Method and device for optimizing InSAR phase unwrapping network based on redundant observations

Abstract

The application provides an optimization method and device for an InSAR phase unwrapping network based on redundant observations. The method obtains a target number of short baseline interferograms, the wrapped phase of the corresponding interferogram, and candidate points in the corresponding interferogram based on SLC images observed at different times in a region to be monitored, obtains the arc segment phase difference corresponding to each arc segment formed by connecting two candidate points and a spatial network; based on a preset screening condition and the arc segment phase difference of each arc segment in the spatial network, each arc segment is screened to obtain a screened phase unwrapping network; the screened phase unwrapping network is optimized to obtain an optimized phase unwrapping network, then a linear programming algorithm based on edge constraints is used to obtain the unwrapped phase of each arc segment in the short baseline interferogram, and through unwrapping processing, the current phase unwrapping network is obtained. The method can simultaneously consider and control the phase unwrapping precision and efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of InSAR data processing, in particular to an optimization method and device for an InSAR phase unwrapping network based on redundant observations. BACKGROUND

[0002] At present, InSAR technology is one of the best methods for large-scale and high-precision ground deformation monitoring, and has become an important technical means in modern earth science research. Rapidly obtaining correct unwrapping phase is the basis for subsequent deformation parameter inversion and is one of the most critical steps in InSAR data processing.

[0003] In terms of time-series InSAR phase unwrapping, domestic and foreign researches use Delaunay triangular networks or construct redundant networks to carry out phase unwrapping.

[0004] How to balance the accuracy and efficiency to carry out quality controllable phase unwrapping, there is no feasible scheme at present, especially with the shortening of the revisit period of SAR data and the arrival of the engineering era, this demand is more urgent. Therefore, the existing InSAR time-series phase unwrapping cannot meet the needs of monitoring with consideration of accuracy and efficiency. SUMMARY

[0005] The purpose of the embodiments of the present application is to provide an optimization method and device for an InSAR phase unwrapping network based on redundant observations, to solve the above-mentioned problems existing in the prior art, and to improve the phase unwrapping accuracy and efficiency of the InSAR phase unwrapping network.

[0006] In a first aspect, an optimization method for an InSAR phase unwrapping network based on redundant observations is provided, which can include:

[0007] Based on the SLC images observed at different times in the region to be monitored, a target number of short baseline interferograms, the wrapped phase of the corresponding interferogram, and the candidate points in the corresponding interferogram are obtained;

[0008] According to a preset connection condition, any two candidate points in any short baseline interferogram are connected to obtain the arc phase difference corresponding to the arc segment formed by connecting any two candidate points and a spatial network; the spatial network includes the target number of layers of short baseline interferograms and the arc segments corresponding to each two candidate points in each layer of short baseline interferograms;

[0009] Based on a preset screening condition and the arc phase difference of each arc segment in the spatial network, each arc segment is screened to obtain a phase unwrapping network after screening;

[0010] optimizing the screened phase unwrapping network based on the number of redundant observations of each candidate point in any layer short baseline interferogram, to obtain an optimized phase unwrapping network;

[0011] for any short baseline interferogram in the optimized phase unwrapping network, using a linear programming algorithm based on edge constraints to obtain the unwrapped phase of each arc segment in the short baseline interferogram;

[0012] based on each unwrapped phase in the short baseline interferogram, unwrapping the corresponding wrapped phase to obtain the current phase unwrapping network.

[0013] In an optional implementation, based on the SLC images observed at different times in the monitoring area, a target number of short baseline interferograms, the wrapped phase of the corresponding interferogram, and the candidate points in the corresponding interferogram are obtained, including:

[0014] According to the preset spatio-temporal baseline threshold, the SLC images observed at different times in the monitoring area are processed to generate a target number of short baseline interferograms and the wrapped phase of the corresponding interferogram;

[0015] For any short baseline interferogram, according to the amplitude dispersion index method, the pixel points in the SLC image are selected to determine the candidate points in the short baseline interferogram.

[0016] In an optional implementation, according to a preset connection condition, any two candidate points in a short baseline interferogram are connected to obtain the arc phase difference corresponding to the arc segment formed by connecting each two candidate points and the spatial network, including:

[0017] determining a connection range with a preset distance as the radius centered on each candidate point in any short baseline interferogram;

[0018] connecting the center candidate point with other candidate points within the connection range to obtain the arc segment formed by connecting each two candidate points and the arc phase difference corresponding to the corresponding arc segment;

[0019] Based on the target number of layers of short baseline interferograms and the arc segments corresponding to each two candidate points in each layer of short baseline interferogram, a spatial network is constructed.

