CN113960595A - Surface deformation monitoring method and system - Google Patents

Surface deformation monitoring method and system Download PDF

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CN113960595A
CN113960595A CN202111119766.8A CN202111119766A CN113960595A CN 113960595 A CN113960595 A CN 113960595A CN 202111119766 A CN202111119766 A CN 202111119766A CN 113960595 A CN113960595 A CN 113960595A
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孙鹭怡
陈劲松
韩宇
姜小砾
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Shenzhen Institute of Advanced Technology of CAS
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    • 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
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/9021—SAR image post-processing techniques
    • G01S13/9023—SAR image post-processing techniques combined with interferometric techniques
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    • G06T7/00—Image analysis
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract

The application relates to a method and a system for monitoring surface deformation. The method comprises the following steps: carrying out homogeneous point extraction on SAR sequence image data in a monitoring area by adopting a Gamma confidence interval discrimination method to generate a homogeneous point set of the SAR sequence image data; calculating an interference phase coherence coefficient between a secondary image and a main image in the SAR sequence image data based on the homogeneous point set; performing self-adaptive multi-view processing on an interferogram of SAR sequence image data according to the interference coherence coefficient to obtain a multi-view interference phase; carrying out PS candidate point screening based on the amplitude dispersion of the SAR sequence image to obtain PS candidate points; calculating an adaptive threshold of an interference coherence coefficient based on the homogeneous point set, and screening PS candidate points by adopting the adaptive threshold to obtain an effective PS point mask; and performing atmospheric correction on the SAR sequence image data based on the effective PS point mask and the multi-view interference phase. This application has improved the monitoring precision of earth's surface deformation.

Description

Surface deformation monitoring method and system
Technical Field
The application belongs to the technical field of geological disaster monitoring, and particularly relates to a method and a system for monitoring surface deformation.
Background
In order to meet the high-speed development of economy and society, large-scale municipal engineering construction is carried out on a plurality of cities, and surface deformation of the cities becomes one of main geological disasters due to coastal region sea filling engineering, underground water pumping, railway, subway construction and the like, so that the life safety of people is seriously threatened. At present, the technology for monitoring urban ground surface deformation mainly includes on-site measurement (leveling instrument, total station instrument, etc.) and remote sensing monitoring means. The field measurement is not suitable for large-range and high-frequency dynamic monitoring due to the reasons of high labor cost, high layout risk, sparse monitoring points, small coverage range and the like. In the remote sensing monitoring means, an optical sensor is influenced by clouds, and is difficult to acquire long-sequence effective optical images for monitoring under the climatic conditions of cloudy and rainy cities in coastal cities in south China.
The Synthetic Aperture Radar (SAR) has the characteristics of all-weather working capacity, wide coverage range, strong penetration capacity (effective data can be obtained through a cloud layer), short satellite revisit period and the like, and a lot of SAR data can be downloaded freely in the world, so that large-range and high-frequency dynamic monitoring can be realized, and the SAR has irreplaceable advantages compared with the traditional surface deformation monitoring. Synthetic space radar interferometry (InSAR) technology has become the most efficient and feasible technique for surface deformation measurement. The time series synthetic aperture radar interferometry obtains great achievements in the aspect of surface deformation monitoring. This class of techniques can be broadly divided into two categories: a permanent Scatterer synthetic aperture radar interference (PS-InSAR) technology and a Small Baseline Subset InSAR (SBAS-InSAR) technology. The british scholars Hooper proposed a new time series InSAR algorithm, StaMPS (Stanford Method for Persistent Scatterers), in 2008, and according to the application requirements, two optimization models, namely PS-InSAR and SBAS-InSAR, are adopted independently or effectively combined, which is one of the most widely used surface deformation monitoring technologies at present. In urban building dense areas, the surface deformation monitoring is usually selected by using the PS-InSAR technology in StaMPS. The problems of the existing StaMPS PS-InSAR technology mainly comprise:
1) in order to maintain the original resolution of the SAR image and thus maintain a higher number of PS (Persistent scatterer) candidate points, no multi-view processing is performed on the interference phase, and no suppression is performed on spatial uncorrelated noise before PS-InSAR processing.
2) When the PS points are screened for the second time, the sensitivity of the parameters is high, the fine adjustment of the parameters at the critical points easily causes too few or too many PS points, the too few PS points cannot completely present the spatial variation trend of deformation, and the too many PS points often reserve the noise points of the water body and the vegetation surface by mistake.
3) In the atmospheric phase correction link, the existing StaMPS method adopts a space-time filtering method alone or an external atmospheric product alone to estimate the atmospheric delay phase, and the condition of large space-time change of water vapor on coastal areas in south China cannot be well treated.
Disclosure of Invention
The application provides a method and a system for monitoring surface deformation, which aim to solve at least one of the technical problems in the prior art to a certain extent.
