAR data covering the slope;

[0008] Perform SBAS-InSAR preprocessing on the available SAR data within the synchronous time windows of spaceborne SAR, airborne SAR, and ground-based SAR respectively to obtain the InSAR time series interferograms of each platform;

[0009] Use the multi-source error correction method to correct the errors existing in the InSAR time series interferograms of each platform to obtain the corrected InSAR time series interferograms of each platform;

[0010] Calculate the variances of the corrected InSAR time series interferograms of each platform using the probability density function and Cramer-Rao bound of the InSAR interference phase;

[0011] Calculate the coherence coefficient between the observation times of different SAR images based on the observation time of the interferogram, and calculate the coherence coefficient between the interferograms according to the coherence coefficient between the observation times of different SAR images;

[0012] Construct a time series variance-covariance matrix based on the variances of the corrected InSAR time series interferograms of each platform and the coherence coefficient between the interferograms obtained, and then construct a time series InSAR LOS-direction error transfer model for each platform. Calculate the average deformation rate uncertainty and cumulative deformation uncertainty in the LOS direction within the synchronous time window of each platform based on the time series InSAR LOS-direction error transfer model;

[0013] Obtain an improved SBAS-InSAR time series deformation solution model based on the time series variance-covariance matrix, and calculate the average deformation rate and cumulative deformation in the LOS direction within the synchronous time window of each platform according to the improved SBAS-InSAR time series deformation solution model;

[0014] Perform geocoding and resampling on the multi-platform InSA processing results to obtain the multi-platform InSAR processing results with the same spatial resolution and coordinate system;

[0015] Establish a system of equations for the relationship between multi-platform LOS-direction deformation and three-dimensional deformation based on the imaging geometry of spaceborne SAR, airborne SAR, and ground-based SAR and the multi-platform InSAR processing results, and establish a multi-platform InSAR slope three-dimensional deformation solution model based on the system of equations for the relationship between multi-platform LOS-direction deformation and three-dimensional deformation;

[0016] Construct a multi-platform InSAR joint variance-covariance matrix based on the average deformation rate uncertainty and cumulative deformation uncertainty in the LOS direction within the synchronous time window of each platform;

[0017] Construct a three-dimensional direction error transfer model based on the relationship equations of LOS deformation and three-dimensional deformation and the joint variance-covariance matrix of multi-platform InSAR;

[0018] Improve the three-dimensional deformation solution model of multi-platform InSAR for slopes based on the joint variance-covariance matrix of multi-platform InSAR and the three-dimensional direction error transfer model. Input the average deformation rate and cumulative deformation in the LOS direction within the synchronous time window of each platform into the improved three-dimensional deformation solution model of multi-platform InSAR for slopes, so as to extract the three-dimensional deformation of the slope.

[0019] The method for monitoring the three-dimensional deformation of slopes with covariance constraint of multi-platform InSAR provided by the present invention effectively improves the accuracy of the three-dimensional deformation solution of multi-platform InSAR for slopes by suppressing the random errors from the LOS direction of multi-platforms to the three-dimensional direction, thereby achieving the purpose of improving the accuracy of the monitoring results of the three-dimensional deformation of slopes.

[0020] Specifically, by selecting the synchronous time window of multi-platform InSAR, establish the time-series variance-covariance matrix of each platform using the variance and coherence coefficient of the time-series InSAR interferograms of each platform within the synchronous time window. Improve the SBAS-InSAR algorithm based on the time-series variance-covariance matrix to achieve the suppression of the time-series LOS direction residuals of multi-platforms and improve the accuracy of LOS direction deformation monitoring; further, jointly construct the joint variance-covariance matrix of multi-platform InSAR with the LOS direction uncertainty of multi-platform InSAR, and improve the three-dimensional deformation solution method of slopes based on the joint variance-covariance matrix. Description of the Drawings

[0021] By describing the exemplary embodiments of the present disclosure in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present disclosure will become more obvious. Among them, in the exemplary embodiments of the present disclosure, the same reference numerals generally represent the same components.

