CN111398959A - InSAR time sequence earth surface deformation monitoring method based on earth surface stress strain model - Google Patents

InSAR time sequence earth surface deformation monitoring method based on earth surface stress strain model Download PDF

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CN111398959A
CN111398959A CN202010265777.6A CN202010265777A CN111398959A CN 111398959 A CN111398959 A CN 111398959A CN 202010265777 A CN202010265777 A CN 202010265777A CN 111398959 A CN111398959 A CN 111398959A
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CN111398959B (en
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胡俊
刘计洪
孙倩
李志伟
朱建军
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Central South University
Hunan Normal University
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    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

The invention provides an InSAR time sequence earth surface deformation monitoring method based on an earth surface stress-strain model, which comprises the following steps: step 1, collecting a time sequence SAR image covering a region to be monitored, realizing registration and differential interference of the time sequence SAR image based on the existing software, and generating an interferogram meeting a preset space-time baseline threshold; and 2, constructing a local Dirony triangulation network based on all pixel points in the interference pattern. According to the method, the physical and mechanical relationship between the deformation of the near points of the earth surface is described by using the earth surface stress-strain model, the accuracy of the monitoring of the surface deformation of the InSAR time sequence can be obviously improved, modeling and parameter solving are carried out on the basis of the arc sections formed by the near points, so that the interference diagram does not need to be unwrapped, the data processing efficiency of the InSAR can be effectively improved, the physical and mechanical relationship of observation data on the space is considered, and the high-accuracy InSAR deformation monitoring result with large space coverage density can be obtained without masking the low coherence region in the interference diagram.

Description

InSAR time sequence earth surface deformation monitoring method based on earth surface stress strain model
Technical Field
The invention relates to the field of geodetic surveying based on a satellite interferometric synthetic aperture radar, in particular to an InSAR time sequence earth surface deformation monitoring method based on an earth surface stress strain model.
Background
Synthetic Aperture Radar (SAR, InSAR) technology can measure surface deformation using two SAR images. The time sequence InSAR technology (Multiple Temporal InSAR, MTInSAR) analyzes a series of SAR images in the time sequence, can obtain a time sequence earth surface deformation result of a research area, and can effectively weaken the influence of factors such as atmospheric delay, terrain residual error, coherent error and the like on the earth surface deformation result. The monitoring targets of MTInSAR technology are mainly classified into Permanent Scatterers (PS) and Distributed Scatterers (DS). The PS keeps higher signal-to-noise ratio in the whole detection period, but in a natural environment, the distribution density of the PS is extremely low, and the monitoring requirement is difficult to meet. The DS keeps a better signal-to-noise ratio on some short space-time baseline interferograms, the space-time change characteristics of adjacent pixels are similar, and the DS is high in distribution density (such as sparse vegetation areas, deserts and the like) and wide in application in natural environments.
When acquiring the time-series surface deformation at the DS target, the existing method often needs to perform complex and tedious mathematical optimization estimation (i.e. phase reconstruction) based on all possible interference pairs, or estimate point by point based on the least square criterion using the unwrapped short spatiotemporal baseline interferogram. Practice proves that the former has low efficiency in estimating surface deformation, and the latter needs to be unwound in advance, so that the monitoring result is easily influenced by factors such as unwinding errors and the like. In addition, the existing methods do not consider the physical and mechanical relationship between adjacent DSs when estimating the time sequence earth surface deformation, and a relatively accurate deformation result cannot be obtained in a low coherence region.
Disclosure of Invention
The invention provides an InSAR time sequence earth surface deformation monitoring method based on an earth surface stress-strain model, and aims to solve the problems that the traditional method only performs mathematical spatial filtering on high-coherence points, or performs unwrapping and masking on an interferogram and then performs point-by-point time sequence solving, does not consider the physical and mechanical relationship between adjacent high-coherence points, and is easy to cause lower spatial density of a monitoring result.
In order to achieve the above object, an embodiment of the present invention provides an InSAR time-series surface deformation monitoring method based on a surface stress-strain model, including:
step 1, collecting a time sequence SAR image covering a region to be monitored, realizing registration and differential interference of the time sequence SAR image based on the existing software, and generating an interferogram meeting a preset space-time baseline threshold;
step 2, constructing a local Dirony triangulation network based on all pixel points in the interference pattern;
step 3, establishing a functional relation between the time sequence deformation phase gradient of the target arc section and the interference phase difference of all the arc sections within the range of 1km × 1km around the target arc section on the basis of the earth surface stress strain model;
step 4, introducing robust estimation to eliminate the arc section containing 2 pi ambiguity, and finally realizing the resolving of the target arc section time sequence deformation phase difference based on a weighted least square criterion;
and 5, repeating the step 3 and the step 4 for each edge of the local Dirony triangulation network until the time sequence deformation phase difference on all the arc sections is resolved, and performing space integration on all the arc sections by taking a ground surface stable point or a known deformation point in the triangulation network as a reference to obtain a time sequence deformation result.
Wherein, the step 1 specifically comprises:
collecting a time sequence SAR image covering a T scene of an area to be monitored, realizing registration and differential interference of the time sequence SAR image based on the existing software, and finally generating M interferograms meeting a preset space-time baseline threshold.
Wherein, the step 2 specifically comprises:
based on all pixel points in the M interferograms, a local Dirony triangulation network is constructed, and the local Dirony triangulation network contains all pixels
Figure BDA0002441177580000021
And (3) arc sections, wherein the longest arc section is smaller than a preset threshold value of the length of the arc section, and the preset threshold value of the length of the arc section is determined according to the range of the assumed homogeneous region.
Wherein, the step 2 further comprises:
suppose a certain arc segment A in the constructed local Dirony triangulation network0Is at point PiAnd point PjTo a start point and a stop point, point PiAnd point PjThe corresponding three-dimensional coordinates are respectively
Figure BDA0002441177580000022
And
Figure BDA0002441177580000023
Figure BDA0002441177580000024
point PiAnd point PjThe three-dimensional surface deformation occurring between the SAR image acquisition moments corresponding to the mth interferogram is respectively
Figure BDA0002441177580000025
And
Figure BDA0002441177580000026
Figure BDA0002441177580000027
obtaining the following according to a surface stress strain model:
Figure BDA0002441177580000028
wherein the content of the first and second substances,
Figure BDA0002441177580000031
e represents east-west direction, n represents north-south direction, u represents vertical direction,
Figure BDA0002441177580000032
representative groundThe table stress strain model unknown parameter matrix can be expressed as:
Figure BDA0002441177580000033
wherein the content of the first and second substances,
Figure BDA00024411775800000313
representing partial derivatives, dm＝[dm，edm，ndm，u]TRepresenting the three-dimensional surface deformation field generated between the acquisition moments of the main SAR image and the auxiliary SAR image corresponding to the mth interferogram, wherein x is [ x ═ x [ ]exnxu]TRepresenting three-dimensional directions of east-west, north-south and vertical.
