A phase unwrapping method, terminal and storage medium for multi-temporal InSAR in urban areas

Abstract

The present invention discloses a multi-temporal InSAR phase unwrapping method, terminal and storage medium for urban areas, the method comprising: obtaining a number of registered radar images, generating a plurality of interference graphs according to a multi-main image interference network strategy, calculating the preliminary unwrapping phase of the time dimension of the plurality of interference graphs; calculating the height difference on the corresponding arc segment, screening the arc segment with a preset height difference; controlling the measurement points on the screened arc segment to detect, selecting the measurement points with preset elevation values, and constructing a constraint network according to the selected measurement points; calculating the accuracy of the parameters to be estimated for each arc segment in the constraint network, and optimizing the spatial grid structure of the constraint network according to the accuracy; obtaining the unwrapping phase of the measurement point, performing error detection and correction on the unwrapping phase, and estimating the surface deformation information and elevation information of the study area. The present invention can take into account the building height and the spatial network observation structure, and improve the quality of phase unwrapping in urban areas.

Description

Technical Field

[0001] The present invention relates to the field of surveying and mapping technology, and in particular to an urban multi-temporal InSAR phase unwrapping method, a terminal and a storage medium. Background Art

[0002] Urban areas often cause various forms of surface deformation due to underground construction, land reclamation, groundwater exploitation and other reasons. Surface deformation monitoring is of great significance for the assessment of potential dangers. Since the beginning of the 21st century, multi-temporal InSAR (MT-InSAR) has been widely used as a geodetic technology because of its unique advantages in the field of large-scale and high-resolution surface deformation monitoring. In the processing of MT-InSAR deformation monitoring data, phase unwrapping restores the entangled phase to the unentangled state to obtain deformation information. Unwrapping accuracy is one of the main factors affecting the deformation results, especially in areas with complex terrain and landforms.

[0003] Permanent scatterer InSAR method (PS-InSAR) is the most classic MT-InSAR technology. In this method, phase-stable point targets are usually selected as measurement points, and the measurement points are spatially connected to construct double-difference phase observations. The unwrapped spatiotemporal phase on the connection path is obtained by modeling and analyzing the double-difference phase, and finally the optimal unwrapped phase and deformation parameters on the measurement point are obtained by spatial network adjustment. Based on this idea, some PS-InSAR phase unwrapping methods have been developed, such as 3D phase unwrapping and minimum cost flow unwrapping. In addition, researchers have proposed technologies such as adaptive deformation models and interferometric phase reconstruction to address problems in different MT-InSAR application scenarios. In addition to improving the selection of measurement points and networking methods, existing technologies use homogeneous points in SAR image stacks for phase filtering to improve the interferometric phase signal-to-noise ratio and thus improve the phase unwrapping accuracy. However, in urban areas, especially in densely built areas, due to the large height differences of infrastructure, large gradient interferometric phases are easily generated, resulting in phase unwrapping errors and causing error accumulation in the network unwrapping direction, resulting in reduced unwrapping accuracy of measurement points, thereby affecting the accuracy of building deformation and elevation parameter calculation. High-precision phase unwrapping is a key issue in urban surface deformation monitoring using MT-InSAR technology. In densely populated urban environments, the accuracy of current phase unwrapping methods is difficult to guarantee.

[0004] Therefore, the prior art needs to be improved. Summary of the invention

[0005] The technical problem to be solved by the present invention is that, in view of the defects of the prior art, the present invention provides a multi-temporal InSAR phase unwrapping method, a terminal and a storage medium in an urban area to solve the problem of low precision of the existing phase unwrapping method.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0007] In a first aspect, the present invention provides a method for phase unwrapping of multi-temporal InSAR in an urban area, comprising:

[0008] Acquire a number of registered radar images, generate a plurality of interference patterns according to a multi-primary image interference network strategy, and calculate preliminary unwrapped phases of the time dimension of the plurality of interference patterns;

[0009] Calculate the height difference on the corresponding arc segment according to the preliminary unwrapped phase, and select the arc segment with a preset height difference;

[0010] Control the measurement points on the screened arc segment for detection, select measurement points with preset elevation values, and construct a constraint network based on the selected measurement points;

[0011] Calculating the accuracy of the parameters to be estimated for each arc segment in the constraint network, and optimizing the spatial grid structure of the constraint network according to the accuracy;

[0012] The unwrapped phase of the measurement point in the optimized constraint network is obtained, errors are detected and corrected for the unwrapped phase, and the surface deformation information and elevation information of the study area are estimated.