[0022] Figure 1 It is a flowchart of the method for monitoring the three-dimensional deformation of slopes with covariance constraint of multi-platform InSAR provided by the embodiments of the present disclosure;

[0023] Figure 2 It is the observation geometry diagram of multi-platform InSAR provided by the embodiments of the present disclosure;

[0024] Figure 3 It is the principle block diagram of the electronic device provided by the embodiments of the present disclosure. Detailed Embodiments

[0025] The embodiments of the present disclosure will be described in detail below in conjunction with the drawings.

[0026] It should be clear that the following uses specific specific examples to illustrate the implementation manners of the present disclosure, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific implementation manners, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope protected by the present disclosure.

[0027] It should be noted that the following describes various aspects of the embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present disclosure, those skilled in the art should 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 described herein can be used to implement the device and / or practice the method. In addition, this device and / or this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0028] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present disclosure in a schematic manner. The diagrams only show the components related to the present disclosure, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in its actual implementation can be an arbitrary change, and the component layout type may also be more complex.

[0029] In addition, in the following description, specific details are provided 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.

[0030] SBAS-InSAR is a time-series InSAR data processing technology, specifically for extracting time-series deformation using an interferogram network composed of short spatio-temporal baselines;

[0031] The coherence coefficient of SAR images is the correlation between two original SAR images;

[0032] The coherence coefficient of interferograms is the correlation between two interferograms;

[0033] The atmospheric numerical model atmospheric products include ECMWF, MERRA2, and GACOS;

[0034] Spatial-temporal phase closure refers to detecting the unwrapping error by checking whether the sum of the triangular phase network formed by the phase closure loop is zero. The annual average deformation rate uncertainty and the cumulative deformation uncertainty are the square roots of the annual average deformation rate variance and the cumulative deformation variance respectively.

[0035] For the sake of easy understanding, as Figure 1 shown, this embodiment discloses a method for three-dimensional deformation monitoring of slopes with multi-platform InSAR covariance constraint, including:

[0036] Step S101: Determine the synchronous time window for multi-platform InSAR processing according to the observation periods of multi-orbit spaceborne SAR data covering the slope. Specifically, obtain multi-orbit spaceborne SAR data covering the slope area. The multi-orbit spaceborne SAR data includes SAR satellite data of different ascending / descending orbits and different bands. Determine the synchronous time window of the spaceborne SAR data of each orbit, where the starting time deviation of the synchronous time window is less than or equal to 1 day; determine the airborne SAR and ground-based SAR observation data according to this synchronous time window.

[0037] Step S102: Perform SBAS-InSAR preprocessing on the available SAR data within the synchronous time windows of spaceborne SAR, airborne SAR, and ground-based SAR respectively to obtain the time-series interferograms of each platform. Specifically, assume that there are N SAR images within the synchronous time window of one platform. The SBAS-InSAR time-series deformation solution model can be expressed as:

[0038]

[0039] where is the time-series LOS-direction deformation phase within the estimated synchronous time window, is the cumulative deformation phase at the i-th observation time, are M time-series interferograms obtained within the synchronous time window of each platform. The interferograms can be obtained by performing interference processing on each SAR image in the N SAR images with the adjacent t SAR images. W is the diagonal weight matrix of the M time-series interferograms, and A is the design matrix of the small baseline set interferogram connection network of M×N. When t = 2, the design matrix A can be expressed as:

[0040]

[0041] Step S103: Use the multi-source error correction method to correct the errors existing in the InSAR time-series interferograms of each platform, and obtain the corrected InSAR time-series interferograms of each platform. Specifically, use the existing multi-source error correction method to correct the atmospheric error, unwrapping error, and DEM error existing in the time-series interferogram. Specifically, the atmospheric error can be removed by the atmospheric product of the atmospheric numerical model, the unwrapping error can be detected and optimized by constructing a spatio-temporal phase closure loop, and the DEM error can be estimated and filtered by time-series InSAR.