Wherein, the step 2 further comprises:
due to the point PiAnd point PjThe distance is short, the difference of the geometric visual angles of the two points when the SAR satellite observes the two points can be ignored, namely the point PiAnd point PjProjecting the three-dimensional deformation to a coefficient matrix B of the InSAR visual line deformationgeoAre identical, coefficient matrix BgeoAs follows:
Bgeo＝[a b c](3)
wherein a represents a projection coefficient of east-west deformation in InSAR sight line direction, b represents a projection coefficient of south-north deformation in InSAR sight line direction, and c represents a projection coefficient of vertical deformation in InSAR sight line direction, which can be determined according to the imaging geometry of SAR satellites;
the same sign of the formula (1) is multiplied by BgeoThe following formula is obtained:
Figure BDA0002441177580000035
wherein the content of the first and second substances,
Figure BDA0002441177580000036
respectively represent point PiAnd point PjThe projection deformation of the three-dimensional surface deformation along the InSAR visual line, namely in the mth frameThe observed values at two points in the interferogram are, in addition,
Figure BDA0002441177580000037
Figure BDA0002441177580000038
can be regarded as that the InSAR one-dimensional deformation observed value is in an arc section A0And deformation gradient parameters along east-west direction, north-south direction and vertical direction.
Wherein, the step 3 specifically comprises:
based on the surface stress-strain model, the arc segment A in the mth interferogram0Gradient of surface deformation in a certain spatial range
Figure BDA0002441177580000039
Can be assumed to be constant, and therefore, based on equation (4), the target arc segment A in the mth interferogram can be established0Deformation gradient of upper earth surface
Figure BDA00024411775800000310
All K arc sections A within a certain range of the peripherykComprising an arc segment A0Phase difference of deformation
Figure BDA00024411775800000311
Where (K ═ 1, 2.., K), yields:
Figure BDA00024411775800000312
wherein the content of the first and second substances,
Figure BDA0002441177580000041
Bsm＝[Δ1，Δ2，...，Δk，…，ΔK]T，Δkrepresents arc segment AkThe coordinate increment between the two end points is,
Figure BDA0002441177580000042
represents arc segment AkTwo end pointsAnd difference of the inter InSAR deformation observed values.
Wherein, the step 3 further comprises:
by taking the first SAR image of the M interferograms as a reference, a conversion matrix B between a deformation observation value corresponding to the InSAR interferogram and the time sequence earth surface deformation of the InSAR sight line is easy to obtaintrsWherein B istrsThe matrix size of the reference image is M × (T-1), each row corresponds to an interference pattern, each column corresponds to an SAR image, the reference image is removed, in each row, the columns corresponding to the M interference pattern main images are-1, the columns corresponding to the auxiliary images are +1, and other elements are 0;
based on formula (5) and matrix BtrsThen, the deformation phase difference delta L on all arc sections in the M interference patterns can be establisheddefoAnd arc segment A0Time-series earth surface deformation phase gradient
Figure BDA0002441177580000043
Functional relationship between:
Figure BDA0002441177580000044
wherein, Delta Ldefo＝[(ΔL1)T，(ΔL2)T，…，(ΔLm)T，…，(ΔLM)T]T，
Figure BDA0002441177580000045
Figure BDA0002441177580000046
Wherein T is 2, 3., T,
Figure BDA0002441177580000047
represents the sign of the kronecker product operation,
Figure BDA0002441177580000048
to know
Figure BDA0002441177580000049
Respectively representing that the InSAR sight line deforms to a phase at an arc section A when the t-th SAR image is obtained0Gradients in east-west, north-south and vertical directions;
target arc segment A0The actual InSAR observed value comprises a surface deformation phase difference
Figure BDA00024411775800000410
Figure BDA00024411775800000411
Involving terrain residual correlated phase
Figure BDA00024411775800000412
And a noise phase, wherein the target arc segment A0Phase of the terrain residual
Figure BDA00024411775800000413
Expressed as:
Figure BDA00024411775800000414
wherein the content of the first and second substances,
Figure BDA00024411775800000415
Figure BDA00024411775800000416
Figure BDA0002441177580000051
wherein the content of the first and second substances,
Figure BDA0002441177580000052
representing arc segment A in the mth interferogram0The phase of the terrain residual on top of the terrain,
Figure BDA0002441177580000053
representing the vertical base line, dz, of the mth interferogram0Represents arc segment A0The terrain residual on the satellite, λ, ρ, θ, respectively represent the wavelength of the satellite, the distance from the satellite to the target point, and the satellite incident angle at the target point.
Wherein, the step 3 further comprises:
assuming that the terrain residual correlation phase is spatially uncorrelated, arc segment AkThe above terrain residual phase can be regarded as noise, and a functional relation between the time sequence deformation phase gradient of the target arc segment and the interference phase difference delta L of all the arc segments within a certain range around can be established based on a surface stress strain model by combining the formula (6) and the formula (7):
Figure BDA0002441177580000054
wherein Δ L is the interference phase on the corresponding K arc segments in all M interferograms, which is obtained by the difference of the real observed phases of the corresponding arc segment end points, B ═ Bdefo，Bdz]，
Figure BDA0002441177580000055
Matrix BdzIs (M × K) × 1, matrix BdzNumber of rows and BdefoIn accordance with Δ L, matrix BdzAnd arc segment A of Δ L0The elements of the position corresponding to the upper interference phase difference observed value are respectively
Figure BDA0002441177580000056
The other elements are 0.