[0013] In one implementation, the calculating of the preliminary unwrapped phases of the time dimension of the plurality of interference patterns includes:

[0014] Using a spatial free connection network to establish a redundant spatial network path; wherein the spatial network path is an arc segment formed according to a preset measurement point pair;

[0015] The double difference phases on all interference patterns on each spatial network path are obtained to obtain the preliminary unwrapped phase.

[0016] In one implementation, the step of calculating the height difference on the corresponding arc segment according to the preliminary unwrapped phase and selecting the arc segment with a preset height difference includes:

[0017] Performing a discrete search of a single elevation parameter on the arc segment retained by the preliminary unwrapping phase;

[0018] The arc segments with the preset height difference are selected according to the search results to obtain a plurality of spatially independent sub-networks.

[0019] In one implementation, the control detects the measurement points on the arc segment after screening, selects the measurement points with preset elevation values, and constructs a constraint network according to the selected measurement points, including:

[0020] Detect each independent subnet and perform adjustment on each independent subnet to obtain the elevation value at the corresponding measuring point;

[0021] The measurement points of the preset elevation values ​​are selected, and the constraint network is constructed to constrain and connect the independent sub-networks.

[0022] In one implementation, the calculating the accuracy of the parameter to be estimated for each arc segment in the constraint network and optimizing the spatial grid structure of the constraint network according to the accuracy includes:

[0023] Calculating the accuracy of the estimated parameters of each arc segment in the constraint network according to the phase temporal coherence algorithm;

[0024] Redundant arcs in the constraint network are screened according to the accuracy to obtain a network path that meets the accuracy requirement.

[0025] In one implementation, obtaining the unwrapped phase of the measurement point in the optimized constraint network includes:

[0026] Adjustment processing is performed on each measurement point in the optimized constraint network to estimate the unwrapped phase corresponding to each measurement point.

[0027] In one implementation, the error detection and correction of the unwrapped phase and the estimation of the surface deformation information and elevation information of the study area include:

[0028] Using the closed value of the triangular closed network in the interference pattern network of the unwrapped phase to identify the phase jump;

[0029] Correct the unwrapping error based on the identified phase jump and correction algorithm;

[0030] The surface deformation information and elevation information of the study area are obtained according to the corrected unwrapped phase.

[0031] In one implementation, the correcting the unwrapping error according to the identified phase jump and the correction algorithm includes:

[0032] Comparing the phase residual between the original wrapped phase and the unwrapped phase, performing ambiguity detection in combination with the closure value and the number of ambiguities, and identifying the ambiguity existing in the phase main value part;

[0033] Using the minimum two phase residual phase, combined with the closure value and the number of ambiguities, ambiguity detection is performed to identify ambiguities existing in the integer phase part;

[0034] The unwrapping error is corrected according to the identified ambiguity and the correction algorithm.

[0035] In a second aspect, the present invention further provides a terminal, comprising: a processor and a memory, wherein the memory stores an urban multi-phase InSAR phase unwrapping program, and when the urban multi-phase InSAR phase unwrapping program is executed by the processor, it is used to implement the operation of the urban multi-phase InSAR phase unwrapping method as described in the first aspect.

[0036] In a third aspect, the present invention further provides a storage medium, which is a computer-readable storage medium, and which stores a multi-phase InSAR phase unwrapping program for an urban area. When the multi-phase InSAR phase unwrapping program for an urban area is executed by a processor, it is used to implement the operation of the multi-phase InSAR phase unwrapping method for an urban area as described in the first aspect.