[0042] Step S104: Calculate the variance of the corrected InSAR time-series interferograms of each platform by using the probability density function of the InSAR interference phase and the Cramer-Rao bound. Specifically: Divide the coherence ρ of each interferogram into two intervals: 0 < ρ ≤ 0.9 and 0.9 < ρ ≤ 1. When the coherence of the interferogram is 0 < ρ ≤ 0.9, calculate the variance by using the probability density function. When the coherence of the interferogram is 0.9 < ρ ≤ 1, calculate the variance by using the Cramer-Rao bound. Among them, the calculation of the variance based on the probability density function can be expressed as:

[0043]

[0044] In the formula, σ 2 is the variance of the interferogram, is the interference phase, is the expectation of the interference phase, is the probability density function of the interference phase.

[0045] The calculation of the variance based on the Cramer-Rao bound can be expressed as:

[0046]

[0047] In the formula, ρ is the coherence coefficient between SAR images, and R is the number of looks of the multi-looked interferogram.

[0048] Step S105: Calculate the coherence coefficient between the observation times of different SAR images based on the observation time of the interferogram, and calculate the coherence coefficient between interferograms according to the coherence coefficient between the observation times of different SAR images. Specifically: Calculate the coherence coefficient between the observation times of different SAR images based on the observation time of the interferogram, and calculate the coherence coefficient between interferograms according to this coefficient. Specifically, the formula for the coherence coefficient between interferograms can be expressed as

[0049]

[0050] where the subscripts e, f, g, t of the interferograms and represent the observation times of the SAR images, and ρ eg 、ρ ft 、ρ et 、ρfg , ρ ef and ρ gt is the coherence coefficient between SAR images corresponding to the observation time.

[0051] Step S106: Construct a temporal variance-covariance matrix based on the variances of the corrected InSAR temporal interferograms of each platform and the coherence coefficients between the interferograms, and then construct a temporal InSAR LOS-direction error transfer model for each platform. Calculate the average deformation rate uncertainty and cumulative deformation uncertainty in the LOS direction within the synchronization time window of each platform based on the temporal InSAR LOS-direction error transfer model;

[0052] Construct a temporal variance-covariance matrix based on the obtained interferogram variances and coherence coefficients, and then construct a temporal InSAR LOS-direction error transfer model for each platform, and calculate the average deformation rate uncertainty and cumulative deformation uncertainty in the LOS direction within the synchronization time window of each platform. Specifically, the temporal variance-covariance matrix of one of the platforms can be expressed as:

[0053]

[0054] κ i,j represents the coherence coefficient between the i-th and j-th interferograms. Construct a temporal InSAR LOS-direction error transfer model, and its formula can be expressed as:

[0055] ε = k·[A -1 Y(A -1 ) T ,

[0056] In the formula is the phase-to-deformation coefficient, λ is the wavelength, the diagonal is the deformation variances corresponding to N observation times, where ε i is the cumulative deformation uncertainty at the i-th observation time. Perform a linear fit on the variances corresponding to N observation times to obtain the average deformation rate uncertainty where the fitting formula can be expressed as:

[0057]

[0058] where t i is the i-th observation time, i = 1, …, N, and c is a fixed constant.

[0059] Step S107: Obtain an improved SBAS-InSAR time-series deformation solution model based on the time-series variance-covariance matrix, and calculate the average deformation rate and cumulative deformation in the LOS direction within the synchronous time window of each platform according to this improved SBAS-InSAR time-series deformation solution model. Specifically: Based on the time-series variance-covariance matrix calculated in step S106 and the solution model given in step S102, obtain the improved SBAS-InSAR time-series deformation solution model. The formula of the improved SBAS-InSAR time-series deformation solution model can be expressed as:

[0060]

[0061] where τ is the time-series LOS direction regularization parameter, and H is the time-series LOS direction regularization matrix. Calculate the time-series interference phase according to the improved SBAS-InSAR time-series deformation solution model, multiply by the phase-to-deformation coefficient k to obtain the deformation corresponding to N observation times. The cumulative deformation at the Nth observation time is Perform linear fitting on the deformations corresponding to N observation times to obtain the average deformation rate v. The fitting formula can be expressed as

[0062]

[0063] where i is the i-th observation, i = 1, …, N, and b is a fixed constant.

