CN111998766A - Surface deformation inversion method based on time sequence InSAR technology - Google Patents

Surface deformation inversion method based on time sequence InSAR technology Download PDF

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CN111998766A
CN111998766A CN202010897015.8A CN202010897015A CN111998766A CN 111998766 A CN111998766 A CN 111998766A CN 202010897015 A CN202010897015 A CN 202010897015A CN 111998766 A CN111998766 A CN 111998766A
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CN111998766B (en
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李博峰
柳虹宇
郭海京
瞿申润
高绵新
区永洪
王斌
岳志成
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SURVEYING AND MAPPING INSTITUTE LANDS AND RESOURCE DEPARTMENT OF GUANGDONG PROVINCE
Tongji University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
    • G01B7/16—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge
    • G01B7/24—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge using change in magnetic properties
    • G—PHYSICS
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/885—Radar or analogous systems specially adapted for specific applications for ground probing
    • G—PHYSICS
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
    • G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method

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Abstract

The invention relates to a surface deformation inversion method based on a time sequence InSAR technology, which comprises the following steps: geocoding and image registration, generating a differential interference pair, selecting PS points, performing space-time unwrapping and error separation. Specifically, in an image registration mode, coarse registration and fine registration are adopted, so that the registration accuracy requirement in a TOPS imaging mode is ensured; for the atmospheric delay error, processing the layered atmosphere and the floating atmosphere respectively by using different algorithms, so as to weaken the influence of the atmospheric error to the maximum extent and improve the precision of deformation inversion; if the GNSS data are provided, the observation data can be used for correcting the atmospheric delay, and meanwhile, the combined adjustment is carried out by taking the observation data as a constraint condition in an InSAR observation equation, so that the precision and the reliability of a deformation result are ensured, and all-weather, large-area, continuous and high-spatial-resolution ground surface deformation monitoring is realized.

Description

Surface deformation inversion method based on time sequence InSAR technology
Technical Field
The invention relates to the field of surface deformation monitoring, in particular to a surface deformation inversion method based on a time sequence InSAR technology.
Background
In recent years, geological disasters caused by surface deformation have received much attention due to natural factors including external forces such as crustal movement, lithology, folds, and fractured structures, and artificial factors due to excessive mining of groundwater and minerals. Serious surface deformation can cause serious loss to the society and economy, so that the development of deformation monitoring and inversion has important significance.
The traditional earth surface deformation monitoring method, particularly the earthquake co-seismic deformation field, urban ground settlement and mining area earth surface settlement are monitored by primary and secondary precision levels or GNSS. The precise leveling measurement is suitable for the engineering measurement range with smaller area, is widely applied to early urban settlement monitoring, and along with the development of the technology, the GNSS technology with high precision, rapid network distribution and short period is also successfully applied to deformation monitoring. However, no matter the leveling technology or the GNSS technology, the traditional monitoring method still has obvious defects which are mainly reflected in that (1) an observation point is easily damaged, so that the subsequent observation result is lost; (2) the requirement of large-range monitoring cannot be met only by observing the point line. By adopting point-line monitoring, interpolation is usually needed to estimate the overall deformation trend, so that the distribution and density of monitoring points greatly influence the precision and reliability of monitoring results, and if the monitoring points are too sparse, the deformation surface obtained by interpolation cannot effectively reflect the overall trend; (3) the traditional observation method has the disadvantages of high labor intensity, long time consumption and low working efficiency; (4) for complex terrain areas that are not accessible by human power, fewer monitoring points are available. Therefore, how to provide a surface deformation inversion method has important significance in making up for the defects of the traditional monitoring means and monitoring the surface deformation in a large range and high efficiency.
The synthetic aperture radar interferometry (InSAR) technology is an emerging spatial geodetic measurement technology, and compared with the traditional earth surface deformation monitoring means, the synthetic aperture radar interferometry technology has great progress in the aspects of monitoring range, monitoring efficiency and the like. The InSAR is widely applied to civil and military monitoring fields such as earthquake, volcano, landslide, water and soil loss and the like at present by combining a high-resolution imaging technology, a synthetic aperture radar technology and an interferometric measurement technology. The technology can solve the problems of low space density, long operation period and the like of monitoring networks faced by the traditional measurement such as level and GNSS monitoring, can acquire earth surface deformation information in all weather, large area, continuous and high spatial resolution, and realizes global monitoring.
Because the traditional differential interferometry (DInSAR) technology monitors deformation information between two imaging moments, observation of two images can be limited by factors such as different atmospheric environments, space baselines, scattering body incoherent and the like under a long time, and the measurement precision of the technology is limited. The spatial-temporal decorrelation is that the noise added to the two imaging processes is different or irrelevant, the phase difference cannot be counteracted, so that the signal-to-noise ratio is low, the interference pattern is not obvious, and the final deformation inversion precision is influenced; atmospheric delay is reflected in variable atmospheric conditions, which lead to different phase delays, possibly masking deformation or topographic information. Therefore, how to overcome the above-mentioned temporal-spatial decorrelation, weaken the influence of atmospheric delay on deformation information extraction, and utilize the SAR image data to the maximum extent to establish a differential interferogram of a long-time sequence is a problem to be solved.