[0020] In an optional implementation, based on a preset screening condition and the arc phase difference of each arc segment in the spatial network, the arc segments are screened to obtain a screened phase unwrapping network, including:

[0021] using a preset least squares phase algorithm to calculate the arc phase difference of each arc segment in the spatial network to obtain the residual phase of each arc segment;

[0022] screening the arc segments based on preset screening conditions and the residual phase of the arc segments, to obtain a screened phase unwrapping network, wherein the preset screening condition is that a maximum value of an absolute value of the residual phase is less than a preset residual threshold.

[0023] In an optional implementation, the screened phase unwrapping network is optimized based on the number of redundant observations of each candidate point in any layer of short baseline interferograms, to obtain an optimized phase unwrapping network, including:

[0024] After deleting isolated candidate points in the screened phase unwrapping network, an initial optimized phase unwrapping network and the number of redundant observations of each candidate point in any layer of short baseline interferograms in the initial optimized phase unwrapping network are obtained.

[0025] If the number of redundant observations of any candidate point in the initial optimized phase unwrapping network all meet a preset number of redundant observations, the initial optimized phase unwrapping network is determined as the optimized phase unwrapping network.

[0026] If the number of redundant observations of any candidate point in the initial optimized phase unwrapping network all are greater than the preset number of redundant observations, an arc segment corresponding to a minimum arc segment phase difference of the preset number of redundant observations is selected according to an order from small to large of the arc segment phase difference, to obtain the optimized phase unwrapping network.

[0027] If the number of redundant observations of any candidate point in the initial optimized phase unwrapping network is less than the preset number of redundant observations, a new preset distance greater than a preset distance is determined as a radius, a new connection range is determined, and the step of connecting a candidate point as a center to other candidate points in the connection range is executed.

[0028] In an optional implementation, the linear programming algorithm based on edge constraints is represented as:

[0029]

[0030] wherein and represent a weight of each arc segment in the phase unwrapping network, I is a unit matrix, C is a coefficient matrix composed of all phase unwrapping networks in space, with a dimension of DxP, D is a number of arc segments in the optimized phase unwrapping network, and P is a number of pixels, wherein a node of the phase unwrapping network corresponds to row and column elements of 1 and -1, and other elements are 0, and are ambiguity vectors of all arc segments, and are ambiguities of each pixel, and an unwrapping phase of the mth short baseline interferogram is represented as

[0031] In an optional implementation, based on each unwrapped phase in the short baseline interferogram, a corresponding wrapped phase is unwrapped to obtain a current phase unwrapping network, including:

[0032] The target number of short baseline unwrapping interferograms are used to estimate a time series phase, and a time series residual phase, i.e., a phase unwrapping error, is obtained;

[0033] A preset quality evaluation algorithm is used to evaluate the phase unwrapping error to obtain a phase unwrapping quality evaluation result;

[0034] Based on the phase unwrapping quality evaluation result, candidate points corresponding to an arc segment that does not meet the accuracy are deleted to obtain a current phase unwrapping network.

[0035] In a second aspect, an InSAR phase unwrapping network optimization device based on redundant observations is provided, which can include:

[0036] An acquisition unit is configured to acquire a target number of short baseline interferograms, candidate points in corresponding interferograms, and wrapped phases of the corresponding interferograms based on SLC images observed at different time points in a region to be monitored.

[0037] In addition, any two candidate points in any short baseline interferogram are connected according to a preset connection condition to obtain an arc phase difference corresponding to an arc segment formed by connecting any two candidate points and a spatial network; the spatial network includes the target number of layers of short baseline interferograms and arc segments corresponding to any two candidate points in each layer of short baseline interferogram.

[0038] A screening unit is configured to screen the arc segments based on a preset screening condition and the arc phase difference of each arc segment in the spatial network to obtain a screened phase unwrapping network.

[0039] An optimization unit is configured to optimize the screened phase unwrapping network based on the number of redundant observations of each candidate point in any layer of short baseline interferogram to obtain an optimized phase unwrapping network.