[0064] According to the above steps S102 - S107, the cumulative deformation uncertainty, average deformation rate uncertainty, cumulative deformation, and average deformation rate of the spaceborne SAR, airborne SAR, and ground-based SAR platforms within the synchronous time window can be obtained.

[0065] Step S108: Perform geocoding and resampling on the multi-platform InSAR processing results to obtain multi-platform InSAR processing results with the same spatial resolution and coordinate system. Specifically: Establish the mapping relationship between the coordinates of the multi-platform SAR observation results and the geodetic coordinates respectively, convert the multi-platform SAR observation results to geodetic coordinates, and perform common area cropping on the geocoded multi-platform SAR observation results. Resample the observation results of the airborne SAR and spaceborne SAR respectively using the nearest neighbor method according to the spatial resolution of the ground-based SAR observation results to make the spatial resolution and geographical coordinates of the multi-platform InSAR observation results consistent.

[0066] Step S109: Based on the imaging geometric relationships of spaceborne SAR, airborne SAR, and ground-based SAR and the processing results of multi-platform InSAR, establish a system of equations for the relationship between multi-platform LOS-direction deformation and three-dimensional deformation. Based on the system of equations for the relationship between multi-platform LOS-direction deformation and three-dimensional deformation, establish a multi-platform InSAR three-dimensional deformation resolution model for slopes. Specifically, according to the imaging geometric relationships of multi-platform SAR as Figure 2 shown, establish a system of equations for the relationship between multi-platform LOS-direction deformation and three-dimensional deformation. The matrix expression is:

[0067]

[0068] In the formula, l i represents the LOS-direction deformation of the i-th orbit, i = 1, …, P, where P is the number of LOS-direction deformations obtained by multi-platform SAR, and its magnitude is related to the number of orbits of spaceborne SAR, airborne SAR, and ground-based SAR covering the slope.

[0069] θ i is the incident angle of multi-platform SAR, α i is the clockwise angle between the true north direction and the running direction of the multi-platform SAR orbit, that is, the heading angle. The incident angle and heading angle of multi-platform SAR are as Figure 2 shown; du, de, and dn are the vertical, east-west, and north-south three-dimensional deformations that occur on the slope, respectively.

[0070] The multi-platform InSAR three-dimensional deformation resolution model for slopes can be expressed as:

[0071] D = (B T PB) -1 B T PL,

[0072] In the formula where l i can be the cumulative deformation or average deformation rate of the LOS direction of multiple platforms, and P is the weight matrix of the LOS-direction deformation of multiple platforms.

[0073] D is the three-dimensional deformation, expressed as:

[0074] Step S110: Based on the uncertainty of the average deformation rate and the cumulative deformation of the LOS direction within the synchronization time window of each platform, construct a multi-platform InSAR joint variance-covariance matrix. Specifically, construct a multi-platform InSAR joint variance-covariance matrix according to the uncertainty of the average deformation rate of multiple platforms obtained in Step S106. Its formula can be expressed as:

[0075]

[0076] Construct a multi-platform joint variance-covariance matrix according to the cumulative deformation uncertainty, and its formula can be expressed as:

[0077]

[0078] Step S111: Based on the relationship equations between the LOS-direction deformation and the three-dimensional deformation of multiple platforms and the multi-platform InSAR joint variance-covariance matrix, construct a three-dimensional direction error transfer model, and calculate the three-dimensional direction cumulative deformation uncertainty and the average rate uncertainty. Specifically, the formula of the error transfer model can be expressed as:

[0079] ω = B -1 C(B -1 ) T ,

[0080] In the formula The diagonal elements are the cumulative deformation variances or average rate variances in the vertical, east-west, and north-south directions.

[0081] Input the and C ε of step S110 into this error transfer model to calculate the three-dimensional direction cumulative deformation uncertainty and the average rate uncertainty respectively.