The timing InSAR technologies such as PSInSAR (permanent Scatter Interferrometry) and SBAS (Small Baseline subset) reduce the influence of time-space loss correlation by selecting a high coherence point and adopting a short Baseline. However, with the development of a satellite-borne SAR platform, different imaging modes also put higher requirements on image registration, and the common polynomial registration method cannot meet the precision requirement. Meanwhile, for the processing of atmospheric errors, the existing filtering processing method does not consider the different characteristics of the layered atmosphere and the floating atmosphere to be respectively processed. In addition, considering the complementary characteristics of GNSS and InSAR data in terms of time and space resolution, how to fuse the two technologies to obtain a high-precision deformation sequence is also a problem worthy of solving.
Disclosure of Invention
In view of the above, in order to overcome the defects in the prior art, the present application provides the following technical solutions.
A surface deformation inversion method based on a time sequence InSAR technology comprises the following steps:
s1, geocoding and image registration, wherein an external DEM is used for geometric registration, correlation registration is carried out based on the amplitude and intensity information of the image, and the fine registration of spectral diversity is enhanced;
s2, generating a differential interference pair;
s3, selecting a PS point, and performing iterative selection of the PS point by respectively using the image intensity and the phase information;
s4, performing space-time unwrapping, increasing time dimension on the basis of a two-dimensional plane to form a three-dimensional space search path, and determining residual points to perform three-dimensional unwrapping;
and S5, error separation, namely, carrying out atmospheric error separation by adopting parameterization and space-time filtering modes respectively for layered atmosphere and floating atmosphere.
Further, if the GNSS data are provided, the surface deformation inversion method further comprises GNSS-InSAR fusion, which is mainly embodied in atmospheric delay correction and InSAR data adjustment with GNSS constraint.
Preferably, in step S1, geometric registration is performed by using external DEM, specifically:
converting the external DEM into an image coordinate system of a main image, establishing a lookup table, then calculating the position of a certain pixel through satellite orbit parameters, setting a proper window size in the main image, uniformly selecting pixel points, performing simultaneous solution on the coordinates of the pixel points through a distance equation, a Doppler equation and an earth model equation, and inversely calculating the coordinates of the pixel points in an auxiliary image coordinate system;
and calculating the offsets of all the pixel points by utilizing Delaunay triangulation network interpolation, and correcting the secondary image by utilizing a final offset file.
Preferably, in step S1, the relevant registration is performed based on the amplitude and intensity information of the image, specifically:
selecting a proper window in the main image, and calculating the coherence coefficient of the window;
utilizing maximum likelihood estimation to carry out offset between the main image and the auxiliary image;
and obtaining a conversion matrix according to the calculated offset, carrying out registration on the secondary image, and simultaneously ensuring that the registration precision reaches 0.05 pixel.
Preferably, the fine registration for enhancing spectral diversity in step S1 specifically:
extracting an overlapping area between the main image and the resampled auxiliary image burst;
the main and auxiliary images are interfered to form a front view and a rear view of an overlapped area, and are interfered again and filtered;
estimating ESD phase and registration errors;
and fitting all the deviation of the overlapping areas by using a polynomial to calculate the azimuth offset.
Preferably, the step S2 of generating the differential interference pair includes:
the time base line, the space base line and the frequency base line formed by the main image and the secondary image are important factors influencing the interference effect, and in order to select the optimal public main image, the sum of the correlation coefficients of the time sequence interference pattern needs to be maximized:
Figure BDA0002658779130000031
in the formula, f (gamma)t)，f(γB)，f(γDC) Respectively as time base line coefficient, vertical base line coefficient and Doppler mass center frequency difference coefficient, finding out optimum main imageForming an interference pair;
and the external DEM is transferred to a radar coordinate system to form a terrain phase, and the terrain phase and the flat ground phase are subtracted from the interference phase to form a differential interference pair.
Preferably, the step S3 of selecting the PS point includes:
roughly selecting by using the amplitude dispersion coefficient, and reserving a point target higher than a threshold value;
calculating a coherence coefficient of a phase point in time through a differential phase, a filtering phase and a terrain residual phase after differential interference, selecting a proper coherence coefficient threshold, and identifying a point higher than the threshold as a PS target point.
Preferably, the space-time disentangling in the step S4 includes:
after removing the topographic phase, the single point phase in the interferometric pair is represented as:
W{φ}＝W{φd+φτ+φa+φo+φn}
wherein W {. is phase winding, phi is single-point interference phase, phid、φτ、φa、φo、φnRespectively, deformation, terrain error, atmosphere, orbit, and noise phase.