In order to solve the above problems, the present application provides the following technical solutions:
a method of monitoring surface deformation, comprising:
based on StaMPS algorithm, adopting Gamma confidence interval discrimination method to extract homogeneous points of SAR sequence image data in a monitored area, and generating a homogeneous point set of the SAR sequence image data;
calculating an interference phase coherence coefficient between the secondary image and the main image in the SAR sequence image data based on the homogeneous point set;
performing self-adaptive multi-view processing on an interferogram of the SAR sequence image data according to the interference coherence coefficient calculation result to obtain a multi-view interference phase;
calculating the amplitude dispersion of the SAR sequence image, and screening PS candidate points according to the amplitude dispersion to obtain PS candidate points;
calculating an adaptive threshold of an interference coherence coefficient based on the homogeneous point set, and screening PS candidate points by adopting the adaptive threshold to obtain an effective PS point mask;
based on the effective PS point mask and the multi-view interference phase, carrying out atmospheric correction on the SAR sequence image data by adopting a GACOS model and an InSAR space-time filtering algorithm;
and performing time-series inversion on the deformation rate and the accumulated deformation of the atmospheric corrected SAR sequence image data based on the StaMPS algorithm to obtain a surface deformation monitoring result of the monitoring area.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the method for extracting the homogeneous points of the SAR sequence image data in the monitoring area by adopting a Gamma confidence interval discrimination method comprises the following steps:
respectively determining a search window for each pixel in the SAR sequence image data, taking the pixel as a reference pixel, and calculating a reference sample mean value by using the time-dimensional sampling of the pixel
Figure BDA0003276659590000031
To be provided with
Figure BDA0003276659590000032
And the set alpha value calculates Gamma distribution confidence interval; the Gamma distribution confidence interval calculation formula is as follows:
Figure BDA0003276659590000041
wherein 1-alpha is the confidence level,
Figure BDA0003276659590000042
representing a standard Gamma G (NL,1) distribution
Figure BDA0003276659590000043
Performing quantile point division, wherein N is the image number of the SAR sequence image data, L is the view number of the SAR sequence image data, and a is L; sigma2The backscattering intensity of the SAR sequence image data is obtained;
judging the search windowWhether the time dimension sample mean value of the intraoral neighborhood pixel falls into a Gamma distribution confidence interval or not is judged, if yes, the neighborhood pixel and the reference pixel belong to the same statistical distribution, and the initial homogeneous pixel subset omega of the pixel is obtainedinit；
Using the initial homogeneous pixel subset ΩinitAverage of sample means of K pixels in the set against the reference sample mean
Figure BDA0003276659590000045
Performing updating according to the updated
Figure BDA0003276659590000046
Recalculating the Gamma distribution confidence interval;
according to the new Gamma distribution confidence interval, the initial homogeneous pixel subset omega is subjected toinitAnd (4) performing secondary judgment on the pixel in the group, reserving a neighborhood pixel which falls into a new Gamma distribution confidence interval and is connected with the reference pixel space, and generating a carefully-selected homogeneous pixel subset omega of the pixel.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the calculating interference coherence coefficient between the secondary image and the primary image in the SAR sequence image data based on the homogeneous point set comprises:
for each pixel in the SAR sequence image data, taking a homogeneous pixel subset omega of the pixel to perform interference coherence coefficient estimation to obtain the biased estimation of the interference coherence coefficient of the pixel; the interference coherence coefficient calculation formula is as follows:
Figure BDA0003276659590000044
wherein Z1(l)，Z2(l) Representing two SAR images Z for interference1，Z2A plurality of signals corresponding to the middle pixel l, wherein K is the number of pixels in the homogeneous pixel subset omega where the pixel is located;
and carrying out deviation correction on the interference coherence coefficient biased estimation by adopting a dual boosting method to obtain the SAR sequence image dataUnbiased interference correlation coefficient; the interference coherent coefficient of approximate unbiased estimation
Figure BDA0003276659590000051
Comprises the following steps:
Figure BDA0003276659590000052
wherein all points in the homogeneous pel subset Ω are denoted as X ═ X (X)1,x2,…,xK) K is the number of the homogeneous points, R is the number of times of Bootstrap sampling X repeatedly,
Figure BDA0003276659590000053
for Bootstrap copied samples, γ is a biased estimate of the interference coherence coefficient.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the self-adaptive multi-view processing of the interferogram of the SAR sequence image data according to the interference coherence coefficient calculation result comprises the following steps:
preprocessing the SAR sequence image data to generate an interferogram of the SAR sequence image data; the pretreatment comprises the following steps:
performing space-time baseline analysis on the SAR sequence image data, and selecting a first-stage image with the shortest space and time baseline in the SAR sequence image data as a main image and other images as auxiliary images;
and registering the secondary image and the main image by utilizing the orbit parameters and the DEM model according to the pairing criterion of the PS-InSAR algorithm, and forming an interference pair by the secondary image and the main image to generate an interference pattern.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the self-adaptive multi-view processing of the interferogram of the SAR sequence image data according to the interference coherence coefficient calculation result comprises the following steps:
for each pixel in the SAR sequence image data, taking a homogeneous pixel subset where the pixel is located as a unit, weighting an interferogram complex signal in the homogeneous pixel subset according to an interference coherence coefficient, and taking the phase of the interferogram complex signal to obtain a multi-view interference phase;
for any pixel p, the calculation formula of the multi-view interference phase is as follows:
Figure BDA0003276659590000061
where k is the number of homogeneous pixels in the homogeneous subset of pixels in which pixel P is located, CohiIs the interference coherence coefficient, Ifg, of the ith pixeliIs the complex signal of the interference pattern of the ith pixel.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the specific steps of calculating the amplitude dispersion of the SAR sequence image data are as follows:
Figure BDA0003276659590000062
wherein D isARepresenting amplitude dispersion, σAAnd muARespectively, standard deviation and mean value of SAR sequence image data calculated along time dimension.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the step of calculating an adaptive threshold of the interference coherence coefficient based on the homogeneous point set, and the step of screening the PS candidate points by adopting the adaptive threshold comprises the following steps:
according to the set real threshold value
Figure BDA0003276659590000063
And the number of homogeneous pixels in the homogeneous pixel subset, calculating a self-adaptive threshold value:
Figure BDA0003276659590000064
wherein sigmacoh is the Cramer-Rao lower bound standard deviation of the unbiased interference correlation coefficient;