Wherein, the step 3 further comprises:
weighting different arc sections according to the coherence of the high coherence point, wherein the coherence is InSAR observed value of c, and the corresponding variance sigma2Can be expressed as:
Figure BDA0002441177580000057
easily-obtained InSAR observed value variance of two endpoints forming arc section
Figure BDA0002441177580000058
And
Figure BDA0002441177580000059
further, the variance of the difference between the InSAR observed values of two points can be obtained
Figure BDA00024411775800000510
Comprises the following steps:
Figure BDA00024411775800000511
ignoring InSAR observation value covariance between different arc sections, and obtaining a variance matrix D of an observation value vector delta L based on the expression (9) and the expression (10) and coherence values of all pointsΔLWherein D isΔLThe diagonal elements respectively correspond to the variance of InSAR observed values delta L on the arc sections, the other elements are 0, and the weight matrix P of the observed value vector delta LΔLCan be expressed as:
PΔL＝(DΔL)-1(11)。
wherein, the step 4 specifically comprises:
based on the equations (8) and (11), the initial solution of the unknown parameter vector can be solved based on the weighted least square criterion
Figure BDA0002441177580000061
Obtaining:
Figure BDA0002441177580000062
in actual data, the difference between the observed values of adjacent points may include 2 π ambiguity, that is, the observed value Δ L may include gross error, and the unknown parameter vector is subjected to reduction of the gross error
Figure BDA0002441177580000063
The coarse difference elimination is carried out according to the statistical characteristics of the observed value, namely when the correction number of the observed value is larger than a certain threshold value vthrThen, the corresponding observation is considered to be gross, where,vthrcalculated according to the following formula:
Figure BDA0002441177580000064
wherein the content of the first and second substances,
Figure BDA0002441177580000065
observed value correction v0Calculated according to the following formula:
Figure BDA0002441177580000066
culling the correction v in Δ L0＞vthrThe unknown parameter vector calculated again according to equation (12)
Figure BDA0002441177580000067
I.e. considered to be unaffected by the gross error, i.e. to obtain arc segment A0Difference dz between terrain residuals between top and bottom dead center0And time-series surface deformation gradients in east-west, north-south and vertical directions
Figure BDA0002441177580000068
Figure BDA0002441177580000069
To obtain an arc segment A0Two end points PiAnd point PjTime-series surface deformation phase difference therebetween
Figure BDA00024411775800000610
Figure BDA00024411775800000611
The scheme of the invention has the following beneficial effects:
according to the InSAR time sequence surface deformation monitoring method based on the surface stress-strain model, the surface stress-strain model is used for describing the physical mechanical relationship between the deformation of the adjacent points of the surface, the accuracy of the InSAR time sequence surface deformation monitoring can be obviously improved, modeling and parameter solving are carried out based on the arc sections formed by the adjacent points, so that the unwrapping of an interference graph is not needed, the InSAR data processing efficiency can be effectively improved, the physical mechanical relationship of observation data on the space is considered, and the high-accuracy InSAR deformation monitoring result with large space coverage density can be obtained without masking the low coherent region in the interference graph.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a time-series surface deformation diagram of SAR image acquisition time simulated by the present invention;
FIG. 3 is an atmospheric delay phase diagram of SAR image acquisition time simulated by the present invention;
FIG. 4 is a spatiotemporal baseline profile of a simulated SAR image of the present invention;
FIG. 5 is a simulated short baseline InSAR interferogram of the present invention;
FIG. 6 is a plot of the terrain residual phase in a simulated InSAR interferogram of the present invention;
FIG. 7 is a phase diagram of the loss of coherent noise in a simulated InSAR interferogram of the present invention;
FIG. 8 is a graph of the average deformation rate solved for the original simulated interferograms of the present invention for (a), (b) unmasked and (c), (d) masked cases (a), (c) the method of the present invention and (b), (d) the conventional method.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides an InSAR time sequence earth surface deformation monitoring method based on an earth surface stress strain model, aiming at the problems that the conventional method only performs mathematical spatial filtering on high-coherence points, or firstly unwinds and masks an interferogram and then performs point-by-point time sequence solving, the physical and mechanical relationship between adjacent high-coherence points is not considered, and the space density of a monitoring result is low easily caused.
As shown in fig. 1 to 8, an embodiment of the invention provides an InSAR time sequence earth surface deformation monitoring method based on an earth surface stress strain model, which comprises the steps of 1, collecting time sequence SAR images covering an area to be monitored, realizing registration and differential interference of the time sequence SAR images based on existing software, generating an interference map meeting a preset time-space baseline threshold, 2, constructing a local dironey triangulation network based on all pixel points in the interference map, 3, establishing a functional relation between a time sequence deformation phase gradient of a target arc section and interference phase differences of all arc sections within a range of 1km × 1km around the target arc section based on the earth surface stress strain model, 4, introducing robust estimation and eliminating the arc section containing 2 ambiguities, and finally realizing resolving of a target arc section time sequence deformation phase difference based on a weighted least square criterion, 5, repeating the steps 3 and 4 on each edge of the local dironey triangulation network until the resolving of the time sequence deformation phase differences on all arc sections is completed, and obtaining all time sequence deformation results by taking an earth surface stable point or a known integral deformation point in the triangulation network as a reference space.
In the method for monitoring the InSAR time sequence surface deformation based on the surface stress-strain model according to the embodiment of the invention, the surface stress-strain model describes a physical and mechanical relationship between the three-dimensional surface deformation of the surface near point, and a functional relationship between the InSAR one-dimensional time sequence deformation phase gradient of the target arc segment and the interference phase difference of all the arc segments within the range of 1km × 1km around the target arc segment can be established based on the surface stress-strain model through the geometric relationship between the InSAR observed value and the three-dimensional surface deformation.
Wherein, the step 1 specifically comprises: collecting a time sequence SAR image covering a T scene of an area to be monitored, realizing registration and differential interference of the time sequence SAR image based on the existing software, and finally generating M interferograms meeting a preset space-time baseline threshold.
In the method for monitoring the surface deformation of the InSAR time sequence based on the surface stress-strain model, the setting of the time-space baseline threshold can be determined according to the surface change condition of a specific research area, the orbit control of a new generation SAR satellite (such as a sentinel No. 1 satellite) is better, and the space baseline of a time-sequence SAR image in the same research area is often far smaller than the space incoherent baseline threshold, so the influence of the setting of the space baseline threshold on the selection of an interferogram is smaller, the time baseline threshold can be determined empirically according to the vegetation density degree of the research area, the wavelength of the SAR satellite and other factors, and under the general condition, the time baseline can be set to be 100 days, and the space baseline can be set to be 200 meters.