[0082] Step S112: Improve the multi-platform InSAR three-dimensional deformation solution model for slopes based on the multi-platform InSAR joint variance-covariance matrix and the three-dimensional direction error transfer model. Input the average deformation rate and cumulative deformation in the LOS direction within the synchronous time window of each platform into the improved multi-platform InSAR three-dimensional deformation solution model for slopes, so as to extract the high-precision three-dimensional deformation of the slope. Specifically, the improved multi-platform InSAR three-dimensional deformation solution model for slopes can be expressed as:

[0083]

[0084] In the formula, μ is the regularization parameter for three-dimensional deformation solution, U is the regularization matrix for three-dimensional deformation solution, is the estimated three-dimensional deformation of the slope.

[0085] The electronic device according to an embodiment of the present disclosure includes a memory and a processor. The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0086] The processor may be a central processing unit (CPU) or other forms of processing units 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 the present disclosure, the processor is used to run the computer-readable instructions stored in the memory, so that the electronic device executes all or part of the steps of the image deformation method according to the foregoing embodiments of the present disclosure.

[0087] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user experience effects, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included in the protection scope of the present disclosure.

[0088] As Figure 3 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. It shows a schematic structural diagram of an electronic device suitable for implementing the electronic device in the embodiments of the present disclosure. Figure 3 The illustrated electronic device is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0089] As Figure 3 As shown, the electronic device may include a processing device (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM, and RAM are connected to each other through a bus. The input / output (I / O) interface is also connected to the bus.

[0090] Generally, the following devices may be connected to the I / O interface: an input device including, for example, a sensor or a visual information acquisition device; an output device including, for example, a display screen; a storage device including, for example, a magnetic tape, a hard disk, etc.; and a communication device. The communication device may allow the electronic device to communicate with other devices (such as edge computing devices) wirelessly or wiredly to exchange data. Although Figure 3 the illustrated electronic device has various devices, it should be understood that it is not required to implement or have all the illustrated devices. Instead, more or fewer devices may be implemented or had.

[0091] In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts. In such an embodiment, 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 method for image deformation according to the embodiments of the present disclosure are performed.

[0092] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated herein.

[0093] A computer-readable storage medium according to an embodiment of the present disclosure stores non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are run by a processor, all or part of the steps of the method for image deformation according to the foregoing embodiments of the present disclosure are performed.

[0094] The above-mentioned computer-readable storage medium includes, but is not limited to: optical storage media (such as CD-ROMs and DVDs), magneto-optical storage media (such as MOs), magnetic storage media (such as magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (such as memory cards), and media with built-in ROMs (such as ROM cartridges).

[0095] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated herein.

[0096] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present disclosure. In addition, the above-mentioned specific details are only for illustrative and facilitating understanding purposes, and not for limitation. The above details do not limit the present disclosure to necessarily adopt the above specific details for implementation.

[0097] In this disclosure, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any way. Words such as "including", "comprising", "having", etc. are open-ended terms, meaning "including but not limited to", and can be used interchangeably with each other. The words "or" and "and" used herein refer to the term "and / or", and can be used interchangeably with it, unless the context clearly indicates otherwise. The phrase "such as" used herein refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0098] In addition, as used herein, "or" in the listing of items starting with "at least one" indicates a disjunctive listing, so that for example, the listing of "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). Further, the term "exemplary" does not mean that the examples described are preferred or better than other examples.

[0099] It should also be noted that in the systems and methods of this disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of this disclosure.

[0100] Various changes, substitutions, and alterations to the technologies described herein can be made without departing from the teachings defined by the appended claims. In addition, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and acts described above. Current or later-developed processes, machines, manufactures, compositions of events, means, methods, or acts that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or acts within their scope.

[0101] 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 the broadest scope consistent with the principles and novel features disclosed herein.

[0102] The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the present disclosure to the form disclosed herein. Although several example aspects and embodiments have been discussed above, those skilled in the art will recognize some variations, modifications, alterations, additions, and subcombinations thereof.