[0040] The acquisition unit is further configured to use a linear programming algorithm based on edge constraints on any short baseline interferogram in the optimized phase unwrapping network to obtain the unwrapped phase of each arc segment in the short baseline interferogram.

[0041] A processing unit is configured to unwrap a corresponding wrapped phase based on each unwrapped phase in the short baseline interferogram to obtain a current phase unwrapping network.

[0042] In a third aspect, an electronic device is provided, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus;

[0043] The memory is configured to store a computer program.

[0044] The processor is configured to execute the program stored in the memory to implement the method steps of any one of the first aspect.

[0045] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the method steps of any one of the first aspect.

[0046] The method for optimizing an InSAR phase unwrapping network based on redundant observation numbers provided in the application includes the following steps: obtaining a target number of short baseline interferograms, corresponding wrapped phases of the interferograms and candidate points in the interferograms based on SLC images observed at different time points in a region to be monitored; connecting each two candidate points in any short baseline interferogram according to a preset connection condition to obtain an arc phase difference corresponding to an arc segment formed by connecting each two candidate points and a spatial network; the spatial network includes target number of layers of short baseline interferograms and arcs corresponding to each two candidate points in each layer of short baseline interferograms; screening each arc based on a preset screening condition and the arc phase difference of each arc in the spatial network to obtain a screened phase unwrapping network; optimizing the screened phase unwrapping network based on the redundant observation numbers of each candidate point in any layer of short baseline interferogram to obtain an optimized phase unwrapping network; and obtaining unwrapping phases of each arc in the short baseline interferogram by using a linear programming algorithm based on edge constraint for any short baseline interferogram in the optimized phase unwrapping network; and unwrapping the corresponding wrapped phase based on each unwrapping phase in the short baseline interferogram to obtain a current phase unwrapping network. The method improves the phase unwrapping precision and efficiency of the InSAR phase unwrapping network. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments of the application. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0048] Figure 1 A flowchart of a method for optimizing an InSAR phase unwrapping network based on redundant observation numbers provided in the embodiments of the application is shown in the figure.

[0049] Figure 2 A schematic diagram of a to-be-monitored area provided for an embodiment of the present application;

[0050] Figure 3 A schematic diagram of phase unwrapping precision for different redundant observation network provided for an embodiment of the present application;

[0051] Figure 4 A schematic diagram of estimated deformation parameters after phase unwrapping for a network with 25 redundant observations provided for an embodiment of the present application;

[0052] Figure 5 A structural schematic diagram of an optimization device for an InSAR phase unwrapping network based on redundant observation numbers provided for an embodiment of the present application;

[0053] Figure 6 A structural schematic diagram of an electronic device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, and not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0055] For the convenience of understanding, the terms involved in the embodiments of the present application are explained as follows:

[0056] The differential interferometric synthetic aperture radar (InSAR) is a brand-new earth observation technology, which is a new technology combining the traditional radar remote sensing technology and the radio astronomy interferometry technology. This measurement method uses two or more synthetic aperture radar images to generate a ground deformation map according to the phase difference of the echoes received by the satellite or the airplane, so as to explore the micro deformation information of the ground. The InSAR has the characteristics of all-weather and all-day detection, wide detection range, high detection precision, and low detection cost. When the InSAR technology is used for ground deformation monitoring, the prerequisite is to obtain the absolute value of the phase of each short baseline interferogram, which is called phase unwrapping.

[0057] An arc segment refers to the connection relationship between two pixel points in space. The arc segment is the phase difference between the two pixel points.

[0058] The linear programming algorithm based on edge constraint is an optimization-based method, which estimates the phase gradient, estimates the real phase through global phase information, takes the principal value of the phase, minimizes the minimum one-norm between the estimated principal value of the phase and the wrapped phase gradient, and continuously optimizes to obtain the unwrapped phase. However, the traditional minimum cost flow (MCF) phase unwrapping method uses a Delaunay triangular network to identify spatial triangular closure difference constraints, and then uses a linear programming method to realize phase unwrapping. However, it is difficult to find a triangular closure network as a constraint after optimization of the phase unwrapping network, so the patent uses spatial edge constraints instead of triangular closure difference constraints.