Wherein, the step 2 specifically comprises: based on all pixel points in the M interferograms, a local Dirony triangulation network is constructed, and the local Dirony triangulation network contains all pixels
Figure BDA0002441177580000086
And (3) arc sections, wherein the longest arc section is smaller than a preset threshold value of the length of the arc section, and the preset threshold value of the length of the arc section is determined according to the range of the assumed homogeneous region.
In the method for monitoring earth surface deformation based on the InSAR time sequence of the earth surface stress-strain model according to the embodiment of the invention, the preset threshold value of the arc length is generally selected according to the range of the homogeneous region, in general, the range of the homogeneous region is considered to be 1km × 1km, that is, the preset threshold value of the arc length is set to 1km, all two points meeting the threshold value are connected into a line, and finally the number of the connecting lines is the number of the connecting lines
Figure BDA0002441177580000087
Wherein, the step 2 further comprises: suppose a certain arc segment A in the constructed local Dirony triangulation network0Is at point PiAnd point PjTo a start point and a stop point, point PiAnd point PjThe corresponding three-dimensional coordinates are respectively
Figure BDA0002441177580000081
Figure BDA0002441177580000082
And
Figure BDA0002441177580000083
point PiAnd point PjThe three-dimensional surface deformation occurring between the SAR image acquisition moments corresponding to the mth interferogram is respectively
Figure BDA0002441177580000084
And
Figure BDA0002441177580000085
obtaining the following according to a surface stress strain model:
Figure BDA0002441177580000091
wherein the content of the first and second substances,
Figure BDA0002441177580000092
e represents east-west direction, n represents north-south direction, u represents vertical direction,
Figure BDA0002441177580000093
the unknown parameter matrix representing the surface stress-strain model can be expressed as:
Figure BDA0002441177580000094
wherein the content of the first and second substances,
Figure BDA0002441177580000095
representing partial derivatives, dm＝[dm，edm，ndm，u]TRepresenting the three-dimensional surface deformation field generated between the acquisition moments of the main SAR image and the auxiliary SAR image corresponding to the mth interferogram, wherein x is [ x ═ x [ ]exnxu]TRepresenting three-dimensional directions of east-west, north-south and vertical.
Wherein, the step 2 further comprises: due to the point PiAnd point PjThe distance is short, the difference of the geometric visual angles of the two points when the SAR satellite observes the two points can be ignored, namely the point PiAnd point PjProjecting the three-dimensional deformation to a coefficient matrix B of the InSAR visual line deformationgeoAre identical, coefficient matrix BgeoAs follows:
Bgeo＝[a b c](3)
wherein a represents a projection coefficient of east-west deformation in InSAR sight line direction, b represents a projection coefficient of south-north deformation in InSAR sight line direction, and c represents a projection coefficient of vertical deformation in InSAR sight line direction, which can be determined according to the imaging geometry of SAR satellites;
the same sign of the formula (1) is multiplied by BgeoThe following formula is obtained:
Figure BDA0002441177580000096
wherein the content of the first and second substances,
Figure BDA0002441177580000097
respectively represent point PiAnd point PjThe projection deformation of the three-dimensional surface deformation along the InSAR visual line, namely the observed value at two points in the mth interferogram, and in addition,
Figure BDA0002441177580000098
Figure BDA0002441177580000099
can be regarded as that the InSAR one-dimensional deformation observed value is in an arc section A0And deformation gradient parameters along east-west direction, north-south direction and vertical direction.
Wherein, the step 3 specifically comprises: based on the surface stress-strain model, the arc segment A in the mth interferogram0Gradient of surface deformation in a certain spatial range
Figure BDA00024411775800000910
Can be assumed to be constant, and therefore, based on equation (4), the target arc segment A in the mth interferogram can be established0Deformation gradient of upper earth surface
Figure BDA00024411775800000911
All K arc sections A within a certain range of the peripherykComprising an arc segment A0Phase difference of deformation
Figure BDA00024411775800000914
Where (K ═ 1, 2.., K), yields:
Figure BDA00024411775800000912
wherein the content of the first and second substances,
Figure BDA00024411775800000913
Bsm＝[Δ1，Δ2，…，Δk，…，ΔK]T，Δkrepresents arc segment AkThe coordinate increment between the two end points is,
Figure BDA0002441177580000101
represents arc segment AkAnd the difference value of InSAR deformation observed values between the two endpoints.
Wherein, the step 3 further comprises: by taking the first SAR image of the M interferograms as a reference, a conversion matrix B between a deformation observation value corresponding to the InSAR interferogram and the time sequence earth surface deformation of the InSAR sight line is easy to obtaintrsWherein B istrsThe matrix size of the reference image is M × (T-1), each row corresponds to an interference pattern, each column corresponds to an SAR image, the reference image is removed, in each row, the columns corresponding to the M interference pattern main images are-1, the columns corresponding to the auxiliary images are +1, and other elements are 0;
based on formula (5) and matrix BtrsThen, the deformation phase difference delta L on all arc sections in the M interference patterns can be establisheddefoAnd arc segment A0Time-series earth surface deformation phase gradient
Figure BDA0002441177580000102
Functional relationship between:
Figure BDA0002441177580000103
wherein, Delta Ldefo＝[(ΔL1)T，(ΔL2)T，…，(ΔLm)T，…，(ΔLM)T]T，
Figure BDA0002441177580000104
Figure BDA0002441177580000105
Wherein T is 2, 3., T,
Figure BDA0002441177580000106
represents the sign of the kronecker product operation,
Figure BDA0002441177580000107
and
Figure BDA0002441177580000108
respectively representing that the InSAR sight line deforms to a phase at an arc section A when the t-th SAR image is obtained0Gradients in east-west, north-south and vertical directions;
target arc segment A0The actual InSAR observed value comprises a surface deformation phase difference
Figure BDA0002441177580000109
Figure BDA00024411775800001010
Involving terrain residual correlated phase
Figure BDA00024411775800001011
And a noise phase, wherein the target arc segment A0Phase of the terrain residual
Figure BDA00024411775800001012
Expressed as:
Figure BDA00024411775800001013
wherein the content of the first and second substances,
Figure BDA00024411775800001014
Figure BDA00024411775800001015
Figure BDA00024411775800001016
wherein the content of the first and second substances,
Figure BDA00024411775800001017
representing arc segment A in the mth interferogram0The phase of the terrain residual on top of the terrain,
Figure BDA00024411775800001018
representing the vertical base line, dz, of the mth interferogram0Represents arc segment A0The terrain residual on the satellite, λ, ρ, θ, respectively represent the wavelength of the satellite, the distance from the satellite to the target point, and the satellite incident angle at the target point.