Wherein phid、φaAnd phioHaving correlation in space, the correlation phase can be estimated by separating it with space low-pass filtering
Figure BDA0002658779130000041
After that, it was removed to give:
Figure BDA0002658779130000042
in the formula, the superscript u represents spatial irrelevance and represents the total amount of deformation, atmosphere and orbit errors which are not removed and are spatially uncorrelated; the three have strong spatial correlation, so the value is small;
constructing a coherent coefficient:
Figure BDA0002658779130000043
because the interference coefficient gamma is very small, the interference coefficient gamma can reflect the influence of noise on a single point, the larger the noise mean value is, the larger the interference coefficient value is, the maximum value of gamma can be calculated by using a space search method, and the corresponding maximum value can be calculated
Figure BDA0002658779130000044
In removing
Figure BDA0002658779130000045
And after the error, performing space-time three-dimensional unwrapping.
Preferably, the error separation in step S5 includes:
the phase values after unwrapping were:
Figure BDA0002658779130000046
in the formula, phiuwIn order to be able to unwind the phase,
Figure BDA0002658779130000047
the residual terrain residual error phase with spatial correlation is obtained, and k is a single-difference ambiguity integer; if the three-dimensional unwrapping is correct, the k for most points should be equal. To separate the deformation phase, time-space domain filtering is required on the arc segment.
Figure BDA0002658779130000048
In the formula (I), the compound is shown in the specification,
Figure BDA0002658779130000049
representing the difference between two points, then constructing a triangular network to carry out time domain low-pass filtering, and separating the triangular network to obtain
Figure BDA00026587791300000410
In the formula [ ·]LP_timeWhich represents a time domain low-pass filtering,
Figure BDA00026587791300000411
and
Figure BDA00026587791300000412
representing the sub-image atmospheric and orbital components, respectively.
Figure BDA00026587791300000413
The method has strong correlation in a time domain, and deformation and main image atmosphere can be obtained by utilizing time domain low pass; the remaining amount of the image is irrelevant in the time domain, the sub-image atmosphere is acquired,
Figure BDA00026587791300000414
the correlation is shown in the space, and,
Figure BDA00026587791300000415
the error is expressed as a random noise error, so that the atmospheric delay phase and the orbit error of the secondary image can be separated through spatial high-pass filtering; finally, the phi of each point can be obtained by integrating from the arc segment to the pointdAnd
Figure BDA00026587791300000416
preferably, if the differential phase still has a relatively obvious long-wave trend after high-low pass filtering in error separation, namely layered atmosphere delay interference, the absorption is performed by using a linear function and the removal is performed.
Preferably, the surface deformation inversion method further comprises GNSS-InSAR fusion. The GNSS has the characteristics of all weather, high precision, high efficiency and the like, can continuously observe for a long time, and provides high-precision observation data. Because the SAR image has a long time period, information with enough time resolution is difficult to provide, and by utilizing the GNSS-InSAR fusion technology, errors (such as atmospheric delay and the like) which are difficult to eliminate of InSAR data can be corrected, and meanwhile, the high time resolution, the high position precision and the high space resolution of the GNSS can be effectively unified, so that a high-reliability and high-precision earth surface deformation result can be obtained. The technical fusion of GNSS and InSAR is mainly embodied as follows: GNSS is applied for atmospheric delay correction; InSAR data adjustment with GNSS constraints.
Specifically, after the unwrapping phase is obtained, if GNSS external data exists, ZWD is obtained through the GNSS data, atmospheric delay is inverted, and the atmospheric delay is removed from the unwrapping phase;
and adding back the DEM error phase, carrying out parameterization again, carrying out deformation phase solution by using least square, and carrying out combined adjustment on the GNSS observation equation and the InSAR observation equation by using the GNSS observation equation as a constraint condition.
The beneficial technical effects obtained by the invention are as follows:
1) the invention solves the problems that the traditional level and GNSS deformation monitoring method has long monitoring period, sparse spatial point positions and consumes manpower and material resources, and the traditional DInSAR monitoring method is seriously influenced by time-space incoherent and atmospheric delay. The method has strong advantages in the aspects of registration method, atmosphere delay processing algorithm and GNSS-InSAR fusion technology, can monitor surface deformation in all weather, large area, continuous and high spatial resolution, weakens the influence of errors and improves monitoring efficiency;
2) the invention adopts a coarse registration and fine registration method on the aspect of image registration. On the basis of coarse registration, a registration method of enhanced spectral decomposition is utilized to ensure the registration accuracy requirement under a TOPS imaging mode;
3) according to the method, for the atmospheric delay error, the layered atmosphere and the floating atmosphere are respectively processed by different algorithms, so that the influence of the atmospheric error is weakened to the maximum extent, and the precision of deformation inversion is improved;
4) if the GNSS data are provided, the GNSS data can be used for correcting the atmospheric delay, and meanwhile, the combined adjustment is carried out by taking the GNSS data as a constraint condition in an InSAR observation equation, so that the precision and the reliability of a deformation result are ensured.