for each pixel in the SAR sequence image data, forming the unbiased interference coherent coefficient in a time dimension according to the adaptive threshold of the pixelThe sequence is counted if a fine estimate of the interference correlation coefficient in the time sequence is made
Figure BDA0003276659590000065
If the number of the points is larger than the set proportion, determining the point as an effective PS point;
and solving the intersection of all the effective PS points and the PS candidate points to obtain an effective PS point mask.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the atmospheric correction of the SAR sequence image data by adopting a GACOS model and an InSAR space-time filtering algorithm based on the effective PS point mask and the multi-view interference phase comprises the following steps:
performing space correlation phase component estimation on the SAR sequence image data by adopting a StaMPS algorithm, and performing phase noise estimation on the effective PS point mask;
and screening the effective PS point mask by combining the amplitude dispersion and the phase noise estimation, and performing three-dimensional phase unwrapping on the screened PS points:
and based on the phase unwrapping result of the PS point, filtering out atmospheric delay and orbit error phases from the interference phase by adopting a GACOS model and an InSAR space-time filtering algorithm, and performing atmospheric correction on the SAR sequence image data.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the atmospheric correction of the SAR sequence image data by adopting a GACOS model and an InSAR space-time filtering algorithm comprises the following steps:
estimating the atmospheric delay phase of each group of interference pairs by adopting a GACOS model, and removing the atmospheric phase component related to the terrain from the corresponding interference phase;
performing time-dimensional low-pass filtering on the SAR sequence image data by adopting an InSAR space-time filtering algorithm to obtain the atmospheric delay and the orbit error of the main image;
performing time-dimensional high-pass filtering nested space-dimensional low-pass filtering on the SAR data by adopting an InSAR space-time filtering algorithm to obtain atmospheric delay, orbit error and DEM error of the secondary image;
and removing the atmospheric delay and orbit error of the main image, the atmospheric delay and orbit error of the auxiliary image and the DEM error from the interference phase, and performing refined atmospheric correction on the SAR sequence image data.
Another technical scheme adopted by the embodiment of the application is as follows: a surface deformation monitoring system comprising:
a homogeneity point extraction module: the method is used for extracting the homogeneous points of SAR sequence image data in a monitoring area by adopting a Gamma confidence interval discrimination method based on a StaMPS algorithm to generate a homogeneous point set of the SAR sequence image data;
a multi-view processing module: the interference coherence coefficient is used for calculating the interference coherence coefficient between the secondary image and the main image in the SAR sequence image data based on the homogeneous point set, and self-adaptive multi-view processing is carried out on an interferogram of the SAR sequence image data according to the interference coherence coefficient calculation result to obtain a multi-view interference phase;
initial PS spot screening module: the amplitude dispersion degree of the SAR sequence image is calculated, and PS candidate point screening is carried out according to the amplitude dispersion degree to obtain PS candidate points;
effective PS point screening module: the adaptive threshold value is used for calculating interference coherence coefficients based on the homogeneous point set, and the PS candidate points are screened by adopting the adaptive threshold value to obtain an effective PS point mask;
an atmosphere correction module: the system is used for performing atmospheric correction on the SAR sequence image data by adopting a GACOS model and an I nSAR space-time filtering algorithm based on the effective PS point mask and the multi-view interference phase;
a sequence inversion module: and the method is used for performing time series inversion on the deformation rate and the accumulated deformation of the atmospheric corrected SAR sequence image data based on the StaMPS algorithm to obtain the surface deformation monitoring result of the monitoring area.
Compared with the prior art, the embodiment of the application has the advantages that: the earth surface deformation monitoring method and the earth surface deformation monitoring system based on homogeneous point extraction improve a classic StaMPS algorithm, self-adaptive multi-view processing based on a homogeneous point set is carried out on interference phases before PS point screening, targets of two different scattering mechanisms of a permanent scattering point PS and a distributed scattering point DS are distinguished, phase noise is effectively inhibited, high-coherence signals of the PS point targets are prevented from being mixed by phase noise of other points, and follow-up resolving precision is improved. The interference coherence coefficient and the self-adaptive threshold value after deviation correction are adopted to assist the PS point screening, so that the integrity of the space deformation trend is maintained as much as possible while the low coherence points on the surfaces of the water body and the vegetation are effectively removed, and the PS point is prevented from being excessively screened. The atmosphere delay phase is finely corrected by combining a GACOS atmosphere model and InSAR space-time filtering, so that the accuracy of atmosphere correction is improved, and the monitoring accuracy of surface deformation is improved.
Drawings
FIG. 1 is a flow chart of a method of monitoring surface deformation according to an embodiment of the present application;
FIG. 2 is a flowchart of atmosphere fine correction incorporating a GACOS atmosphere model and spatio-temporal filtering in accordance with an embodiment of the present application;
fig. 3 is a schematic structural diagram of a surface deformation monitoring system according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Please refer to fig. 1, which is a flowchart illustrating a method for monitoring surface deformation according to an embodiment of the present application. The earth surface deformation monitoring method comprises the following steps:
s10: acquiring SAR sequence image data of a monitored area;
in this step, the acquired SAR sequence image data is a Sentinel-1IW TOPS mode SLC time series data, and the SLC time series data parameters include a time range, a track direction, a revisit period, a polarization mode, an incident angle, a wavelength, a skew sampling interval, an azimuth sampling interval, and the like, which are specifically shown in table 1:
TABLE 1 Sentinel-1IW SLC time series data parameters for monitoring region
Figure BDA0003276659590000091
Figure BDA0003276659590000101
S20: preprocessing SAR sequence image data to generate an interferogram of the SAR sequence image data;
in this step, the preprocessing of the SAR sequence image data specifically includes:
s21: performing space-time baseline analysis on the SAR sequence image data, and selecting a first-stage image with the shortest space and time baseline in the SAR sequence image data as a main image and other images as auxiliary images;
s22: registering the secondary image and the main image by using an orbit parameter and a Digital Elevation Model (DEM) according to a pairing criterion of a PS-InSAR algorithm, and forming an interference pair by the secondary image and the main image to generate an interference map;
s23: and (4) removing the flat land phase and the terrain phase of the interferogram based on the DEM model, and then deriving a registered interference phase file.