[0059] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings of the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application, and the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0060] Figure 1 A flowchart of an optimization method for an InSAR phase unwrapping network based on redundant observation numbers is provided for the embodiments of the present application. As shown in Figure 1 , the method can include:

[0061] Step S110, based on the SLC images observed at different times in the region to be monitored, obtaining a target number of short baseline interferograms, wrapped phases of the corresponding interferograms and candidate points in the corresponding interferograms.

[0062] According to the preset space-time baseline threshold, the SLC images observed at different times in the region to be monitored are processed to generate a target number (such as M) of short baseline interferograms and wrapped phases of the corresponding interferograms Specifically, according to the preset space-time baseline threshold, the SLC images satisfying the space-time baseline threshold are pairwise difference interferometrically processed, and the difference short baseline interferograms with high coherence are generated by using these images with short space-time baseline, so as to obtain a target number of short baseline interferograms, each short baseline interferogram containing a plurality of pixel points and wrapped phases of the corresponding interferograms

[0063] Among them, since the plane positions (i.e. x, y) of the pixel points on all short baseline interferograms corresponding to the same acquisition area are the same, only the position of the pixel point on any short baseline interferogram needs to be determined, and the positions of the pixel points on other short baseline interferograms are the same as that of the pixel point.

[0064] For any short baseline interferogram, according to the amplitude dispersion index method, the pixel points in the obtained SLC images are selected to determine the candidate points in the short baseline interferogram.

[0065] Step S120, connecting any two candidate points in each short baseline interferogram according to a preset connection condition to obtain an arc segment phase difference corresponding to an arc segment formed by connecting any two candidate points and a spatial network.

[0066] In a specific implementation, a connection range with a preset distance as a radius is determined with each candidate point in any short baseline interferogram as a center.

[0067] The candidate point as the center is connected with other candidate points in the connection range to obtain an arc segment formed by connecting any two candidate points and an arc segment phase difference corresponding to the arc segment. That is, an arc segment phase difference of candidate point p and candidate point q in M short baseline interferograms is

[0068] A spatial network is constructed based on the target number of layers of short baseline interferograms and the arcs corresponding to each two candidate points in each layer of short baseline interferograms.

[0069] Step S130, screening each arc based on a preset screening condition and the arc segment phase difference of each arc in the spatial network to obtain a screened phase unwrapping network.

[0070] In a specific implementation, a preset least square phase algorithm is used to calculate the arc segment phase difference of each arc in the spatial network to obtain a residual phase Δ p,q of each arc.

[0071] The preset least square phase algorithm can be represented as:

[0072]

[0073]

[0074]

[0075] In the formula, A M×N represents a design matrix determined by the wrapped phase of M short baseline interferograms, N is the number of SAR data, and the arc (phase difference) between candidate point p and candidate point q. represents a time series phase of the phase difference between candidate point p and candidate point q in N SAR data, is an estimated value, and Δ p,q represents a residual phase obtained by the least square phase algorithm. MxN indicates that N SAR data generate M short baseline interferograms, so for each arc, a matrix A M×N is formed, and the number of SAR data is the number of SLC images.

[0076] Then, based on a preset screening condition and the residual phase Δ p,qThe filtered phase unwrapping network is obtained by screening each arc segment.

[0077] The preset screening condition is that a maximum value of absolute values of residual phases Δ p,q is less than a preset residual threshold value. The preset screening condition can be expressed as: max | Δ p,q | < T residual , wherein T residual is the preset residual threshold value, and max || represents a maximum value of absolute values.

[0078] In step S140, the filtered phase unwrapping network is optimized based on the redundant observation numbers of each candidate point in any layer of short baseline interferograms, to obtain an optimized phase unwrapping network.

[0079] The filtered phase unwrapping network obtained in step S130 is uncontrollable in structure, for example, a candidate point with high quality may be connected to too many other candidate points, forming too many network arcs, which brings a lot of burden to the phase unwrapping in space, and a candidate point with low quality is connected to too few other candidate points, which may cause unreliable phase unwrapping. The redundant observation components of the space network can balance the errors, so network optimization must be performed to improve efficiency and ensure accuracy.

[0080] In a specific implementation, isolated candidate points in the filtered phase unwrapping network are obtained by graph theory, and the isolated candidate points in the filtered phase unwrapping network are deleted. Then, the redundant observation numbers of each candidate point in any layer of short baseline interferograms in the phase unwrapping network after the deletion processing, that is, the connection numbers of each candidate point and other candidate points, are obtained, to obtain an initial optimized phase unwrapping network, that is, the unselected arc segments in the above-processed phase unwrapping network are deleted.