The foregoing description is only an overview of the technical solutions of the present application, so that the technical means of the present application can be more clearly understood and the present application can be implemented according to the content of the description, and in order to make the above and other objects, features and advantages of the present application more clearly understood, the following detailed description is made with reference to the preferred embodiments of the present application and the accompanying drawings.
The above and other objects, advantages and features of the present application will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flowchart of a surface deformation inversion method based on the time sequence InSAR technology in an embodiment of the present disclosure;
fig. 2 is a flowchart illustrating image registration according to an embodiment of the disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments.
Further, the present application may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion.
Example 1
As shown in fig. 1, a surface deformation inversion method based on a time sequence InSAR technology includes the following steps:
s1, geocoding, and image registration, performing geometric registration by using an external DEM, performing correlation registration based on amplitude and intensity information of an image, and performing fine registration by using enhanced spectral diversity, as shown in fig. 2.
Aiming at the TOPS scanning mode in the Sentinel-1A IW mode, when each burst is scanned, an antenna beam rotationally scans from back to front along the azimuth direction, the azimuth beam pattern is compressed, and the scallop effect caused in imaging is inhibited. Because the TOPS mode special causes Doppler centroid shift, the registration precision requirement of data in the azimuth direction is 1/1000, and therefore, the image is processed in a mode of combining coarse registration and fine registration. And selecting a geometric registration mode by utilizing SAR imaging geometry, orbit data and an external DEM for coarse registration so as to obtain the offset between the main image and the auxiliary image, wherein in order to avoid the phase winding problem in the process of fine registration by using ESD, the coarse registration precision can reach 0.05 pixel as far as possible.
The fine registration is mainly used for correcting the problems of system errors caused by non-parallel tracks and the orientation specificity of the TOPS mode. The method mainly processes one-dimensional signals in a frequency domain, respectively performs interference processing on the overlapping areas of two adjacent bursts in the direction of the main and auxiliary images by using a response pulse function, and then performs interference processing on the interference processing result again until the interference phase difference of the overlapping areas reaches, and finally obtains the registration error in the direction.
The geometric registration with external DEM, in particular:
converting the external DEM into an image coordinate system of a main image, establishing a lookup table, then calculating the position of a certain pixel through satellite orbit parameters, setting a proper window size in the main image, uniformly selecting pixel points, performing simultaneous solution on the coordinates of the pixel points through a distance equation, a Doppler equation and an earth model equation, and inversely calculating the coordinates of the pixel points in an auxiliary image coordinate system;
and calculating the offsets of all the pixel points by utilizing Delaunay triangulation network interpolation, and correcting the secondary image by utilizing a final offset file.
The image-based amplitude and intensity information is correlated with registration, specifically:
selecting a proper window in the main image, and calculating the coherence coefficient of the window;
utilizing maximum likelihood estimation to carry out offset between the main image and the auxiliary image;
and obtaining a conversion matrix according to the calculated offset, carrying out registration on the secondary image, and simultaneously ensuring that the registration precision reaches 0.05 pixel.
Fine registration of the enhanced spectral diversity, in particular:
extracting an overlapping area between the main image and the resampled auxiliary image burst;
the main and auxiliary images are interfered to form a front view and a rear view of an overlapped area, and are interfered again and filtered;
estimating ESD phase and registration errors;
and fitting all the deviation of the overlapping areas by using a polynomial to calculate the azimuth offset.
And S2, generating a differential interference pair.
Specifically, the time base, the space base and the frequency base formed by the main image and the secondary image are important factors influencing the interference effect, and in order to select the best common main image, the sum of the correlation coefficients of the time sequence interferogram needs to be maximized:
Figure BDA0002658779130000071
wherein γ is a coherence coefficient, f (γ)t)，f(γB)，f(γDC) The time base line coefficient, the vertical base line coefficient and the Doppler mass center frequency difference coefficient are respectively used for finding the optimal main image to form an interference pair.
And the external DEM is transferred to a radar coordinate system to form a terrain phase, and the terrain phase and the flat ground phase are subtracted from the interference phase to form a differential interference pair.
And S3, selecting the PS points, and performing iterative selection of the PS points by respectively utilizing the image intensity and the phase information.
And (4) roughly selecting by using the amplitude dispersion coefficient, and reserving the point target higher than the threshold value.