[0037] The present invention adopts the above technical solution to achieve the following effects:

[0038] The present invention proposes a MT-InSAR phase unwrapping method for urban area deformation monitoring. The method introduces a highly guided spatial network construction strategy to select high-quality propagation paths, thereby avoiding the influence of large and discontinuous changes in building heights on phase unwrapping. At the same time, in order to optimize the phase unwrapping quality, two quality control strategies are deployed, namely, a spatial network structure optimizer and a phase unwrapping error corrector, to suppress the accumulation and propagation of MT-InSAR phase unwrapping errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0040] Figure 1 It is a flow chart of a multi-temporal InSAR phase unwrapping method for an urban area in one implementation of the present invention.

[0041] Figure 2 It is a structural diagram of a Delaunay triangulation network in the prior art.

[0042] Figure 3 It is a schematic diagram of a spatially free connection network in one implementation of the present invention.

[0043] Figure 4 It is a flow chart of a highly guided spatial networking strategy in one implementation of the present invention.

[0044] Figure 5It is a graph of MT-InSAR elevation estimation results based on network paths of different qualities in one implementation of the present invention.

[0045] Figure 6 It is a functional principle diagram of a terminal in one implementation of the present invention.

[0046] Figure 4 In the figure: (a) is a spatial network diagram based on all measurement points, (b) is an independent subnetwork diagram generated after elevation detection, and a distribution diagram of selected measurement points with lower elevation values, (c) is a constraint network diagram based on control measurement points, and (d) is a spatial network diagram after fusing various networks, i.e., a height-guided network diagram;

[0047] Figure 5 Middle: (a) is the elevation result at the measurement point of the low-quality network path, (b) is the elevation result at the measurement point of the corrected path after height guidance, and (c) is the display of the measurement point in (b) on the Google Earth 3D model.

[0048] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] Exemplary Methods

[0051] The phase unwrapping accuracy of multi-temporal synthetic aperture radar interferometry (MT-InSAR) has a significant impact on the accuracy of ground deformation monitoring. In urban areas, especially in urban areas with dense high-rise buildings and large building height differences, the phase unwrapping failure rate is high when using MT-InSAR to obtain surface deformation, which leads to large deformation and elevation estimation errors.

[0052] In response to the above technical problems, this embodiment proposes an MT-InSAR phase unwrapping method for urban area deformation monitoring. The method introduces a highly guided spatial network construction strategy to select high-quality propagation paths, thereby avoiding the impact of large and discontinuous changes in building heights on phase unwrapping. At the same time, in order to optimize the phase unwrapping quality, two quality control strategies are deployed, namely, a spatial network structure optimizer and a phase unwrapping error corrector, to suppress the accumulation and propagation of MT-InSAR phase unwrapping errors.

[0053] like Figure 1 As shown, an embodiment of the present invention provides a multi-temporal InSAR phase unwrapping method for urban areas, comprising the following steps:

[0054] Step S100, obtaining a plurality of registered radar images, generating a plurality of interference graphs according to a multi-primary image interference network strategy, and calculating preliminary unwrapping phases of the time dimension of the plurality of interference graphs.

[0055] In this embodiment, the urban multi-temporal InSAR phase unwrapping method is applied in a terminal, and the terminal includes but is not limited to: a computer, a mobile terminal and other devices.

[0056] In this embodiment, a height-guided networking strategy (HgN) is established to select high-quality propagation paths, thereby avoiding the influence of large and discontinuous building height changes on the unwrapped propagation path on phase unwrapping; in order to optimize the phase unwrapping quality, two quality control strategies are deployed, namely spatial network refinement (SnR) and phase unwrapping error correction, to suppress and correct the error accumulation or propagation of MT-InSAR phase unwrapping. The three proposed optimization strategies are gradually integrated into the existing PS-InSAR data processing flow to generate a new MT-InSAR urban area phase unwrapping method that mixes various optimizers.

[0057] In this embodiment, it is necessary to obtain a number of registered radar images and generate a plurality of interference graphs according to a multi-primary image interference network strategy.

[0058] Specifically, N+1 precisely registered SAR images (i.e., radar images) can be obtained, and M interferograms (IFGs) can be generated according to the multi-primary image interferometry network strategy. In the process of generating M interferograms, the interferogram stacking method is mainly adopted to stack the N+1 precisely registered SAR images into M interferograms, so as to perform preliminary unwrapping phase calculation in the time dimension according to the M interferograms.