S30: in the StaMPS algorithm, a Gamma confidence interval discrimination method is adopted to extract a Homogeneous point (SHP) of SAR sequence image data to generate a Homogeneous point set of the SAR sequence image data;
in this step, the backscattering intensity of a pixel (i.e. a resolution unit) in the SAR image is superimposed on the backscattering intensities of a large number of scatterers belonging to the pixel. If all the pixels are Distributed scatterers, the pixel is a DS (Distributed scattering point) point target and can be regarded as a zero-mean complex gaussian random variable. Given a SAR sequence image data with N-phase SLC (Single Look Complex) format, for a DS-point target, its time-dimension samples { Z }1,Z2,...,ZN}TWhich can be viewed as a zero-mean complex gaussian random variable, the covariance matrix can be defined as:
Figure BDA0003276659590000111
wherein
Figure BDA0003276659590000112
For the amplitude of the ith image, the diagonal elements of the matrix
Figure BDA0003276659590000113
Is the backscatter intensity of the ith SAR image. In the multi-view case, assuming the view is L, the backscattering intensity is expressed as
Figure BDA0003276659590000114
Obeying a Gamma distribution with a distribution parameter of
Figure BDA0003276659590000115
And L:
Figure BDA0003276659590000116
for a DS-point target, the time-dimensional vector of the backscattering intensity is expressed as
Figure BDA0003276659590000117
Figure BDA0003276659590000118
The object of the present application is to find a confidence interval in relation to sigma. According to SAR statistical theory, σ obeys a Gamma distribution σ to G (a, b), where a ═ L,
Figure BDA0003276659590000119
the mean value is mu-sigma2Variance is
Figure BDA00032766595900001110
Sample mean values according to the central limit theorem
Figure BDA00032766595900001111
Obey normal distribution
Figure BDA00032766595900001112
Then it is corresponding to
Figure BDA00032766595900001113
The confidence interval of (a) is:
Figure BDA00032766595900001114
wherein
Figure BDA00032766595900001115
Being a function of the probability density of the standard global distribution
Figure BDA00032766595900001116
Quantile, 1- α is the confidence level (e.g., 95% confidence, then α is 5%). And distinguishing the neighborhood pixels of the pixel one by one, if the sample mean value of the neighborhood pixels falls into the confidence interval, considering that the neighborhood pixels and the pixel belong to the same statistical distribution, and classifying the neighborhood pixels and the pixel into a homogeneous point subset.
When the time sequence of the SAR image data is short, namely the period number N of the image is less than a certain value, the sample mean value of the Gamma distribution does not follow the normal distribution
Figure BDA00032766595900001117
At this time, the confidence interval is asymmetric, and cannot be inferred according to the central limit theorem. In this case, if a random variable obeys a Gamma distribution σ G (a, b), where a ═ L,
Figure BDA00032766595900001118
then
Figure BDA00032766595900001119
Order to
Figure BDA00032766595900001120
And Y obeys the standard Gamma distribution Y-G (Na,1), and the corresponding confidence interval of the Gamma distribution is as follows:
Figure BDA0003276659590000121
wherein
Figure BDA0003276659590000122
Representing standard Gamma distribution
Figure BDA0003276659590000123
And (5) dividing the site. Because of the fact that
Figure BDA0003276659590000124
b＝σ2L, then
Figure BDA0003276659590000125
Substituting a into L, substituting into (4), and converting to obtain
Figure BDA0003276659590000126
The 1- α confidence interval of (a) is:
Figure BDA0003276659590000127
where 1- α is the confidence level (e.g., 95% confidence level, then α 5%),
Figure BDA0003276659590000128
representing a standard Gamma distribution G (NL,1)
Figure BDA0003276659590000129
Quantile, N is image number of SAR sequence image data, L is view number of SAR sequence image data, sigma2Is the backscatter intensity of the image data of the SAR sequence,
Figure BDA00032766595900001210
is the sample mean of σ. Therefore, no matter the time sequence length of the SAR sequence image data, the equation (7) can be uniformly adopted as a confidence interval to carry out the homogeneous point judgment.