Calculating a coherence coefficient of a phase point in time through a differential phase, a filtering phase and a terrain residual phase after differential interference, selecting a proper coherence coefficient threshold, and identifying a point higher than the threshold as a PS target point. Specifically, for the amplitude deviation selected point, when the pixel has a main scatterer, the phase standard deviation and the amplitude have the following relationship:
Figure BDA0002658779130000081
in the formula (I), the compound is shown in the specification,
Figure BDA0002658779130000082
is the phase standard deviation; sigmaAIs the standard deviation of amplitude a; m isAThe amplitude mean value of the time sequence SAR image is obtained; dAIs a dispersion index when DAAnd when the value is less than the threshold value, selecting the point to be detected as the PS point.
The amplitude dispersion has a high success rate in selecting the PS point with a strong amplitude, but for scatterers with a low signal-to-noise ratio, the relationship between the amplitude dispersion and the phase stability is low, and therefore further selection is needed.
The phase analysis method mainly utilizes the time and space correlation characteristics of different phase components, adopts low-pass filtering to separate out noise phases, and selects pixels which are less affected by noise as PS points.
Figure BDA0002658779130000083
In the formula (I), the compound is shown in the specification,
Figure BDA0002658779130000084
the phase obtained for filtering;
Figure BDA0002658779130000085
error due to baseline; and N is the number of SAR images. Gamma reflects the stability of the target point on the time sequence, and the larger gamma indicates that the noise of the undetermined PS point is smaller, so that the PS point of the point higher than the threshold value is selected. The determination of the threshold value usually requires iteration and filtering operations.
And S4, performing space-time unwrapping, increasing time dimension on the basis of a two-dimensional plane, forming a three-dimensional space search path, and determining residual points to perform three-dimensional unwrapping. The method specifically comprises the following steps:
after removing the topographic phase, the single point phase in the interferometric pair is represented as:
W{φ}＝W{φd+φτ+φa+φo+φn} (4)
wherein W {. is phase winding, phi is single-point interference phase, phid、φτ、φa、φo、φnRespectively, deformation, terrain error, atmosphere, orbit, and noise phase. Wherein phid、φaAnd phioHaving correlation in space, the correlation phase can be estimated by separating it with space low-pass filtering
Figure BDA0002658779130000086
After that, it was removed to give:
Figure BDA0002658779130000087
in the formula, the superscript u represents spatial irrelevance and represents the total amount of deformation, atmosphere and orbit errors which are not removed and are spatially uncorrelated; the three have strong spatial correlation, so the value is small;
constructing a coherent coefficient:
Figure BDA0002658779130000088
because the interference coefficient gamma is very small, the interference coefficient gamma can reflect the influence of noise on a single point, the larger the noise mean value is, the larger the interference coefficient value is, the maximum value of gamma can be calculated by using a space search method, and the corresponding maximum value can be calculated
Figure BDA0002658779130000091
In removing
Figure BDA0002658779130000092
And after the error, performing space-time three-dimensional unwrapping.
In the traditional time sequence InSAR method, a time dimension (1D) and space dimension unwrapping (2D) algorithm is adopted independently, or 1D +2D pseudo three-dimensional unwrapping is carried out, and the time-space characteristics of wrapping phases cannot be considered simultaneously. The present embodiment employs a true three-dimensional (3D) unwrapping algorithm, i.e., the residual point search path is extended from plane to space. Compared with a plane branch cutting method, the method can ensure the accuracy of unwrapping by generating the three-dimensional integral path.
And the three-dimensional unwrapping is respectively embodied in the space dimension and the time dimension, the phase unwrapping is regarded as an optimization problem based on the minimized L-P standard frame, and the minimum value of the objective function is found, so that the phase value after unwrapping is obtained. The objective function is:
Figure BDA0002658779130000093
where Δ φ is a unwrapping phase gradient, Δ ψ is a wrapping phase gradient, x and y denote two directions of two dimensions, respectively, z denotes a third-dimensional direction, i and j denote point coordinates, and w denotes a weight.
The difference from the conventional L-P distribution is that phase gradient information of the third dimension, usually time series information, is added. Here, the parameter P determines how to handle the difference relationship between Δ Φ and Δ ψ, and when P is 0, the above equation is an L-0 norm objective function of a similar branch-cut-and-wrap algorithm if only two-dimensional information is considered; when P is 2, it is the least square unwrapping algorithm.
For nonlinear deformation, under the condition that the prior information is insufficient, deformation is generally assumed to be a linear model in deformation calculation, however, the assumption is difficult to guarantee in the actual deformation process, so that calculation of the elevation and deformation parameters is influenced. Under the scenes of periodic deformation and complex deformation, the influence degree of the function model on DEM error estimation is large. Aiming at the problem of model errors, iterative processing is adopted, different space-time characteristics of terrain, atmosphere and deformation are utilized, and deformation signals are separated in a space-time domain high-low pass filtering mode, so that the influence of the model on parameter estimation is weakened, and the reliability of deformation extraction is ensured. In addition, deformation signals have spatial correlation characteristics, and adjacent points are similar in deformation. In order to avoid errors introduced by the prior deformation model, space deformation constraint can be added, and the deformation quantity is directly inverted.