Claims

1. A multi-platform InSAR covariance-constrained slope three-dimensional deformation monitoring method, characterized in that: include: The synchronization time window of multi-platform InSAR processing is determined based on the observation period of multi-orbit spaceborne SAR data covering the slope; SBAS-InSAR preprocessing is performed on the available SAR data within the synchronization time window of spaceborne SAR, airborne SAR and ground-based SAR to obtain the InSAR time-series interferograms of each platform; The errors in the InSAR time-series interferograms of each platform are corrected using a multi-source error correction method to obtain the corrected InSAR time-series interferograms of each platform. The probability density function of InSAR interferometric phase and Cramer-Rao limit are used to calculate the variance of the corrected InSAR time series interferograms of each platform. The coherence coefficients between different SAR image observation times are calculated based on the observation time of the interference pattern, and the coherence coefficients between the interference patterns are calculated based on the coherence coefficients between different SAR image observation times; The time series variance-covariance matrix is ​​constructed based on the obtained corrected variance of each platform's InSAR time series interferogram and the coherence coefficient between the interferograms, and then the time series InSAR LOS error transfer model of each platform is constructed. Based on the time series InSAR LOS error transfer model, the average deformation rate uncertainty and cumulative deformation uncertainty of the LOS direction in the synchronization time window of each platform are calculated. An improved SBAS-InSAR temporal deformation solution model is obtained based on the temporal variance-covariance matrix, and the average deformation rate and cumulative deformation in the LOS direction within the synchronization time window of each platform are calculated according to the improved SBAS-InSAR temporal deformation solution model. Geocode and resample the multi-platform InSAR processing results to obtain multi-platform InSAR processing results with the same spatial resolution and coordinate system; According to the geometric relationship of satellite-borne SAR, airborne SAR and ground-based SAR imaging and the processing results of multi-platform InSAR, the equation group of the relationship between multi-platform LOS deformation and three-dimensional deformation is established. Based on the equation group of the relationship between multi-platform LOS deformation and three-dimensional deformation, a multi-platform InSAR slope three-dimensional deformation solution model is established. The multi-platform InSAR joint variance-covariance matrix is ​​constructed based on the average deformation rate uncertainty and cumulative deformation uncertainty in the LOS direction within the synchronization time window of each platform. A 3D directional error transfer model is constructed based on the multi-platform LOS deformation and 3D deformation relationship equations and the multi-platform InSAR joint variance-covariance matrix. The multi-platform InSAR three-dimensional deformation solution model of slope is improved based on the multi-platform InSAR joint variance-covariance matrix and three-dimensional directional error transfer model. The average deformation rate and cumulative deformation in the LOS direction within the synchronous time window of each platform are input into the improved multi-platform InSAR three-dimensional deformation solution model of slope, so as to extract the three-dimensional deformation of the slope.

2. The multi-platform InSAR covariance-constrained slope three-dimensional deformation monitoring method according to claim 1 is characterized in that: The SBAS-InSAR preprocessing includes performing SBAS-InSAR preprocessing based on a SBAS-InSAR time series deformation solution model, and the SBAS-InSAR time series deformation solution model is expressed as: in is the estimated LOS deformation phase of the timing within the synchronization time window, is the cumulative deformation phase at the i-th observation time, are the M timing interference graphs obtained in the synchronization time window of each platform, W is the diagonal weight matrix of the M timing interference graphs, and A is the M×N small baseline set interference graph connection network design matrix.

3. The multi-platform InSAR covariance-constrained slope three-dimensional deformation monitoring method according to claim 2 is characterized in that: The method of using a multi-source error correction method to correct errors existing in the InSAR time series interferograms of each platform includes: Correct the atmospheric error, unwrapping error and DEM error in the time series interferogram. Atmospheric errors are removed through atmospheric products of atmospheric numerical models, unwrapping errors are detected and optimized by constructing a spatiotemporal phase closed loop, and DEM errors are estimated and filtered out through time-series InSAR.