[0081] If the redundant observation numbers of any candidate point in the initial optimized phase unwrapping network all satisfy the preset redundant observation number, the initial optimized phase unwrapping network is determined as the optimized phase unwrapping network.

[0082] If the redundant observation numbers of any candidate point in the initial optimized phase unwrapping network are greater than the preset redundant observation number, the arc segment corresponding to the minimum arc segment phase difference of the preset redundant observation number is selected in the order of the arc segment phase difference from small to large, to obtain the optimized phase unwrapping network.

[0083] If the redundant observation numbers of any candidate point in the initial optimized phase unwrapping network are less than the preset redundant observation number, a new preset distance with a radius greater than the preset distance is determined as a new connection range, and the step of connecting the candidate point as the center to other candidate points in the connection range is returned to be executed.

[0084] The above manner can guarantee the number of connections of each candidate point in the network, thereby guaranteeing the accuracy of subsequent phase unwrapping.

[0085] In step S150, the linear programming algorithm based on edge constraint is used to obtain the unwrapped phase of each arc segment in the short baseline interferogram in the optimized phase unwrapping network.

[0086] The linear programming algorithm based on edge constraint can be expressed as:

[0087]

[0088] The weight of each arc segment in the phase unwrapping network is represented by w, I is a unit matrix, C is a coefficient matrix composed of all phase unwrapping networks in space, and the dimension is D×P, D is the number of arc segments in the optimized phase unwrapping network, and P is the number of pixels, wherein the corresponding row and column elements of the phase unwrapping network node are 1 and-1, and the remaining elements are 0. The ambiguity vector of all arc segments is represented by x, The ambiguity of each pixel is represented by x m, and the unwrapped phase of the mth short baseline interferogram is represented by

[0089] In step S160, the corresponding wrapped phase is unwrapped based on each unwrapped phase in any short baseline interferogram, and the current phase unwrapping network is obtained.

[0090] In the specific implementation, all short baseline unwrapping short baseline interferograms estimate the time sequence phase The time sequence residual phase Δθ is obtained residual , that is, the phase unwrapping error;

[0091] The preset quality evaluation algorithm is used to evaluate the time sequence phase unwrapping residual Δθ residual , and the phase unwrapping quality evaluation result γ is obtained.

[0092]

[0093]

[0094] In the formula, θ M×1 is all unwrapped phases, Δθ residual,i is the residual of the i th unwrapped phase, M is the number of short baseline interferograms, N is the number of SAR data, is the estimated unwrapping time sequence phase, A M×N is a coefficient matrix composed of a short baseline interferogram network. ​​​​

[0095] Then, based on the phase unwrapping quality evaluation result γ, the candidate points corresponding to the arc segments not meeting the accuracy are deleted to obtain a current phase unwrapping network.

[0096] Further, if the phase unwrapping accuracy is relatively high, after step S160, the steps S110 to S150 can be iteratively calculated again until a preset iteration termination condition is met.

[0097] The method balances the contradiction between the InSAR phase unwrapping data processing efficiency and the deformation accuracy by establishing a redundant observation model of the phase unwrapping network and using the redundant observation number to further optimize the phase unwrapping network. The phase unwrapping network is optimized by using the redundant observation number, and the accuracy of the phase unwrapping is evaluated by using the real phase residual. The calculation efficiency is improved, the accuracy and efficiency of the InSAR phase unwrapping are independently controllable, the phase unwrapping effect is good. Moreover, the accuracy of the phase unwrapping is further improved through iteration processing. In the case of ensuring the accuracy of the phase unwrapping, the accuracy and efficiency of the InSAR phase unwrapping are independently controllable.

[0098] Test data verification stage:

[0099] For step S140, it is found through experiments that the number of candidate points is unchanged, the more the redundant observation number and the arc segment number of each candidate point, the lower the efficiency of the optimized phase unwrapping network, but the accuracy is improved, as shown in Table 1, therefore, the redundant observation number and the arc segment number of each candidate point cannot be simply increased or decreased, according to the demand and accuracy requirement, the InSAR phase unwrapping accuracy and efficiency are independently controllable through the redundant observation model of the phase unwrapping network.