Wherein, the step 3 further comprises: assuming that the terrain residual correlation phase is spatially uncorrelated, arc segment AkThe above terrain residual phase can be regarded as noise, and a functional relation between the time sequence deformation phase gradient of the target arc segment and the interference phase difference delta L of all the arc segments within a certain range around can be established based on a surface stress strain model by combining the formula (6) and the formula (7):
Figure BDA0002441177580000111
wherein Δ L is the interference phase on the corresponding K arc segments in all M interferograms, which is obtained by the difference of the real observed phases of the corresponding arc segment end points, B ═ Bdefo，Bdz]，
Figure BDA0002441177580000112
Matrix BdzIs (M × K) × 1, matrix BdzNumber of rows and BdefoIn accordance with Δ L, matrix BdzAnd arc segment A of Δ L0The elements of the position corresponding to the upper interference phase difference observed value are respectively
Figure BDA0002441177580000113
The other elements are 0.
Wherein, the step 3 further comprises: weighting different arc sections according to the coherence of the high coherence point, wherein the coherence is InSAR observed value of c, and the corresponding variance sigma2Can be expressed as:
Figure BDA0002441177580000114
easily-obtained InSAR observed value variance of two endpoints forming arc section
Figure BDA0002441177580000115
And
Figure BDA0002441177580000116
further, the variance of the difference between the InSAR observed values of two points can be obtained
Figure BDA0002441177580000117
Comprises the following steps:
Figure BDA0002441177580000118
ignoring InSAR observation value covariance between different arc sections, and obtaining a variance matrix D of an observation value vector delta L based on the expression (9) and the expression (10) and coherence values of all pointsΔLWherein D isΔLThe diagonal elements respectively correspond to the variance of InSAR observed values delta L on the arc sections, the other elements are 0, and the weight matrix P of the observed value vector delta LΔLCan be expressed as:
PΔL＝(DΔL)-1(11)。
wherein, the step 4 specifically comprises: based on the equations (8) and (11), the initial solution of the unknown parameter vector can be solved based on the weighted least square criterion
Figure BDA00024411775800001111
Obtaining:
Figure BDA00024411775800001110
in actual data, the difference between the observed values of adjacent points may include 2 π ambiguity, that is, the observed value Δ L may include gross error, and the unknown parameter vector is subjected to reduction of the gross error
Figure BDA0002441177580000121
The coarse difference elimination is carried out according to the statistical characteristics of the observed value, namely when the correction number of the observed value is larger than a certain threshold value vthrWhen, the corresponding observed value is considered to be gross, where vthrCalculated according to the following formula:
Figure BDA0002441177580000122
wherein the content of the first and second substances,
Figure BDA0002441177580000123
observed value correction v0Calculated according to the following formula:
Figure BDA0002441177580000124
culling the correction v in Δ L0＞vthrThe unknown parameter vector calculated again according to equation (12)
Figure BDA0002441177580000125
I.e. considered to be unaffected by the gross error, i.e. to obtain arc segment A0Difference dz0 between top start and stop points in terrain residual and time-series surface deformation gradient along east-west direction, north-south direction and vertical direction
Figure BDA0002441177580000126
Figure BDA0002441177580000127
To obtain an arc segment A0Two end points PiAnd point PjTime-series surface deformation phase difference therebetween
Figure BDA0002441177580000128
Figure BDA0002441177580000129
The effect of the InSAR time-series surface deformation monitoring method based on the surface stress-strain model according to the above embodiment of the invention can be further illustrated by the following simulation experiments, wherein simulation data describes that ① simulates a surface deformation field of linear change at 10 moments in a certain area (the image size is 100 × 100) as shown in fig. 2, and simultaneously simulates corresponding atmospheric phases at 10 moments based on a fractal surface (the fractal dimension is 2.67) as shown in fig. 3, wherein the maximum value of the atmospheric phase is 1 radian, ② generates 30 interferograms as shown in fig. 5 in combination with a satellite time-space base line of sentinel-1A/B data as shown in fig. 4, wherein terrain residual phases uniformly distributed in an interval of [ -10m, 10m ] are added in the interferogram as shown in fig. 6, and lost coherent noise based on the coherence simulation of a real interferogram is shown in fig. 7.
When the time sequence earth surface deformation of the target arc section is solved, the terrain residual error is solved by using an assumed deformation model (such as a linear deformation model) firstly, then the terrain residual error phase is subtracted from an interference image, the time sequence earth surface deformation is solved by using the residual phase, only the observation phase on the target arc section is considered in the whole process, and the observation values on other arc sections in a certain range around the target arc section are not considered.
In the simulation experiment, the simulation data are solved by using the method and the traditional method respectively under the conditions that the original simulation interferogram is not masked and pixels with average coherence lower than 0.6 are masked. In the solution result, because the method considers the physical mechanical relationship between the deformation of the near points of the earth surface and simultaneously adopts robust estimation to carry out gross error rejection, the method can also obtain the accurate time sequence earth surface deformation result even in the unmasked low-coherence region as shown in the (a) and (c) of the figure 8. In contrast, the conventional method can only obtain more reliable deformation results in the high-coherence region as shown in fig. 8(b) and (d). On the basis, the root mean square error of the time series deformation obtained by the two methods is shown in table 1:
TABLE 1 root mean square error of InSAR time series deformation
Figure BDA0002441177580000131
It can be seen from table 1 and fig. 8 that the algorithm of the present invention can obtain a more accurate and more complete temporal deformation field of InSAR with spatial coverage compared to the conventional algorithm.