And S5, error separation, namely, carrying out atmospheric error separation by adopting parameterization and space-time filtering modes respectively for layered atmosphere and floating atmosphere.
Among the many factors that affect InSAR ground settlement monitoring accuracy, atmospheric delay is one of the major errors. Considering the complexity and variability of meteorological conditions in a survey area, a more serious atmospheric delay error may exist in the InSAR interferogram. The atmospheric delay is mainly influenced by the troposphere, and the stratified atmosphere of the troposphere can be absorbed by adopting a parameter related to elevation due to strong regularity; the floating atmosphere is influenced by the change of water vapor, and the model estimation precision is low. The present embodiment provides a processing scheme for the two-part delay separately.
Atmosphere layering: the elevation phase correlation analysis can be adopted to effectively separate the terrain-related atmospheric delay phase caused by the vertical stratification effect. According to the principle of the vertical stratification effect, the atmospheric delay phase and the elevation are generally considered to have a linear relation, so that the atmospheric delay phase is inverted by using a linear regression model for establishing the relation between the elevation and the phase, and fitting can also be performed by using quadratic or higher-order polynomial. The traditional method carries out independent estimation on the layered atmosphere, but due to the existence of deformation and terrain error phase, the estimation of the delay parameter K has errors; in addition, the layered atmosphere does not vary in space, and therefore a single parameter cannot be applied to the delay amount estimation of the entire interference pair. Aiming at the two problems, a method of layered atmosphere delay and deformation and terrain parameter joint estimation is adopted, and atmosphere parameters are added into a model for absorption.
Δφ＝Δφd+τ+K×Δh
In the formula, Δ represents a double difference observation, Δ φd+τIn order to ensure the accuracy of parameter K estimation and weaken the influence of floating atmosphere, a quadtree algorithm is adopted to calculate the whole interference pair in a partitioning manner, namely the calculation burden is reduced and the accuracy of parameter estimation is also ensured.
Floating atmosphere: the partial atmosphere appears stochastic in space and time and is difficult to describe with a deterministic model. Particularly, in low-latitude areas, the atmospheric image is severe in summer and is mostly influenced by floating atmosphere. Therefore, on the basis of ensuring enough image quantity, the respective interference pair with serious decorrelation can be deleted in a proper amount to reduce the influence of atmospheric delay. The residual atmosphere is separated by using the statistical characteristics of the residual atmosphere, namely, the residual atmosphere is separated mainly by a space-time filtering method according to the correlation difference of the atmosphere delay and the deformation phase in a space-time dimension.
The error separation specifically comprises:
the phase values after unwrapping were:
Figure BDA0002658779130000101
in the formula, phiuwIn order to be able to unwind the phase,
Figure BDA0002658779130000102
having spatial correlation for the residueThe terrain residual phase, k, is a single-difference ambiguity integer. If the three-dimensional unwrapping is correct, the k for most points should be equal. To separate the deformation phase, time-space domain filtering is required on the arc segment.
Figure BDA0002658779130000103
In the formula (I), the compound is shown in the specification,
Figure BDA0002658779130000104
representing the difference between two points, then constructing a triangular network to carry out time domain low-pass filtering, and separating the triangular network to obtain
Figure BDA0002658779130000105
In the formula [ ·]LP_timeWhich represents a time domain low-pass filtering,
Figure BDA0002658779130000106
and
Figure BDA0002658779130000107
respectively representing the atmosphere and the orbit components of the secondary image;
Figure BDA0002658779130000108
the method has strong correlation in a time domain, and deformation and main image atmosphere can be obtained by utilizing time domain low pass; the remaining amount of the image is irrelevant in the time domain, the sub-image atmosphere is acquired,
Figure BDA0002658779130000111
the correlation is shown in the space, and,
Figure BDA0002658779130000112
the error is expressed as a random noise error, so that the atmospheric delay phase and the orbit error of the secondary image can be separated through spatial high-pass filtering; finally, the phi of each point can be obtained by integrating from the arc segment to the pointdAnd
Figure BDA0002658779130000113
further, the earth surface deformation inversion method also comprises GNSS-InSAR fusion. After the unwrapping phase is obtained, if GNSS external data exist, ZWD is obtained through the GNSS data, the atmospheric delay is inverted, and the atmospheric delay is removed from the unwrapping phase.
The GNSS has the characteristic of high time sampling rate, can provide ZWD estimation with higher time resolution and higher precision, and reduces or even avoids uncertainty caused by inconsistency between a water vapor product and radar imaging time. However, GNSS reflects single-point ZTD estimation, and application of this estimation to InSAR atmospheric phase correction requires spatial interpolation, and therefore has high requirements on the density and distribution of GNSS sites.