[0100] Table 1

[0101]

[0102] Figure 2 For the monitoring area to be monitored involved in the present application, Figure 3 For the phase unwrapping accuracy of different redundant observation network, wherein (A) is the estimated time coherence after the network with a redundant observation number of 5 is phase unwrapped, (B) is the estimated time coherence after the network with a redundant observation number of 10 is phase unwrapped, (C) is the estimated time coherence after the network with a redundant observation number of 25 is phase unwrapped, and (D) is the estimated time coherence after the network with a redundant observation number of 50 is phase unwrapped.

[0103] As Figure 4It is shown that the deformation parameters are estimated after phase unwrapping for the network with 25 redundant observations, wherein (A) annual deformation rate, (B) DEM error, (C) thermal expansion coefficient, (D) phase unwrapping time coherence.

[0104] Corresponding to the above method, the embodiment of the application also provides an optimization device for InSAR phase unwrapping network based on redundant observations, as shown in the figure, the device comprises: Figure 5

[0105] The acquisition unit 510 is configured to acquire a target number of short baseline interferograms, corresponding wrapped phases of the interferograms, and candidate points in the corresponding interferograms based on SLC images observed at different time in a region to be monitored.

[0106] In addition, any two candidate points in any short baseline interferogram are connected according to a preset connection condition, to obtain an arc phase difference corresponding to an arc segment formed by connecting any two candidate points, and a spatial network; the spatial network comprises the target number of short baseline interferograms and the arc segments corresponding to any two candidate points in each short baseline interferogram.

[0107] The screening unit 520 is configured to screen the arc segments based on a preset screening condition and the arc phase differences of the arc segments in the spatial network, to obtain a phase unwrapping network after screening.

[0108] The optimization unit 530 is configured to optimize the phase unwrapping network after screening based on the redundant observations of each candidate point in any short baseline interferogram, to obtain an optimized phase unwrapping network.

[0109] The acquisition unit 510 is further configured to obtain the unwrapped phases of the arc segments in any short baseline interferogram in the optimized phase unwrapping network by using a linear programming algorithm based on edge constraints.

[0110] The processing unit 540 is configured to perform unwrapping processing on the corresponding wrapped phase based on each unwrapped phase in the short baseline interferogram, to obtain a current phase unwrapping network.

[0111] The functions of each functional unit of the optimization device for InSAR phase unwrapping network based on redundant observations provided in the above embodiments of the application can be realized through the above method steps, and therefore, the specific working processes and beneficial effects of each unit in the optimization device for InSAR phase unwrapping network based on redundant observations provided in the embodiments of the application will not be repeated here.

[0112] The embodiment of the application also provides an electronic device, as shown in the figure, the device comprises: Figure 6 ​As shown, the electronic device includes a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640.

[0113] The memory 630 is configured to store a computer program.

[0114] The processor 610 is configured to execute the program stored in the memory 630 to implement the following steps:

[0115] Based on the SLC images observed at different times in the region to be monitored, a target number of short baseline interferograms, corresponding wrapped phases of the short baseline interferograms, and candidate points in the corresponding interferograms are obtained.

[0116] According to a preset connection condition, any two candidate points in any short baseline interferogram are connected to obtain an arc phase difference corresponding to an arc segment formed by connecting the two candidate points and a spatial network; the spatial network includes the target number of layers of short baseline interferograms and the arc segments corresponding to any two candidate points in each layer of short baseline interferogram.

[0117] Based on a preset screening condition and the arc phase difference of each arc segment in the spatial network, the arc segments are screened to obtain a screened phase unwrapping network.

[0118] Based on the redundant observation number of each candidate point in any layer of short baseline interferogram, the screened phase unwrapping network is optimized to obtain an optimized phase unwrapping network.

[0119] For any short baseline interferogram in the optimized phase unwrapping network, a linear programming algorithm based on edge constraint is used to obtain the unwrapping phase of each arc segment in the short baseline interferogram.

[0120] Based on each unwrapping phase in the short baseline interferogram, the corresponding wrapped phase is unwrapped to obtain a current phase unwrapping network.

[0121] The above-mentioned communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, only one thick line is shown in the figure, but it does not mean that there is only one bus or only one type of bus.

[0122] The communication interface is configured to communicate between the above-mentioned electronic device and other devices.