According to the InSAR time sequence earth surface deformation monitoring method based on the earth surface stress-strain model, the earth surface stress-strain model with physical and mechanical significance is introduced to solve the time sequence earth surface deformation, the solving precision and the space coverage density of the time sequence earth surface deformation are further improved, meanwhile, the InSAR time sequence earth surface deformation monitoring method based on the earth surface stress-strain model does not need to be subjected to interference pattern unwrapping, and time sequence earth surface deformation estimation errors caused by interference pattern unwrapping errors are effectively weakened.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. An InSAR time sequence earth surface deformation monitoring method based on an earth surface stress strain model is characterized by comprising the following steps:
step 1, collecting a time sequence SAR image covering a region to be monitored, realizing registration and differential interference of the time sequence SAR image based on the existing software, and generating an interferogram meeting a preset space-time baseline threshold;
step 2, constructing a local Dirony triangulation network based on all pixel points in the interference pattern;
step 3, establishing a functional relation between the time sequence deformation phase gradient of the target arc section and the interference phase difference of all the arc sections within the range of 1km × 1km around the target arc section on the basis of the earth surface stress strain model;
step 4, introducing robust estimation to eliminate the arc section containing 2 pi ambiguity, and finally realizing the resolving of the target arc section time sequence deformation phase difference based on a weighted least square criterion;
and 5, repeating the step 3 and the step 4 for each edge of the local Dirony triangulation network until the time sequence deformation phase difference on all the arc sections is resolved, and performing space integration on all the arc sections by taking a ground surface stable point or a known deformation point in the triangulation network as a reference to obtain a time sequence deformation result.
2. The InSAR time-series earth surface deformation monitoring method based on the earth surface stress-strain model as claimed in claim 1, wherein the step 1 specifically comprises:
collecting a time sequence SAR image covering a T scene of an area to be monitored, realizing registration and differential interference of the time sequence SAR image based on the existing software, and finally generating M interferograms meeting a preset space-time baseline threshold.
3. The InSAR time-series earth surface deformation monitoring method based on the earth surface stress-strain model as claimed in claim 2, wherein the step 2 specifically comprises:
based on all pixel points in the M interferograms, a local Dirony triangulation network is constructed, and the local Dirony triangulation network contains all pixels
Figure FDA0002441177570000016
And (3) arc sections, wherein the longest arc section is smaller than a preset threshold value of the length of the arc section, and the preset threshold value of the length of the arc section is determined according to the range of the assumed homogeneous region.
4. The InSAR time-series surface deformation monitoring method based on the surface stress-strain model as claimed in claim 3, wherein the step 2 further comprises:
suppose a certain arc segment A in the constructed local Dirony triangulation network0Is at point PiAnd point PjTo a start point and a stop point, point PiAnd point PjThe corresponding three-dimensional coordinates are respectively
Figure FDA0002441177570000011
And
Figure FDA0002441177570000012
Figure FDA0002441177570000013
point PiAnd point PjThe three-dimensional surface deformation occurring between the SAR image acquisition moments corresponding to the mth interferogram is respectively
Figure FDA0002441177570000014
And
Figure FDA0002441177570000015
Figure FDA0002441177570000021
obtaining the following according to a surface stress strain model:
Figure FDA0002441177570000022
wherein the content of the first and second substances,
Figure FDA0002441177570000023
e represents east-west direction, n represents north-south direction, u represents vertical direction,
Figure FDA0002441177570000024
the unknown parameter matrix representing the surface stress-strain model can be expressed as:
Figure FDA0002441177570000025
wherein the content of the first and second substances,
Figure FDA00024411775700000210
representing partial derivatives, dm＝[dm，edm，ndm，u]TRepresenting the three-dimensional surface deformation field generated between the acquisition moments of the main SAR image and the auxiliary SAR image corresponding to the mth interferogram, wherein x is [ x ═ x [ ]exnxu]TRepresenting three-dimensional directions of east-west, north-south and vertical.
5. The InSAR time-series surface deformation monitoring method based on the surface stress-strain model as claimed in claim 4, wherein the step 2 further comprises:
due to the point PiAnd point PjThe distance is short, the difference of the geometric visual angles of the two points when the SAR satellite observes the two points can be ignored, namely the point PiAnd point PjProjecting the three-dimensional deformation to a coefficient matrix B of the InSAR visual line deformationgeoAre identical, coefficient matrix BgeoAs follows:
Bgeo＝[a b c](3)
wherein a represents a projection coefficient of east-west deformation in InSAR sight line direction, b represents a projection coefficient of south-north deformation in InSAR sight line direction, and c represents a projection coefficient of vertical deformation in InSAR sight line direction, which can be determined according to the imaging geometry of SAR satellites;
the same sign of the formula (1) is multiplied by BgeoThe following formula is obtained:
Figure FDA0002441177570000026
wherein the content of the first and second substances,
Figure FDA0002441177570000027
respectively represent point PiAnd point PjProjection deformation of three-dimensional surface deformation along InSAR visual line, namely in m-th interferenceThe observed values at two points in the graph, in addition,
Figure FDA0002441177570000028
Figure FDA0002441177570000029
can be regarded as that the InSAR one-dimensional deformation observed value is in an arc section A0And deformation gradient parameters along east-west direction, north-south direction and vertical direction.
6. The InSAR time-series earth surface deformation monitoring method based on the earth surface stress-strain model as claimed in claim 5, wherein the step 3 specifically comprises:
based on the surface stress-strain model, the arc segment A in the mth interferogram0Gradient of surface deformation in a certain spatial range
Figure FDA0002441177570000031
Can be assumed to be constant, and therefore, based on equation (4), the target arc segment A in the mth interferogram can be established0Deformation gradient of upper earth surface
Figure FDA00024411775700000316
All K arc sections A within a certain range of the peripherykComprising an arc segment A0Phase difference of deformation
Figure FDA0002441177570000032
Where (K ═ 1, 2.., K), yields:
Figure FDA0002441177570000033
wherein the content of the first and second substances,
Figure FDA0002441177570000034
Bsm＝[Δ1，Δ2，...，Δk，...，ΔK]T，Δkrepresents arc segment AkThe coordinate increment between the two end points is,
Figure FDA0002441177570000035
represents arc segment AkAnd the difference value of InSAR deformation observed values between the two endpoints.