[0059] Specifically, in an implementation of this embodiment, step S100 includes the following steps:

[0060] Step S101, using a spatial free connection network to establish a redundant spatial network path; wherein the spatial network path is an arc segment formed according to a preset measurement point pair;

[0061] Step S102, obtaining double difference phases on all interference patterns on each spatial network path to obtain the preliminary unwrapped phase.

[0062] In this embodiment, after obtaining M interference patterns, a density network can be constructed based on the M interference patterns through a pixel-by-pixel estimator; in the process of constructing the density network, arc segments containing gross errors can be eliminated through least squares estimation and closure monitoring, so that each measurement point is connected by an arc segment that meets the quality requirements, thereby obtaining the required measurement points and paths in the density network (i.e., the arc segments in the density network).

[0063] In the traditional PS-InSAR method, the Delaunay triangulation network (such as Figure 2 As shown in FIG. 1 , a density network is established; in this embodiment, a spatial free connection network (FCN, as shown in FIG. 1 ) is used. Figure 3 As shown in FIG. 1 ), a redundant spatial network path is established to connect each pair of measurement points, and the double difference differential phase on all interference patterns on each path is obtained as an observation value; wherein, in this embodiment, the connection line of a group of measurement point pairs is a path, and a path is an arc segment in the spatial network (such as Figure 3 The arc between point i and point j is shown).

[0064] In this embodiment, the double difference phase on the arc segment can be expressed as:

[0065]

[0066] Wherein, wrap{·} indicates that the wrapping operation can make the phase distribution between (-π,π];

[0067] Δψ i,j is the wound double differential phase;

[0068] is the deformation phase;

[0069] is the terrain residual phase (i.e., DEM error);

[0070] is the orbit error;

[0071] is the atmospheric delay error;

[0072] is decorrelated noise.

[0073] Since the main phase component in formula (1) is and The interferograms of multiple main images can be transformed into the interferograms of time intervals; therefore, the above four main components can be integrated into an unknown parameter to be estimated: And use the least squares estimation to solve. Therefore, the above formula (1) can be expressed as:

[0074]

[0075]

[0076] in, represents the unknown time interval unwrapped phase (N+1 SAR images have N time intervals).

[0077] The matrix A is a coefficient matrix connecting the interferograms of multiple main images and the time interval unwrapped phase. The rows and columns of A represent the interferograms and time interval sequences, respectively. If an interferogram is obtained by the difference between the 1st and 5th SAR images, the 1st to 4th columns in the corresponding row of A are recorded as 1, and the other positions are 0.

[0078] The above formula (3) can be used to solve the estimated parameters using iterative least squares estimation. The use of this estimation model can avoid the influence of the deformation model error introduced when the deformation model is preset in the traditional PS-InSAR technology. On the other hand, when using this model, the gross error detection method in the traditional least squares estimation can be used to eliminate the arc segments containing gross errors, so that each measurement point is connected by an arc segment that meets the quality requirements. The result is as follows Figure 4 As shown in a.

[0079] like Figure 1 As shown, in one implementation of the embodiment of the present invention, the urban multi-temporal InSAR phase unwrapping method further includes the following steps:

[0080] Step S200, calculating the height difference on the corresponding arc segment according to the preliminary unwrapped phase, and screening out the arc segment with a preset height difference.

[0081] In this embodiment, a discrete search is performed based on the estimated value of the terrain residual phase (ie, DEM error) on each arc segment in the spatial network, thereby integrating spatially independent subnetworks and selecting constraint points in the spatial network.

[0082] Specifically, in an implementation of this embodiment, step S200 includes the following steps:

[0083] Step S201, performing a discrete search of a single elevation parameter on the arc segment retained by the preliminary unwrapping phase;

[0084] Step S202: selecting arc segments with the preset height difference according to the search results to obtain a plurality of spatially independent sub-networks.

[0085] In this embodiment, based on the above-mentioned reserved arc segments (i.e., Figure 4The arc segment shown in a in the figure can be discretely searched for a single elevation parameter (Δh) arc by arc using the following formula (4). Arcs with smaller height differences are selected for retention, which will generate more spatially independent subnets (such as Figure 4 The light-colored part shown in b corresponds to an independent subnet).