Based on the above principle of homogeneous point discrimination based on the Gamma distribution confidence interval, in the embodiment of the present application, the process of extracting homogeneous points from the SAR sequence image data by using the Gamma confidence interval discrimination method specifically includes:
s31: detecting and eliminating singular values in SAR sequence image data by adopting an adjusting Boxplot (Adjusted Boxplot) under the premise of considering data distribution skewness (sharwdness) index Medcouple (MC);
s32: judging whether the time dimension vector of each pixel in the SAR sequence image data after removing the singular value obeys Gamma distribution or not by adopting a unilateral Anderson-Darling (AD) inspection method, and executing S33 if the time dimension vector obeys the Gamma distribution;
s33: adopting a formula (7) as a confidence interval to judge the homogeneity point of the SAR sequence image data, and extracting a homogeneity point set of the SAR sequence image data;
wherein the specific extraction process comprises the following steps: respectively determining a search window for each pixel in SAR sequence image data, taking the pixel as a reference pixel, and calculating a reference sample mean value by using the time-dimensional sampling of the pixel
Figure BDA0003276659590000131
To be provided with
Figure BDA0003276659590000132
And the set alpha value calculates Gamma distribution confidence interval; judging all neighborhood pixels in the search window one by one, if the time dimension sample mean value of the neighborhood pixels falls into a Gamma distribution confidence interval, judging that the neighborhood pixels and the reference pixels belong to the same statistical distribution, and obtaining an initial homogeneous pixel subset omega of the pixelsinit. Since the length N of the time series is finite, the sample mean
Figure BDA0003276659590000133
The variance of (c) is large. Therefore, the embodiments of the present application employ an initial homogeneous pixelSet omegainitAverage of sample means of K pixels in the image to the reference sample means
Figure BDA0003276659590000136
Performing an update, and then according to the updated
Figure BDA0003276659590000134
Recalculating Gamma distribution confidence interval, and performing omega matching on the initial homogeneous pixel subset according to the new Gamma distribution confidence intervalinitThe pixel in (b) is judged twice, and the neighborhood pixels that fall into the new Gamma distribution confidence interval and are spatially connected with the reference pixel are retained, thereby generating a refined homogeneous pixel subset Ω of the pixel. And respectively carrying out the processing on each pixel in the SAR sequence image data to generate a homogeneous point set of all the pixels. In the embodiment of the application, the initial homogeneous pixel subset omega is generatedinitWhen, set α to 50%; when the refined homogeneous pixel subset omega is generated, alpha is set to be 5%, and the specific alpha value can be set according to practical application.
S40: estimating interference phase coherence between a secondary image and a main image in SAR sequence image data based on a homogeneous point set, and performing deviation correction on the interference phase coherence by adopting a double boosting (with back sampling) method to obtain an unbiased interference coherence coefficient of the SAR sequence image data;
in this step, interference phase coherence (coherence) is a measure of similarity between two SAR image signals, and is used to measure the estimation accuracy of the InSAR phase and to guide phase filtering and phase unwrapping. In the covariance matrix in the formula (3), the off-diagonal elements
Figure BDA0003276659590000135
That is, the coherence calculation formula using a sliding window when the view is L is:
Figure BDA0003276659590000141
wherein the amplitude value
Figure BDA0003276659590000142
Is the interference coherence coefficient, Zi(l) Is the complex signal of pixel i. When using sliding windows for coherence computation, the implicit assumption is that neighboring pixels within a window all belong to the same statistical distribution. However, in a scene with rich texture, the pixels inside a window are likely to have different statistical distributions. The non-differential averaging of neighborhood pixels with different scattering properties results in a bias in the estimation of the interference phase coherence and a reduced image resolution. Therefore, the embodiment of the invention adopts the homomorphic point set to replace the traditional sliding window to estimate the interference coherent coefficient, and adopts a double boosting (with back sampling) method to further correct the deviation, thereby improving the estimation precision of the interference coherent coefficient.
Specifically, the interference phase coherence estimation and deviation correction of the SAR sequence image data based on the homogeneous point set comprises the following steps:
s41: for each pixel, taking the homogeneous pixel subset omega of each pixel to carry out interference coherence coefficient estimation to obtain the biased estimation of the interference coherence coefficient of the pixel;
wherein, replacing the sliding window in the formula (8) with a homogenous point set, the calculation formula of the interference coherence coefficient is as follows:
Figure BDA0003276659590000143
wherein Z1(l)，Z2(l) Representing two SAR images Z for interference1，Z2And K is the number of pixels in the homogeneous pixel subset omega where the pixel is located. The method can avoid estimation deviation caused by texture difference of all parts of the SAR image.
S42: adopting an unparameterized double Bootstrapping method to carry out deviation correction on the biased estimation of the interference coherent coefficient to obtain a unbiased interference coherent coefficient;
for two SAR images participating in interference, the complex signal corresponding to each pixel in the homogeneous point set omega can be obtainedTo be regarded as a pair of complex observations xi＝(z1i,z2i) (ii) a All points in the homogenous point set Ω may be represented as X ═ X (X)1,x2,…,xK) K is the number of the homogeneous points and can be regarded as a boosting sample
Figure BDA0003276659590000151
I.e., there are K samples taken back from the original observation X, which is the first resampling. After repeatedly sampling X for R times, a group of double boosting samples can be obtained
Figure BDA0003276659590000152
After calculating the interference coherence coefficient by adopting the formula (9) for each sample, R Bootstrap copied samples can be obtained
Figure BDA0003276659590000153
The samples copied by adopting the Bootstrap have biased estimation on interference coherent coefficient
Figure BDA0003276659590000154
Deviation correction is carried out to obtain interference coherent coefficient approximate to unbiased estimation
Figure BDA0003276659590000155
Figure BDA0003276659590000156
S50: adopting a homogeneous point set, and carrying out self-adaptive multi-view processing on an interferogram of SAR sequence image data according to an interference coherence coefficient calculation result to obtain a multi-view interference phase;
in the step, because the surfaces with different scattering characteristics such as vegetation, water bodies and artificial buildings exist in the research area, in order to avoid the interference of phase noise of scatterers with low coherence on scatterers with high coherence without confusing scatterers with different characteristics during multi-view processing, the embodiment of the application adopts a homogenous point set to replace a regular sliding window, performs self-adaptive multi-view processing on all interferograms in a time sequence, and distinguishes targets with two different scattering mechanisms of a permanent scattering point PS and a distributed scattering point DS, so that not only is the phase noise effectively inhibited, but also the high-coherence signal of the PS point target is prevented from being confused by the phase noise of other points, and the subsequent resolving precision is improved.