[0123] The memory can include a random access memory (RAM) and can also include a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory can also be at least one storage device located remotely from the aforementioned processor.

[0124] The processor described above can be a general processor, including a central processing unit (CPU), a network processor (NP), etc. It can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0125] The implementation manners and beneficial effects of the electronic device in the above-mentioned embodiments can be achieved by referring to the steps in the above-mentioned embodiments, and thus the specific working process and beneficial effects of the electronic device provided by the embodiments of the present application will not be repeated here. Figure 1 The implementation manners and beneficial effects of the electronic device in the above-mentioned embodiments can be achieved by referring to the steps in the above-mentioned embodiments, and thus the specific working process and beneficial effects of the electronic device provided by the embodiments of the present application will not be repeated here.

[0126] In another embodiment provided by the present application, a computer readable storage medium is provided, and the computer readable storage medium stores instructions, when the instructions are run on a computer, the computer executes the optimization method of the InSAR phase unwrapping network based on redundant observations in any of the above-mentioned embodiments.

[0127] In another embodiment provided by the present application, a computer program product containing instructions is provided, when the instructions are run on a computer, the computer executes the optimization method of the InSAR phase unwrapping network based on redundant observations in any of the above-mentioned embodiments.

[0128] Those skilled in the art should understand that the embodiments in the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the embodiments in the present application can be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the embodiments in the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0129] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each flowchart and / or block diagram can represent a method, module, and / or portion of code which comprises one or more executable Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0130] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each flowchart and / or block diagram can represent a method, module, and / or portion of code which comprises one or more executable Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0131] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each flowchart and / or block diagram can represent a method, module, and / or portion of code which comprises one or more executable ​ The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0132] Although preferred embodiments of the application have been described herein, it will be apparent to those skilled in the art that various modifications and changes can be made to the embodiments without departing from the spirit and scope of the application. Accordingly, it is intended that all claims be interpreted to encompass all such modifications and changes.

[0133] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described herein.

Claims

1. An optimization method for an InSAR phase unwrapping network based on redundant observation pairs, characterized in that, The method includes: Based on SLC images observed at different times within the area to be monitored, the number of short baseline interferograms of the target, the winding phase of the corresponding interferograms, and the candidate points in the corresponding interferograms are obtained; According to the preset connection conditions, every two candidate points in any short baseline interferogram are connected to obtain the arc phase difference and spatial network corresponding to the arc segment formed by connecting two candidate points; the spatial network includes the target number of short baseline interferograms and the arc segment corresponding to every two candidate points in each short baseline interferogram. Based on preset filtering conditions and the arc phase difference of each arc segment in the spatial network, each arc segment is filtered to obtain a filtered phase unwrapped network. Based on the number of redundant observations of each candidate point in any layer of short baseline interferogram, the filtered phase unwrapping network is optimized to obtain the optimized phase unwrapping network. For any short baseline interferogram in the optimized phase unwrapping network, a linear programming algorithm based on edge constraints is used to obtain the unwrapped phase of each arc segment in the short baseline interferogram; Based on each unwound phase in the short baseline interferogram, the corresponding wrapped phase is unwound to obtain the current phase unwound network.

2. The method as described in claim 1, characterized in that, Based on SLC images observed at different times within the monitored area, a number of short baseline interferograms of the target, the wrapped phase of the corresponding interferograms, and candidate points in the corresponding interferograms are obtained, including: Based on the preset spatiotemporal baseline threshold, the SLC images observed at different times in the area to be monitored are processed to generate the target number of short baseline interferograms and the corresponding interferogram winding phase; For any short baseline interferogram, the pixels in the SLC image are selected according to the amplitude deviation index method to determine the candidate points in the short baseline interferogram.

3. The method as described in claim 1, characterized in that, According to preset connection conditions, every two candidate points in any short baseline interferogram are connected to obtain the arc segment phase difference and spatial network corresponding to the arc segment formed by connecting every two candidate points, including: Determine the connection range centered on each candidate point in any short baseline interferogram, with a preset distance as the radius; Connect the candidate point as the center with other candidate points within the connection range to obtain the arc segment formed by connecting every two candidate points, and the phase difference of the corresponding arc segment; A spatial network is constructed based on the target number of short baseline interferograms and the arc segments corresponding to every two candidate points in each short baseline interferogram.