[0086]

[0087] In this embodiment, by performing a discrete search of a single elevation parameter, arcs with smaller height differences are selected for retention, thereby controlling the measurement points on the retained arcs for detection and constructing a constraint network.

[0088] like Figure 1 As shown, in one implementation of the embodiment of the present invention, the urban multi-temporal InSAR phase unwrapping method further includes the following steps:

[0089] Step S300, controlling the measurement points on the screened arc segment to detect, selecting measurement points with preset elevation values, and constructing a constraint network based on the selected measurement points.

[0090] In this embodiment, after the discrete search, the measurement points on the screened arc segments are controlled to perform detection; then, a constraint network is constructed according to the elevation values ​​at each measurement point.

[0091] Specifically, in an implementation of this embodiment, step S300 includes the following steps:

[0092] Step S301, detects each independent subnet, and performs adjustment processing on each independent subnet to obtain the elevation value on the corresponding measuring point;

[0093] Step S302, selecting the measurement points of the preset elevation values, and constructing the constraint network to constrain and connect the independent sub-networks.

[0094] In this embodiment, each independent subnetwork is detected, and each independent subnetwork is adjusted to obtain the elevation value at the measuring point, and the measuring point with a lower elevation value is selected to construct a constraint network to constrain and connect each independent subnetwork (such as Figure 4 In this way, the height information of each measurement point can be used to guide the propagation of the network path, so that the solution results can be propagated from the bottom of the building to the top (as shown in Figure 2). Figure 5 As shown in a Figure 5 As shown in b, the positions of the measurement points Figure 5 This avoids the problem of phase discontinuity in the interference pattern caused by dense buildings.

[0095] like Figure 1As shown, in one implementation of the embodiment of the present invention, the urban multi-temporal InSAR phase unwrapping method further includes the following steps:

[0096] Step S400, calculating the accuracy of the parameters to be estimated for each arc segment in the constraint network, and optimizing the spatial grid structure of the constraint network according to the accuracy.

[0097] In this embodiment, after integrating the constraint network and each independent subnetwork, the spatial grid structure of the constraint network can be optimized according to the accuracy of the estimated parameters of each arc segment on the network to obtain a spatial network after integrating each network, that is, a highly guided network (such as Figure 4 (shown in d).

[0098] Specifically, in an implementation of this embodiment, step S400 includes the following steps:

[0099] Step S401, calculating the accuracy of the estimated parameter of each arc segment in the constraint network according to the phase time coherence algorithm;

[0100] Step S402: screening redundant arcs in the constraint network according to the accuracy to obtain a network path that meets the accuracy requirement.

[0101] In this embodiment, after the constraint network and each independent subnetwork are integrated, the accuracy of the estimated parameter of each arc segment on the network is usually expressed by the temporal coherence index (TCI) of the phase. The TCI can be calculated by the following formula:

[0102]

[0103] in, is the temporal phase coherence (TCI), the higher the The higher the value, the higher the accuracy of the estimated parameter.

[0104] Usually when When The parameter estimation is correct. After all arcs are estimated, assuming that the residual phase The spatial network can be directly adjusted to obtain the unwrapped residual phase at each measurement point. By combining the estimated parameters and the unwrapped residual phase, the corresponding deformation time series and other information can be solved.

[0105] However, since the space free network has been used in step S101 to generate redundant arcs, the redundant arcs have arc estimation values ​​of different qualities, and the same measurement point can be connected by arcs of different qualities, which easily leads to the propagation of errors during spatial network adjustment. Therefore, filtering out high-quality networks from redundant arcs for spatial adjustment can reduce error propagation. Therefore, the spatial shortest path method can be used to filter out high-quality network paths from the redundant free network according to the quality of each arc, i.e., TCI value, and to adjust and estimate the unwrapped phase of all measurement points after completing spatial network optimization.

[0106] like Figure 1 As shown, in one implementation of the embodiment of the present invention, the urban multi-temporal InSAR phase unwrapping method further includes the following steps:

[0107] Step S500, obtaining the unwrapped phase of the measurement point in the optimized constraint network, performing error detection and correction on the unwrapped phase, and estimating the surface deformation information and elevation information of the study area.