Specifically, for each pixel, taking the homogeneous pixel subset where the pixel is located as a unit, weighting the interferogram complex signal in the homogeneous pixel subset according to the interference coherence coefficient which is accurately estimated, taking the phase of the interferogram complex signal, and repeating the operation on each pixel in the SAR sequence image data to obtain the interference phase after the self-adaption multi-vision. For any pixel p, the calculation formula of the multi-view interference phase is as follows:
Figure BDA0003276659590000161
where K is the number of homogeneous pixels in the homogeneous sub-set of pixels in which the pixel is located, CohiIs the interference coherence coefficient, Ifg, of the ith pixeliIs the complex signal of the interference pattern of the ith pixel.
Based on the above, for each pixel, the interference phase is weighted by the homogeneous pixel subset where the pixel is located, the weight is the coherence coefficient of the pixel, and the PS point is located inside the homogeneous pixel subset which belongs to the pixel alone and is not interfered by the target phase noise of other points; and the DS point carries out phase averaging of the weighting of the coherent coefficient in the homogeneous pixel subset, thereby obviously reducing the phase noise. Compared with the prior art, the embodiment of the application keeps local details while suppressing the phase noise, and avoids reducing the number of PS points.
S60: calculating amplitude dispersion (amplitude dispersion) of the SAR sequence image data, and performing PS candidate point screening on the SAR sequence image data according to the amplitude dispersion to obtain PS candidate points;
in this step, the amplitude dispersion calculation formula is as follows:
Figure BDA0003276659590000162
wherein D isARepresenting amplitude dispersion, σAAnd muARespectively, standard deviation and mean value of SAR sequence image data calculated along time dimension. In the examples of this application, DAThe threshold value of (2) is set to 0.35 to 0.4.
S70: calculating an adaptive threshold of the interference coherence coefficient based on the homogeneous point set, and screening PS candidate points by adopting the adaptive threshold to obtain an effective PS point mask;
in this step, first, according to the set real threshold
Figure BDA0003276659590000163
And the number of homogeneous pixels in the homogeneous point set is calculated to obtain a self-adaptive threshold value:
Figure BDA0003276659590000164
wherein sigmacohThe calculation formula is the Cramer-Rao lower bound standard deviation of the unbiased interference correlation coefficient:
Figure BDA0003276659590000171
for each pixel in the image data of the SAR sequence, calculating the adaptive threshold value of the pixel according to the formula (13), counting the sequence formed by the interference coherence coefficient in the time dimension which is calculated based on the homogeneous point set and subjected to deviation correction, and if the precise estimation value of the interference coherence coefficient in the time sequence is adopted
Figure BDA0003276659590000172
If the number of the PS dots is larger than a predetermined ratio (which can be adjusted by itself, for example, 85%) and the PS dots are determined to be valid. And analyzing all the pixels one by one, and then solving intersection with the PS candidate points to obtain an effective PS point mask.
Based on the above, the interference coherence coefficient and the adaptive threshold value after deviation correction are adopted to assist the PS point screening, so that the integrity of the space deformation trend is maintained as much as possible while the low coherence points on the surfaces of the water body and the vegetation are effectively removed, and the excessive screening of the PS points is avoided.
S80: performing space correlation phase component estimation on SAR sequence image data by adopting a StaMPS algorithm, performing phase noise estimation on an effective PS point mask, and acquiring terrain residual phase estimation;
s90: screening effective PS point masks by combining amplitude dispersion and phase noise estimation, and performing three-dimensional (space two-dimensional + time dimension) phase unwrapping on the screened PS points;
s100: based on the phase unwrapping result of the PS points, filtering out atmospheric delay, orbit error phases and the like from the interference phases by adopting a GACOS model and an InSAR space-time filtering algorithm, and performing refined atmospheric correction on SAR sequence image data;
in the step, firstly, the GACOS model is adopted to estimate the atmospheric delay phase of each group of interference pairs, the atmospheric phase component related to the terrain is removed, and the temporal-spatial filtering method in the StaMPS is adopted to filter the residual atmospheric phase, so that the atmospheric correction precision is improved, and the monitoring precision of the surface deformation is improved. Specifically, as shown in fig. 2, a flowchart for refining atmosphere correction by combining a GACOS atmosphere model and an InSAR space-time filtering algorithm in the embodiment of the present application specifically includes the following steps:
s101: firstly, estimating an atmospheric delay phase of each SAR data acquisition date based on a GACOS atmospheric model, removing an atmospheric phase component related to terrain from a corresponding interference phase, and completing primary atmospheric correction of SAR sequence image data;
s102: performing time-dimensional low-pass filtering on the SAR data by adopting an InSAR space-time filtering algorithm to obtain the atmospheric delay and the orbit error of the main image;
s103: performing time-dimensional high-pass filtering nested space-dimensional low-pass filtering on SAR data by adopting an InSAR space-time filtering algorithm to obtain atmospheric delay, orbit error and DEM error of other auxiliary images;
s104: and removing the atmospheric delay and orbit error of the main image, the atmospheric delay and orbit error of the auxiliary image, and the DEM error from the interference phase to finish the refined atmospheric correction of the SAR sequence image data.
S110: and performing time series inversion on the annual deformation rate and the accumulated deformation of the SAR sequence image data after the refined atmosphere correction based on the StaMPS algorithm to obtain a surface deformation monitoring result of the monitored area.