4. The method as described in claim 1, characterized in that, Based on preset filtering conditions and the arc segment phase difference of each arc segment in the spatial network, the arc segments are filtered to obtain a filtered phase unwrapping network, including: A preset least squares phase algorithm is used to calculate the arc phase difference of each arc segment in the spatial network to obtain the residual phase of each arc segment; Based on preset screening conditions and the residual phase of each arc segment, each arc segment is screened to obtain a screened phase unwrapping network. The preset screening condition is that the maximum value of the absolute value of the residual phase is less than a preset residual threshold.

5. The method as described in claim 3, characterized in that, Based on the number of redundant observations for each candidate point in any layer of short baseline interferogram, the filtered phase unwrapping network is optimized to obtain an optimized phase unwrapping network, including: After deleting isolated candidate points in the filtered phase unwrapping network, an initial optimized phase unwrapping network is obtained, along with the number of redundant observations of each candidate point in the initial optimized phase unwrapping network in any layer of short baseline interferogram. If the number of redundant observations at any candidate point in the initially optimized phase unwinding network meets the preset number of redundant observations, then the initially optimized phase unwinding network is determined as the optimized phase unwinding network. If the number of redundant observations at any candidate point in the initially optimized phase unwinding network is greater than the preset number of redundant observations, then the arc segment corresponding to the smallest arc segment phase difference with the preset number of redundant observations is selected according to the arc segment phase difference sorted from smallest to largest, and the optimized phase unwinding network is obtained. If the number of redundant observations at any candidate point in the initially optimized phase unwrapping network is less than the preset number of redundant observations, then a new preset distance greater than the preset distance is used as the radius to determine a new connection range, and the process returns to the execution step: connect the candidate point as the center with other candidate points within the connection range.

6. The method as described in claim 1, characterized in that, The linear programming algorithm based on edge constraints is expressed as follows: in and Let I represent the weight of each arc segment in the phase unwinding network, where I is the identity matrix, C is the coefficient matrix composed of all phase unwinding networks in space, and the dimension is D×P. Here, D is the number of arc segments in the optimized phase unwinding network, and P is the number of pixels. The corresponding row and column elements of each phase unwinding network node are 1 and -1, with all other elements being 0. and It is the ambiguity vector of all arc segments. and It represents the blurriness of each pixel, and the wrapped phase of the m-th short baseline interferogram is expressed as...

7. The method as described in claim 1, characterized in that, Based on each unwound phase in the short baseline interferogram, the corresponding wrapped phase is unwound to obtain the current phase unwound network, including: Estimate the time series phase of the target number of short baseline unwrapped interferograms, and obtain the time series residual phase, i.e., the phase unwrapping error; The phase unwinding error is evaluated using a preset quality evaluation algorithm to obtain the phase unwinding quality evaluation result; Based on the phase unwinding quality evaluation results, candidate points corresponding to arc segments that do not meet the accuracy requirements are deleted to obtain the current phase unwinding network.

8. An optimization device based on a redundant observation pair InSAR phase unwrapping network, characterized in that, The device includes: The acquisition unit is used to acquire the target number of short baseline interferograms, the winding phase of the corresponding interferograms, and the candidate points in the corresponding interferograms based on SLC images observed at different times in the area to be monitored. Furthermore, according to preset connection conditions, every two candidate points in any short baseline interferogram are connected to obtain the arc phase difference and spatial network corresponding to the arc segment formed by connecting every two candidate points; the spatial network includes the target number of layers of short baseline interferograms and the arc segment corresponding to every two candidate points in each layer of short baseline interferograms. The filtering unit is used to filter each arc segment based on preset filtering conditions and the arc segment phase difference in the spatial network to obtain a filtered phase unwrapped network. An optimization unit is used to optimize the filtered phase unwrapping network based on the number of redundant observations of each candidate point in any layer of short baseline interferogram, so as to obtain an optimized phase unwrapping network. The acquisition unit is also used to obtain the unwound phase of each arc segment in the short baseline interferogram of any short baseline interferogram in the optimized phase unwrapping network by using a linear programming algorithm based on edge constraints. The processing unit is used to perform unwinding processing on the corresponding entangled phase based on each unwound phase in the short baseline interferogram to obtain the current phase unwound network.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

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 described in any one of claims 1-7.

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