And adding back the DEM error phase, carrying out parameterization again, carrying out deformation phase solution by using least square, and carrying out combined adjustment on the GNSS observation equation and the InSAR observation equation by using the GNSS observation equation as a constraint condition. Specifically, the GNSS prior monitoring result is used as strong constraint, a constraint condition for deformation calculation is constructed, and the calculation precision of least square can be improved. The fusion of the constraint and InSAR data can have three schemes: (1) using the GNSS observation value as conditional constraint to carry out function model constraint; (2) solving additional random model constraint by utilizing a covariance matrix of the GNSS observed quantity; (3) and simultaneously carrying out fusion calculation of double constraints of the function and the random model. The constraint error equation can be written as:
Figure BDA0002658779130000114
in the formula, LGNSSAnd LInSARRespectively GNSS and InSAR observations,GNSSandInSARand for observation errors, X is a parameter to be estimated and comprises deformation and terrain parameters, and final deformation information is obtained by utilizing least square calculation.
The earth surface deformation inversion method based on the time sequence InSAR technology can monitor earth surface deformation in an all-weather, large-area, continuous and high-spatial resolution mode. Specifically, in an image registration mode, coarse registration and fine registration are adopted, so that the registration accuracy requirements under different imaging modes are met; for the atmospheric delay error, processing the layered atmosphere and the floating atmosphere respectively by using different algorithms, so as to weaken the influence of the atmospheric error to the maximum extent and improve the precision of deformation inversion; if the GNSS data are provided, the GNSS data can be used for correcting the atmospheric delay, and meanwhile, the combined adjustment is carried out by taking the GNSS data as a constraint condition in an InSAR observation equation, so that the precision and the reliability of a deformation result are ensured. Compared with the traditional time sequence technology, the method has stronger advantages in the aspects of a registration method, an atmospheric delay processing algorithm and a GNSS-InSAR fusion technology.
The above description is only a preferred embodiment of the present invention, and it is not intended to limit the scope of the present invention, and various modifications and changes may be made by those skilled in the art. Variations, modifications, substitutions, integrations and parameter changes of the embodiments may be made without departing from the principle and spirit of the invention, which may be within the spirit and principle of the invention, by conventional substitution or may realize the same function.

Claims (10)

1. A surface deformation inversion method based on a time sequence InSAR technology is characterized by comprising the following steps:
s1, geocoding and image registration, wherein an external DEM is used for geometric registration, correlation registration is carried out based on the amplitude and intensity information of an image, and a method for enhancing spectral diversity is used for fine registration;
s2, generating a differential interference pair;
s3, selecting a PS point, and performing iterative selection of the PS point by respectively using the image intensity and the phase information;
s4, performing space-time unwrapping, increasing time dimension on the basis of a two-dimensional plane to form a three-dimensional space search path, and determining residual points to perform three-dimensional unwrapping;
and S5, error separation, namely, carrying out atmospheric error separation by adopting parameterization and space-time filtering modes respectively for layered atmosphere and floating atmosphere.
2. The surface deformation inversion method based on the time series InSAR technology as claimed in claim 1, characterized in that in the step S1, an external DEM is used for geometric registration, specifically:
converting the external DEM into an image coordinate system of a main image, establishing a lookup table, then calculating the position of a certain pixel through satellite orbit parameters, setting a proper window size in the main image, uniformly selecting pixel points, performing simultaneous solution on the coordinates of the pixel points through a distance equation, a Doppler equation and an earth model equation, and inversely calculating the coordinates of the pixel points in an auxiliary image coordinate system;
and calculating the offsets of all the pixel points by utilizing Delaunay triangulation network interpolation, and correcting the secondary image by utilizing a final offset file.
3. The earth surface deformation inversion method based on the time series InSAR technology as claimed in claim 1, wherein the step S1 is performed with correlation registration based on the amplitude and intensity information of the image, specifically:
selecting a proper window in the main image, and calculating the coherence coefficient of the window;
utilizing maximum likelihood estimation to carry out offset between the main image and the auxiliary image;
and obtaining a conversion matrix according to the calculated offset, carrying out registration on the secondary image, and simultaneously ensuring that the registration precision reaches 0.05 pixel.
4. The surface deformation inversion method based on the time series InSAR technology as claimed in claim 1, wherein the fine registration for enhancing the spectrum diversity in step S1 specifically comprises:
extracting an overlapping area between the main image and the resampled auxiliary image burst;
the main and auxiliary images are interfered to form a front view and a rear view of an overlapped area, and are interfered again and filtered;
estimating ESD phase and registration errors;
and fitting all the deviation of the overlapping areas by using a polynomial to calculate the azimuth offset.