[0108] In this embodiment, after obtaining the unwrapped phase at the measurement point, the unwrapped phase is subjected to error detection and correction. First, the phase jump is identified by using the closure value (temporal closure value, TCP) of the triangular closed network in the interferogram network of the unwrapped phase, and the phase ambiguity of the three interferometric phases on the triangular closed interferometric network is used to determine whether error detection and correction of the unwrapped phase at the test point is required.

[0109] Specifically, in an implementation of this embodiment, step S500 includes the following steps:

[0110] Step S501, performing adjustment processing on each measurement point in the optimized constraint network to estimate the unwrapped phase corresponding to each measurement point;

[0111] Step S502, identifying phase jumps by using the closed value of the triangular closed network in the interference pattern network of the unwrapped phase;

[0112] Step S503, correcting the unwrapping error according to the identified phase jump and the correction algorithm;

[0113] Step S504, obtaining the surface deformation information and elevation information of the study area according to the corrected unwrapped phase.

[0114] In this embodiment, in the process of identifying phase jumps, the closing value of the unwrapped phase on the triangular closed interference network and the number of ambiguities existing in the triangular closed interference pattern can be calculated by the following formula:

[0115] φ Triplet =Δφ i,j+Δφ j,k -Δφ i,k (6)

[0116] K amb =round[(φ Triplet -wrap(φ Triplet )) / 2π] (7)

[0117] Among them, Δφ i,k Unwrap the phase for the interferogram;

[0118] round[·] means the nearest integer;

[0119] φ Triplet Represents the closure value of the unwrapped phase on a triangular closed interferometer network.

[0120] K amb is the number of ambiguities in the triangular closed interference pattern, which is theoretically 0, i.e. K amb =0. If K amb If is a non-zero value, it means that there is phase ambiguity in the three interference phases on the triangular closed interference network.

[0121] However, a single closed loop can only indicate the presence or absence of ambiguity, but cannot correct it. However, by combining multiple closed loops of all interferometric networks in the entire time dimension, the unwrapping error can be corrected by the following formula:

[0122]

[0123] Among them, L Triplet represents a T×M matrix;

[0124] T and M are the number of triangular closed networks and the number of interference phases, respectively.

[0125] Since each interferometric phase has an unknown ambiguity, L Triplet The matrix may be rank-deficient and may need to be regularized to obtain a unique solution.

[0126] It is worth noting that the rank deficiency problem of formula (8) can be avoided if the interference pattern ambiguity is correctly detected. Therefore, the present invention uses the residual phase to identify a specific interference pattern with ambiguity.

[0127] Specifically, in an implementation of this embodiment, step S503 includes the following steps:

[0128] Step S503a, comparing the phase residual between the original wrapped phase and the unwrapped phase, performing ambiguity detection in combination with the closed value and the number of ambiguities, and identifying the ambiguity existing in the phase main value part;

[0129] Step S503b, using the least two phase residual phase, combined with the closed value and the number of ambiguities to perform ambiguity detection, and identify the ambiguities existing in the integer phase part;

[0130] Step S503c: correct the unwrapping error according to the identified ambiguity and the correction algorithm.

[0131] In this embodiment, in order to identify the ambiguity existing in the phase main value part, the phase residual Δψ between the original wrapped phase and the unwrapped phase can be compared. res , use formula (6) and formula (7) to perform ambiguity detection.

[0132] In order to identify the ambiguity existing in the integer phase part, the ambiguity detection is performed using the least two phase residual phase through formulas (6) and (7).

[0133] The phase residual Δψ between the original wrapped phase and the unwrapped phase res It can be calculated by the following formula:

[0134]

[0135] Among them, Δψ and Δφ represent the interference phase before and after unwrapping, respectively;

[0136] G represents the design matrix connecting the interference pattern and the phases of consecutive intervals.

[0137] Δψ res and Δφ res Substituting the following formula can calculate the fuzziness K amb . With non-zero K amb The triangular closed interference network is marked as an interference graph affected by the unwrapping error. When the phase residual corresponding to the interference graph satisfies the following threshold, it is considered that the interference graph has an unwrapping error.