Based on the above, the earth surface deformation monitoring method provided by the embodiment of the application provides the SHP StaMPS algorithm based on homogeneous point extraction, improves the classic StaMPS algorithm, performs adaptive multi-view processing based on a homogeneous point set on an interference phase before PS point screening, and effectively suppresses phase noise by distinguishing targets of two different scattering mechanisms, namely a permanent scattering point PS and a distributed scattering point DS, and avoids confusion of high-coherence signals of the PS point targets by phase noise of other points, thereby improving subsequent resolving accuracy. The interference coherence coefficient and the self-adaptive threshold value after deviation correction are adopted to assist the PS point screening, so that the integrity of the space deformation trend is maintained as much as possible while the low coherence points on the surfaces of the water body and the vegetation are effectively removed, and the PS point is prevented from being excessively screened. The atmosphere delay phase is finely corrected by combining a GACOS atmosphere model and InSAR space-time filtering, so that the accuracy of atmosphere correction is improved, and the monitoring accuracy of surface deformation is improved.
Please refer to fig. 3, which is a schematic structural diagram of a surface deformation monitoring system according to an embodiment of the present application. The surface deformation monitoring system 40 of the embodiment of the present application includes:
homogeneity point extraction module 41: the method is used for extracting the homogeneous points of SAR sequence image data in a monitoring area by adopting a Gamma confidence interval discrimination method based on a StaMPS algorithm to generate a homogeneous point set of the SAR sequence image data;
the multi-view processing module 42: the interference coherence coefficient is used for calculating the interference coherence coefficient of the SAR sequence image data based on the homogeneous point set, and self-adaptive multi-view processing is carried out on an interferogram of the SAR sequence image data according to the calculation result of the interference coherence coefficient to obtain a multi-view interference phase;
initial PS spot screening module 43: the method comprises the steps of calculating amplitude dispersion of SAR sequence image data, and performing PS candidate point screening on the SAR sequence image data according to the amplitude dispersion to obtain PS candidate points;
valid PS spot screening module 44: the adaptive threshold value is used for calculating the interference coherence coefficient based on the homogeneous point set, and the PS candidate points are screened by adopting the adaptive threshold value to obtain an effective PS point mask;
the atmosphere correction module 45: the system is used for performing atmospheric correction on the SAR sequence image data by adopting a GACOS model and an InSAR space-time filtering algorithm based on the effective PS point mask and the multi-view interference phase;
sequence inversion module 46: and the method is used for performing time series inversion on the deformation rate and the accumulated deformation of the atmospheric corrected SAR sequence image data based on the StaMPS algorithm to obtain the surface deformation monitoring result of the monitoring area.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A method for monitoring surface deformation, comprising:
based on StaMPS algorithm, adopting Gamma confidence interval discrimination method to extract homogeneous points of SAR sequence image data in a monitored area, and generating a homogeneous point set of the SAR sequence image data;
calculating an interference phase coherence coefficient between the secondary image and the main image in the SAR sequence image data based on the homogeneous point set;
performing self-adaptive multi-view processing on an interferogram of the SAR sequence image data according to the interference coherence coefficient calculation result to obtain a multi-view interference phase;
calculating the amplitude dispersion of the SAR sequence image, and screening PS candidate points according to the amplitude dispersion to obtain PS candidate points;
calculating an adaptive threshold of an interference coherence coefficient based on the homogeneous point set, and screening PS candidate points by adopting the adaptive threshold to obtain an effective PS point mask;
based on the effective PS point mask and the multi-view interference phase, carrying out atmospheric correction on the SAR sequence image data by adopting a GACOS model and an InSAR space-time filtering algorithm;
and performing time-series inversion on the deformation rate and the accumulated deformation of the atmospheric corrected SAR sequence image data based on the StaMPS algorithm to obtain a surface deformation monitoring result of the monitoring area.
2. The method for monitoring surface deformation according to claim 1, wherein the step of extracting the homogeneous points of the SAR sequence image data of the monitored area by using a Gamma confidence interval discrimination method comprises the following steps:
respectively determining a search window for each pixel in the SAR sequence image data, taking the pixel as a reference pixel, and calculating a reference sample mean value by using the time-dimensional sampling of the pixel
Figure FDA0003276659580000012
To be provided with
Figure FDA0003276659580000013
And the set alpha value calculates Gamma distribution confidence interval; the Gamma distribution confidence interval calculation formula is as follows:
Figure FDA0003276659580000011
wherein 1-alpha is the confidence level,
Figure FDA0003276659580000021
representing a standard Gamma G (NL,1) distribution
Figure FDA0003276659580000022
Performing quantile point division, wherein N is the image number of the SAR sequence image data, L is the view number of the SAR sequence image data, and a is L; sigma2The backscattering intensity of the SAR sequence image data is obtained;
judging whether the time dimension sample mean value of the neighborhood pixels in the search window falls into a Gamma distribution confidence interval or not, if so, judging that the neighborhood pixels and the reference pixels belong to the same statistical distribution, and obtaining an initial homogeneous pixel subset omega of the pixelsinit；
Using the initial homogeneous pixel subset ΩinitAverage of sample means of K pixels in the set against the reference sample mean
Figure FDA0003276659580000023
Performing updating according to the updated
Figure FDA0003276659580000024
Recalculating the Gamma distribution confidence interval;
according to the new Gamma distribution confidence interval, the initial homogeneous pixel subset omega is subjected toinitAnd (4) performing secondary judgment on the pixel in the group, reserving a neighborhood pixel which falls into a new Gamma distribution confidence interval and is connected with the reference pixel space, and generating a carefully-selected homogeneous pixel subset omega of the pixel.