5. The surface deformation inversion method based on the time series InSAR technology as claimed in claim 1, wherein the step S2 of generating differential interference pairs comprises:
the time base line, the space base line and the frequency base line formed by the main image and the secondary image are important factors influencing the interference effect, and in order to select the optimal public main image, the sum of the correlation coefficients of the time sequence interference pattern needs to be maximized:
Figure FDA0002658779120000021
wherein γ is a coherence coefficient, f (γ)t)，f(γB)，f(γDC) Respectively obtaining a time baseline coefficient, a vertical baseline coefficient and a Doppler mass center frequency difference coefficient, and forming an interference pair after finding the optimal main image;
and the external DEM is transferred to a radar coordinate system to form a terrain phase, and the terrain phase and the flat ground phase are subtracted from the interference phase to form a differential interference pair.
6. The surface deformation inversion method based on the time sequence InSAR technology as claimed in claim 1, wherein the step S3 of selecting the PS point comprises:
roughly selecting by using the amplitude dispersion coefficient, and reserving a point target higher than a threshold value;
calculating a coherence coefficient of a phase point in time through a differential phase, a filtering phase and a terrain residual phase after differential interference, selecting a proper coherence coefficient threshold, and identifying a point higher than the threshold as a PS target point.
7. The surface deformation inversion method based on the time series InSAR technology as claimed in claim 1, wherein the space-time unwrapping in the step S4 includes:
after removing the topographic phase, the single point phase in the interferometric pair is represented as:
W{φ}＝W{φd+φτ+φa+φo+φn}
wherein W {. is phase winding, phi is single-point interference phase, phid、φτ、φa、φo、φnRespectively representing deformation, terrain error, atmosphere, orbit, and noise phase; wherein phid、φaAnd phioHaving correlation in space, the correlation phase can be estimated by separating it with space low-pass filtering
Figure FDA0002658779120000022
After that, it was removed to give:
Figure FDA0002658779120000023
in the formula, the superscript u represents spatial irrelevance and represents the total amount of deformation, atmosphere and orbit errors which are not removed and are spatially uncorrelated; the three have strong spatial correlation, so the value is small;
constructing a coherent coefficient:
Figure FDA0002658779120000024
because the interference coefficient gamma is very small, the interference coefficient gamma can reflect the influence of noise on a single point, the larger the noise mean value is, the larger the interference coefficient value is, the maximum value of gamma can be calculated by using a space search method, and the corresponding maximum value can be calculated
Figure FDA0002658779120000025
In removing
Figure FDA0002658779120000031
And after the error, performing space-time three-dimensional unwrapping.
8. The surface deformation inversion method based on the time series InSAR technology as claimed in claim 1, wherein the error separation in the step S5 includes:
the phase values after unwrapping were:
Figure FDA0002658779120000032
in the formula, phiuwIn order to be able to unwind the phase,
Figure FDA0002658779120000033
the residual terrain residual error phase with spatial correlation is obtained, and k is a single-difference ambiguity integer; if the three-dimensional unwrapping is correct, k should be equal for most points; in order to separate out the deformation phase, time-space domain filtering is required to be carried out on the arc section;
Figure FDA0002658779120000034
in the formula (I), the compound is shown in the specification,
Figure FDA0002658779120000035
representing the difference between two points, then constructing a triangular network to carry out time domain low-pass filtering, and separating the triangular network to obtain
Figure FDA0002658779120000036
In the formula [ ·]LP_timeWhich represents a time domain low-pass filtering,
Figure FDA0002658779120000037
and
Figure FDA0002658779120000038
respectively representing the atmosphere and the orbit components of the secondary image;
Figure FDA0002658779120000039
the method has strong correlation in a time domain, and deformation and main image atmosphere can be obtained by utilizing time domain low pass; the remaining amount of the image is irrelevant in the time domain, the sub-image atmosphere is acquired,
Figure FDA00026587791200000310
the correlation is shown in the space, and,
Figure FDA00026587791200000311
the error is expressed as a random noise error, so that the atmospheric delay phase and the orbit error of the secondary image can be separated through spatial high-pass filtering; finally, the phi of each point can be obtained by integrating from the arc segment to the pointdAnd
Figure FDA00026587791200000312
9. the earth surface deformation inversion method based on the time sequence InSAR technology as claimed in claim 1, characterized in that if the differential phase still has a relatively obvious long wave trend after the high-low pass filtering in the error separation, i.e. the layered atmosphere delay interference, the linear function is adopted for absorption and removal.
10. The earth surface deformation inversion method based on the time sequence InSAR technology as claimed in claim 1, characterized in that the earth surface deformation inversion method further comprises GNSS-InSAR fusion, after the unwrapping phase is obtained, if there is GNSS external data, ZWD can be obtained preferably through GNSS data, atmospheric delay is inverted and removed from the unwrapping phase;
and adding back the DEM error phase, carrying out parameterization again, carrying out deformation phase solution by using least square, and carrying out combined adjustment on a GNSS observation equation and an InSAR observation equation to obtain final deformation information.
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