[0138]

[0139] Where ε is the threshold value, theoretically ε = 1. After locating the interferogram with ambiguity, the phase ambiguity value of the corresponding interferogram is obtained by solving formula (8), and the unwrapping error is corrected by the following formula

[0140] Δφ corrected =Δφ+K amb *κ (11).

[0141] After obtaining the high-precision unwrapped phase at the measurement point, the surface deformation can be estimated. In this process, a large number of existing methods or models can be used to correct and compensate for various errors, such as orbit error, atmospheric error, and elevation error, so as to obtain more accurate surface deformation and elevation information.

[0142] This embodiment achieves the following technical effects through the above technical solution:

[0143] This embodiment proposes an MT-InSAR phase unwrapping method for urban area deformation monitoring. The method introduces a highly guided spatial network construction strategy to select high-quality propagation paths, thereby avoiding the impact of large and discontinuous changes in building heights on phase unwrapping. At the same time, in order to optimize the phase unwrapping quality, two quality control strategies are deployed, namely, a spatial network structure optimizer and a phase unwrapping error corrector, to suppress the accumulation and propagation of MT-InSAR phase unwrapping errors.

[0144] Exemplary Devices

[0145] Based on the above embodiments, the present invention also provides a terminal, comprising: a processor, a memory, an interface, a display screen and a communication module connected via a system bus; wherein the processor is used to provide computing and control capabilities; the memory includes a storage medium and an internal memory; the storage medium stores an operating system and a computer program; the internal memory provides an environment for the operation of the operating system and the computer program in the storage medium; the interface is used to connect external devices, such as mobile terminals and computers; the display screen is used to display corresponding information; and the communication module is used to communicate with a cloud server or a mobile terminal.

[0146] When the computer program is executed by the processor, it is used to implement the operation of a multi-temporal InSAR phase unwrapping method for urban areas.

[0147] It can be understood by those skilled in the art that Figure 6 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the terminal to which the scheme of the present invention is applied. The specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0148] In one embodiment, a terminal is provided, comprising: a processor and a memory, wherein the memory stores an urban multi-phase InSAR phase unwrapping program, and the urban multi-phase InSAR phase unwrapping program is used to implement the operation of the urban multi-phase InSAR phase unwrapping method as described above when executed by the processor.

[0149] In one embodiment, a storage medium is provided, wherein the storage medium stores an urban multi-phase InSAR phase unwrapping program, and when the urban multi-phase InSAR phase unwrapping program is executed by the processor, it is used to implement the operation of the urban multi-phase InSAR phase unwrapping method as described above.

[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory.

[0151] In summary, the present invention provides a multi-temporal InSAR phase unwrapping method, terminal and storage medium for urban areas, the method comprising: obtaining a number of registered radar images, generating a plurality of interference graphs according to a multi-main image interference network strategy, and calculating the preliminary unwrapping phase of the time dimension of the plurality of interference graphs; calculating the height difference on the corresponding arc segment, and screening the arc segment with a preset height difference; controlling the measurement points on the screened arc segment for detection, selecting the measurement points with preset elevation values, and constructing a constraint network according to the selected measurement points; calculating the accuracy of the parameters to be estimated for each arc segment in the constraint network, and optimizing the spatial grid structure of the constraint network according to the accuracy; obtaining the unwrapping phase of the measurement point, performing error detection and correction on the unwrapping phase, and estimating the surface deformation information and elevation information of the study area. The present invention can take into account the building height and the spatial network observation structure, and improve the quality of phase unwrapping in urban areas.