7. The InSAR time-series surface deformation monitoring method based on the surface stress-strain model as claimed in claim 6, wherein the step 3 further comprises:
by taking the first SAR image of the M interferograms as a reference, a conversion matrix B between a deformation observation value corresponding to the InSAR interferogram and the time sequence earth surface deformation of the InSAR sight line is easy to obtaintrsWherein B istrsThe matrix size of the reference image is M × (T-1), each row corresponds to an interference pattern, each column corresponds to an SAR image, the reference image is removed, in each row, the columns corresponding to the M interference pattern main images are-1, the columns corresponding to the auxiliary images are +1, and other elements are 0;
based on formula (5) and matrix BtrsThen, the deformation phase difference delta L on all arc sections in the M interference patterns can be establisheddefoAnd arc segment A0Time-series earth surface deformation phase gradient
Figure FDA0002441177570000036
Functional relationship between:
Figure FDA0002441177570000037
wherein, Delta Ldefo＝[(ΔL1)T，(ΔL2)T，…，(ΔLm)T，…，(ΔLM)T]T，
Figure FDA0002441177570000038
Figure FDA0002441177570000039
Wherein T is 2, 3., T,
Figure FDA00024411775700000310
represents the sign of the kronecker product operation,
Figure FDA00024411775700000311
and
Figure FDA00024411775700000312
respectively representing that the InSAR sight line deforms to a phase at an arc section A when the t-th SAR image is obtained0Gradients in east-west, north-south and vertical directions;
target arc segment A0The actual InSAR observed value comprises a surface deformation phase difference
Figure FDA00024411775700000313
Figure FDA00024411775700000314
Involving terrain residual correlated phase
Figure FDA00024411775700000315
And a noise phase, wherein the target arc segment A0Phase of the terrain residual
Figure FDA0002441177570000041
Expressed as:
Figure FDA0002441177570000042
wherein the content of the first and second substances,
Figure FDA0002441177570000043
Figure FDA0002441177570000044
Figure FDA0002441177570000045
wherein the content of the first and second substances,
Figure FDA0002441177570000046
representing arc segment A in the mth interferogram0The phase of the terrain residual on top of the terrain,
Figure FDA0002441177570000047
representing the vertical base line, dz, of the mth interferogram0Represents arc segment A0The terrain residual on the satellite, λ, ρ, θ, respectively represent the wavelength of the satellite, the distance from the satellite to the target point, and the satellite incident angle at the target point.
8. The InSAR time-series surface deformation monitoring method based on the surface stress-strain model as claimed in claim 7, wherein the step 3 further comprises:
assuming that the terrain residual correlation phase is spatially uncorrelated, arc segment AkThe above terrain residual phase can be regarded as noise, and a functional relation between the time sequence deformation phase gradient of the target arc segment and the interference phase difference delta L of all the arc segments within a certain range around can be established based on a surface stress strain model by combining the formula (6) and the formula (7):
ΔL＝B·x (8)
wherein, the delta L is the interference phase on the corresponding K arc segments in all M interferograms, and is obtained by making difference of the real observed phases of the end points of the corresponding arc segments,
Figure FDA0002441177570000048
matrix BdzIs (M × K) × 1, matrix BdzNumber of rows and BdefoIn accordance with Δ L, matrix BdzAnd arc segment A of Δ L0The elements of the position corresponding to the upper interference phase difference observed value are respectively
Figure FDA0002441177570000049
The other elements are 0.
9. The method for monitoring InSAR time-series earth surface deformation based on the earth surface stress-strain model in claim 8, wherein the step 3 further comprises:
weighting different arc sections according to the coherence of the high coherence point, wherein the coherence is InSAR observed value of c, and the corresponding variance sigma2Can be expressed as:
Figure FDA0002441177570000051
easily-obtained InSAR observed value variance of two endpoints forming arc section
Figure FDA0002441177570000052
And
Figure FDA0002441177570000053
further, the variance of the difference between the InSAR observed values of two points can be obtained
Figure FDA0002441177570000054
Comprises the following steps:
Figure FDA0002441177570000055
ignoring InSAR observation value covariance between different arc sections, and obtaining a variance matrix D of an observation value vector delta L based on the expression (9) and the expression (10) and coherence values of all pointsΔLWherein D isΔLThe diagonal elements respectively correspond to the variance of InSAR observed values delta L on the arc sections, the other elements are 0, and the weight matrix P of the observed value vector delta LΔLCan be expressed as:
PΔL＝(D△L)-1(11)。
10. the InSAR temporal surface deformation monitoring method based on the surface stress-strain model according to claim 9, wherein the step 4 specifically includes:
based on the equations (8) and (11), the initial solution x of the unknown parameter vector can be solved based on the weighted least square criterion0Obtaining:
x0＝(BT·PΔL·B)-1·BT·PΔL·ΔL (12)
in actual data, the difference value of the observed values of adjacent points may include 2 pi ambiguity, that is, the observed value Δ L may include gross errors, and in order to reduce the influence of the gross errors on the unknown parameter vector x, gross error elimination is performed according to the statistical characteristics of the observed values, that is, when the correction number of the observed values is greater than a certain threshold value vthrWhen, the corresponding observed value is considered to be gross, where vthrCalculated according to the following formula:
Figure FDA0002441177570000056
wherein D isx＝(BT·PΔL·B)-1，
Observed value correction v0Calculated according to the following formula:
v0＝B·x0-ΔL (14)
culling the correction v in Δ L0＞vthrThe unknown parameter vector calculated again according to equation (12)
Figure FDA0002441177570000057
I.e. considered to be unaffected by the gross error, i.e. to obtain arc segment A0Difference dz between terrain residuals between top and bottom dead center0And time-series surface deformation gradients in east-west, north-south and vertical directions
Figure FDA0002441177570000058