4. The multi-platform InSAR covariance-constrained slope three-dimensional deformation monitoring method according to claim 3 is characterized in that: The method of calculating the corrected variance of the InSAR time series interferograms of each platform by using the probability density function of the InSAR interferometric phase and the Cramer-Rao limit includes: The coherence ρ of each InSAR time series interferogram is divided into two intervals: 0<ρ≤0.9 and 0.9<ρ≤1. When the coherence of the interferogram is 0<ρ≤0.9, the variance is calculated using the probability density function. When the coherence of the interferogram is 0.9<ρ≤1, the variance is calculated using the Cramer-Rao limit.

5. The multi-platform InSAR covariance-constrained slope three-dimensional deformation monitoring method according to claim 4 is characterized in that: The variance calculation based on the probability density function can be expressed as: In the formula, σ 2 is the interference pattern variance, is the interference phase, is the expected interference phase, is the probability density function of the interference phase; The variance calculated based on the Cramer-Rao limit can be expressed as: In the formula, σ 2 is the variance of the interferogram, ρ is the coherence coefficient between SAR images, and R is the number of views of the interferogram.

6. The multi-platform InSAR covariance-constrained slope three-dimensional deformation monitoring method according to claim 5 is characterized in that: The calculation formula for calculating the coherence coefficient between the interference patterns according to the coherence coefficient between different SAR image observation times is expressed as: The interference pattern and The subscripts e, f, g, and t represent the observation time of the SAR image. eg , ft , et , fg , ef and ρ gt is the coherence coefficient between SAR images at the corresponding observation time.

7. The multi-platform InSAR covariance-constrained slope three-dimensional deformation monitoring method according to claim 6 is characterized in that: The time series variance-covariance matrix can be expressed as: κ i,j represents the coherence coefficient between the i-th and j-th interference patterns, σ is the interference pattern variance; The time series InSAR LOS error transfer model formula is expressed as: ε=k[A -1 Y(A -1 ) T ], In the formula is the phase transformation coefficient, λ is the wavelength; The improved SBAS-InSAR temporal deformation solution model formula is expressed as: Where τ is the time series LOS regularization parameter, and H is the time series LOS regularization matrix.

8. The multi-platform InSAR covariance-constrained slope three-dimensional deformation monitoring method according to claim 7 is characterized in that: The matrix expression of the equation group of the relationship between the LOS deformation and the three-dimensional deformation of the multi-platform is: In the formula, l i represents the LOS deformation of the ith orbit, i = 1, ..., P, P is the number of LOS deformations obtained by the multi-platform SAR, and its size is related to the number of orbits of the satellite-borne SAR, airborne SAR, and ground-based SAR covering the slope, θ i is the multi-platform SAR incident angle, α i The clockwise angle between the true north and the multi-platform SAR track running direction is the heading angle, du, de, dn are the vertical, east-west and north-south three-dimensional deformations of the slope respectively; The multi-platform InSAR slope three-dimensional deformation solution model formula is expressed as: D = (B T PB) -1 B T PL, In the formula Among them l i is the LOS cumulative deformation or average deformation rate of multiple platforms, P is the LOS deformation weight matrix of multiple platforms, and D is the three-dimensional deformation.

9. The multi-platform InSAR covariance-constrained slope three-dimensional deformation monitoring method according to claim 8, characterized in that: The three-dimensional directional error transfer model formula is expressed as: ω=B -1 C(B -1 ) T , In the formula is the multi-platform InSAR joint variance-covariance matrix constructed based on the multi-platform average deformation rate uncertainty, C ε is the multi-platform joint variance-covariance matrix constructed based on the cumulative deformation uncertainty.

10. The multi-platform InSAR covariance-constrained slope three-dimensional deformation monitoring method according to claim 9, characterized in that: The improved multi-platform InSAR slope three-dimensional deformation solution model formula is expressed as: Where μ is the regularization parameter for three-dimensional deformation solution, U is the regularization matrix for three-dimensional deformation solution, is the estimated three-dimensional deformation of the slope.

Images