[0152] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A multi-temporal InSAR phase unwrapping method for urban areas, characterized in that: The urban multi-temporal InSAR phase unwrapping method comprises: Acquire a number of registered radar images, generate a plurality of interference patterns according to a multi-primary image interference network strategy, and calculate preliminary unwrapped phases of the time dimension of the plurality of interference patterns; Calculate the height difference on the corresponding arc segment according to the preliminary unwrapped phase, and select the arc segment with a preset height difference; Control the measurement points on the screened arc segment for detection, select measurement points with preset elevation values, and construct a constraint network based on the selected measurement points; Calculating the accuracy of the parameters to be estimated for each arc segment in the constraint network, and optimizing the spatial grid structure of the constraint network according to the accuracy; Obtaining the unwrapped phase of the measurement point in the optimized constraint network, performing error detection and correction on the unwrapped phase, and estimating the surface deformation information and elevation information of the study area; The step of obtaining the unwrapped phase of the measurement point in the optimized constraint network includes: Performing adjustment processing on each measuring point in the optimized constraint network to estimate the unwrapped phase corresponding to each measuring point; The error detection and correction of the unwrapped phase and the estimation of the surface deformation information and elevation information of the study area include: Using the closed value of the triangular closed network in the interference pattern network of the unwrapped phase to identify the phase jump; Correct the unwrapping error based on the identified phase jump and correction algorithm; Acquire the surface deformation information and elevation information of the study area according to the corrected unwrapped phase; The step of correcting the unwrapping error according to the identified phase jump and the correction algorithm includes: Comparing the phase residual between the original wrapped phase and the unwrapped phase, performing ambiguity detection in combination with the closure value and the number of ambiguities, and identifying the ambiguity existing in the phase main value part; Using the minimum two phase residual phase, combined with the closure value and the number of ambiguities, ambiguity detection is performed to identify ambiguities existing in the integer phase part; Correcting the unwrapping error according to the identified ambiguity and the correction algorithm; In the process of identifying the phase jump, the closing value of the unwrapping phase on the triangular closed network and the number of ambiguities existing in the triangular closed interference pattern are calculated, and the multiple closed loops of all interference networks in the entire time dimension are combined to correct the unwrapping error according to the number of ambiguities.

2. The urban multi-temporal InSAR phase unwrapping method according to claim 1, characterized in that: The step of calculating the preliminary unwrapped phases of the time dimension of the plurality of interference patterns comprises: Using a spatial free connection network to establish a redundant spatial network path; wherein the spatial network path is an arc segment formed according to a preset measurement point pair; The double difference phases on all interference patterns on each spatial network path are obtained to obtain the preliminary unwrapped phase.

3. The urban multi-temporal InSAR phase unwrapping method according to claim 1, characterized in that: The step of calculating the height difference on the corresponding arc segment according to the preliminary unwrapped phase and screening the arc segment with the preset height difference comprises: Performing a discrete search of a single elevation parameter on the arc segment retained by the preliminary unwrapping phase; The arc segments with the preset height difference are selected according to the search results to obtain a plurality of spatially independent sub-networks.

4. The urban multi-temporal InSAR phase unwrapping method according to claim 1, characterized in that: The control detects the measuring points on the arc segment after screening, selects the measuring points with preset elevation values, and constructs a constraint network according to the selected measuring points, including: Detect each independent subnet and perform adjustment on each independent subnet to obtain the elevation value at the corresponding measuring point; The measurement points of the preset elevation values ​​are selected, and the constraint network is constructed to constrain and connect the independent sub-networks.

5. The urban multi-temporal InSAR phase unwrapping method according to claim 1, characterized in that: The calculation of the accuracy of the estimated parameter of each arc segment in the constraint network and optimizing the spatial grid structure of the constraint network according to the accuracy include: Calculating the accuracy of the estimated parameters of each arc segment in the constraint network according to the phase temporal coherence algorithm; Redundant arcs in the constraint network are screened according to the accuracy to obtain a network path that meets the accuracy requirement.

6. A terminal, characterized in that: include: A processor and a memory, wherein the memory stores an urban multi-phase InSAR phase unwrapping program, and when the urban multi-phase InSAR phase unwrapping program is executed by the processor, it is used to implement the operation of the urban multi-phase InSAR phase unwrapping method as described in any one of claims 1 to 5.

7. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and the storage medium stores a multi-phase InSAR phase unwrapping program for urban areas. When the multi-phase InSAR phase unwrapping program for urban areas is executed by a processor, it is used to implement the operation of the multi-phase InSAR phase unwrapping method for urban areas as described in any one of claims 1 to 5.

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