Figure FDA0002441177570000059
Figure FDA0002441177570000061
To obtain an arc segment A0Two end points PiAnd point PjTime-series surface deformation phase difference therebetween
Figure FDA0002441177570000062
Figure FDA0002441177570000063
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112797886A (en) * 2021-01-27 2021-05-14 中南大学 Winding phase oriented InSAR time sequence three-dimensional deformation monitoring method
CN112835043A (en) * 2021-01-06 2021-05-25 中南大学 Method for monitoring two-dimensional deformation in any direction
CN113281744A (en) * 2021-03-11 2021-08-20 中南大学 Time sequence InSAR method based on hypothesis test and self-adaptive deformation model
CN114814839A (en) * 2022-03-22 2022-07-29 武汉大学 InSAR phase gradient stacking-based wide-area earth surface deformation detection method and system
CN115201823A (en) * 2022-07-22 2022-10-18 电子科技大学 Surface deformation monitoring method by utilizing BDS-InSAR data fusion
CN116338690A (en) * 2023-03-28 2023-06-27 中南林业科技大学 Model constraint-free time sequence InSAR terrain residual error and earth surface deformation estimation method

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005010556A1 (en) * 2003-07-18 2005-02-03 University Of Nottingham Radar position and movement measurement for geophysical monitoring
CA2652639A1 (en) * 2008-02-06 2009-08-06 Halliburton Energy Services, Inc. Geodesy via gps and insar integration
WO2011154804A1 (en) * 2010-06-07 2011-12-15 Universitat Politècnica De Catalunya Method for estimating the topography of the earth's surface in areas with plant cover
US20150042510A1 (en) * 2012-11-07 2015-02-12 Neva Ridge Technologies SAR Point Cloud Generation System
US20160033639A1 (en) * 2014-08-04 2016-02-04 University Of Seoul Industry Cooperation Foundation Method and apparatus for stacking multi-temporal mai interferograms
CN108594224A (en) * 2018-03-30 2018-09-28 中国电力工程顾问集团中南电力设计院有限公司 Merge the three-D sequential deformation monitoring method of different platform and track SAR data
CN109061641A (en) * 2018-07-06 2018-12-21 中南大学 A kind of InSAR timing earth's surface deformation monitoring method based on sequential adjustment
CN109884635A (en) * 2019-03-20 2019-06-14 中南大学 The InSAR Deformation Monitoring Data processing method of large scale and high accuracy
CN110456345A (en) * 2019-06-28 2019-11-15 深圳市水务规划设计院股份有限公司 A kind of building inclination monitoring method based on InSAR technology
US20190353779A1 (en) * 2018-05-15 2019-11-21 University Of Electronic Science And Technology Of China Ground-based interferometric synthetic aperture radar-based atmospheric phase compensation method
CN110888130A (en) * 2019-10-30 2020-03-17 华东师范大学 Coal mine area ground surface deformation monitoring method based on lifting rail time sequence InSAR

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005010556A1 (en) * 2003-07-18 2005-02-03 University Of Nottingham Radar position and movement measurement for geophysical monitoring
CA2652639A1 (en) * 2008-02-06 2009-08-06 Halliburton Energy Services, Inc. Geodesy via gps and insar integration
US20090237297A1 (en) * 2008-02-06 2009-09-24 Halliburton Energy Services, Inc. Geodesy Via GPS and INSAR Integration
WO2011154804A1 (en) * 2010-06-07 2011-12-15 Universitat Politècnica De Catalunya Method for estimating the topography of the earth's surface in areas with plant cover
US20150042510A1 (en) * 2012-11-07 2015-02-12 Neva Ridge Technologies SAR Point Cloud Generation System
US20160033639A1 (en) * 2014-08-04 2016-02-04 University Of Seoul Industry Cooperation Foundation Method and apparatus for stacking multi-temporal mai interferograms
CN108594224A (en) * 2018-03-30 2018-09-28 中国电力工程顾问集团中南电力设计院有限公司 Merge the three-D sequential deformation monitoring method of different platform and track SAR data
US20190353779A1 (en) * 2018-05-15 2019-11-21 University Of Electronic Science And Technology Of China Ground-based interferometric synthetic aperture radar-based atmospheric phase compensation method
CN109061641A (en) * 2018-07-06 2018-12-21 中南大学 A kind of InSAR timing earth's surface deformation monitoring method based on sequential adjustment
CN109884635A (en) * 2019-03-20 2019-06-14 中南大学 The InSAR Deformation Monitoring Data processing method of large scale and high accuracy
CN110456345A (en) * 2019-06-28 2019-11-15 深圳市水务规划设计院股份有限公司 A kind of building inclination monitoring method based on InSAR technology
CN110888130A (en) * 2019-10-30 2020-03-17 华东师范大学 Coal mine area ground surface deformation monitoring method based on lifting rail time sequence InSAR

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
J. HU ET AL.: "Resolving three-dimensional surface displacements from InSAR measurements: A review", 《EARTH-SCIENCE REVIEWS》 *
J. HU ET AL.: "Resolving three-dimensional surface displacements from InSAR measurements: A review", 《EARTH-SCIENCE REVIEWS》, 3 March 2014 (2014-03-03), pages 1 - 17 *
朱建军 等: "INSAR矿区地表三维形变监测与预计研究进展", 测绘学报, vol. 48, no. 2, pages 135 - 144 *
葛大庆等: "基于相干点目标的多基线D-InSAR技术与地表形变监测", 《遥感学报》 *
葛大庆等: "基于相干点目标的多基线D-InSAR技术与地表形变监测", 《遥感学报》, vol. 11, no. 04, 15 July 2007 (2007-07-15), pages 574 - 580 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112835043A (en) * 2021-01-06 2021-05-25 中南大学 Method for monitoring two-dimensional deformation in any direction
CN112797886A (en) * 2021-01-27 2021-05-14 中南大学 Winding phase oriented InSAR time sequence three-dimensional deformation monitoring method
CN112797886B (en) * 2021-01-27 2022-04-22 中南大学 Winding phase oriented InSAR time sequence three-dimensional deformation monitoring method
CN113281744A (en) * 2021-03-11 2021-08-20 中南大学 Time sequence InSAR method based on hypothesis test and self-adaptive deformation model
CN114814839A (en) * 2022-03-22 2022-07-29 武汉大学 InSAR phase gradient stacking-based wide-area earth surface deformation detection method and system
CN114814839B (en) * 2022-03-22 2024-05-03 武汉大学 Wide area earth surface deformation detection method and system based on InSAR phase gradient stacking
CN115201823A (en) * 2022-07-22 2022-10-18 电子科技大学 Surface deformation monitoring method by utilizing BDS-InSAR data fusion
CN115201823B (en) * 2022-07-22 2023-08-04 电子科技大学 Ground surface deformation monitoring method utilizing BDS-InSAR data fusion
CN116338690A (en) * 2023-03-28 2023-06-27 中南林业科技大学 Model constraint-free time sequence InSAR terrain residual error and earth surface deformation estimation method
CN116338690B (en) * 2023-03-28 2024-01-16 中南林业科技大学 Model constraint-free time sequence InSAR terrain residual error and earth surface deformation estimation method

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