3. The method for monitoring surface deformation according to claim 2, wherein the calculating interference coherence coefficients between the secondary image and the primary image in the SAR sequence image data based on the set of homogenous points comprises:
for each pixel in the SAR sequence image data, taking a homogeneous pixel subset omega of the pixel to perform interference coherence coefficient estimation to obtain the biased estimation of the interference coherence coefficient of the pixel; the interference coherence coefficient calculation formula is as follows:
Figure FDA0003276659580000025
wherein Z1(l)，Z2(l) Representing two SAR images Z for interference1，Z2A plurality of signals corresponding to the middle pixel l, wherein K is the number of pixels in the homogeneous pixel subset omega where the pixel is located;
performing deviation correction on the interference coherence coefficient biased estimation by adopting a double boosting method to obtain an unbiased interference coherence coefficient of the SAR sequence image data; the interference coherent coefficient of approximate unbiased estimation
Figure FDA0003276659580000031
Comprises the following steps:
Figure FDA0003276659580000032
wherein all points in the homogeneous pel subset Ω are denoted as X ═ X (X)1,x2,…,xK) K is the number of the homogeneous points, R is the number of times of Bootstrap sampling X repeatedly,
Figure FDA0003276659580000033
for Bootstrap copied samples, γ is a biased estimate of the interference coherence coefficient.
4. The surface deformation monitoring method according to claim 3, wherein the performing adaptive multi-view processing on the interferogram of the SAR sequence image data according to the interference coherence coefficient calculation result comprises:
preprocessing the SAR sequence image data to generate an interferogram of the SAR sequence image data; the pretreatment comprises the following steps:
performing space-time baseline analysis on the SAR sequence image data, and selecting a first-stage image with the shortest space and time baseline in the SAR sequence image data as a main image and other images as auxiliary images;
and registering the secondary image and the main image by utilizing the orbit parameters and the DEM model according to the pairing criterion of the PS-InSAR algorithm, and forming an interference pair by the secondary image and the main image to generate an interference pattern.
5. The surface deformation monitoring method according to claim 4, wherein the performing adaptive multi-view processing on the interferogram of the SAR sequence image data according to the interference coherence coefficient calculation result comprises:
for each pixel in the SAR sequence image data, taking a homogeneous pixel subset where the pixel is located as a unit, weighting an interferogram complex signal in the homogeneous pixel subset according to an interference coherence coefficient, and taking the phase of the interferogram complex signal to obtain a multi-view interference phase;
for any pixel p, the calculation formula of the multi-view interference phase is as follows:
Figure FDA0003276659580000041
where k is the number of homogeneous pixels in the homogeneous subset of pixels in which pixel P is located, CohiIs the interference coherence coefficient, Ifg, of the ith pixeliIs the complex signal of the interference pattern of the ith pixel.
6. The method for monitoring surface deformation according to any one of claims 1 to 5, wherein the calculating of the amplitude dispersion of the SAR sequence image data specifically comprises:
Figure FDA0003276659580000042
wherein D isARepresenting amplitude dispersion, σAAnd muARespectively, standard deviation and mean value of SAR sequence image data calculated along time dimension.
7. The method of claim 6, wherein the computing of the adaptive threshold of the interference coherence coefficient based on the set of homogenous points and the filtering of the PS candidate points using the adaptive threshold comprises:
according to the set real threshold value
Figure FDA0003276659580000043
And the number of homogeneous pixels in the homogeneous pixel subset, calculating a self-adaptive threshold value:
Figure FDA0003276659580000044
wherein sigmacohThe standard deviation of the lower bound of Cramer-Rao of the unbiased interference correlation coefficient;
for each pixel in the SAR sequence image data, according to the self-adaptive threshold value of the pixel, counting the sequence formed by the unbiased interference coherent coefficient in the time dimension, if the precise estimation value of the interference coherent coefficient in the time sequence
Figure FDA0003276659580000045
If the number of the points is larger than the set proportion, determining the point as an effective PS point;
and solving the intersection of all the effective PS points and the PS candidate points to obtain an effective PS point mask.
8. The method for monitoring surface deformation according to claim 7, wherein the performing atmospheric correction on the SAR sequence image data by using a GACOS model and an InSAR space-time filtering algorithm based on the effective PS point mask and the multi-view interference phase comprises:
performing space correlation phase component estimation on the SAR sequence image data by adopting a StaMPS algorithm, and performing phase noise estimation on the effective PS point mask;
and screening the effective PS point mask by combining the amplitude dispersion and the phase noise estimation, and performing three-dimensional phase unwrapping on the screened PS points:
and based on the phase unwrapping result of the PS point, filtering out atmospheric delay and orbit error phases from the interference phase by adopting a GACOS model and an InSAR space-time filtering algorithm, and performing atmospheric correction on the SAR sequence image data.
9. The method for monitoring surface deformation according to claim 8, wherein the atmospheric correction of the SAR sequence image data by using the GACOS model and the InSAR spatiotemporal filtering algorithm comprises:
estimating the atmospheric delay phase of each group of interference pairs by adopting a GACOS model, and removing the atmospheric phase component related to the terrain from the corresponding interference phase;
performing time-dimensional low-pass filtering on the SAR sequence image data by adopting an InSAR space-time filtering algorithm to obtain the atmospheric delay and the orbit error of the main image;
performing time-dimensional high-pass filtering nested space-dimensional low-pass filtering on the SAR data by adopting an InSAR space-time filtering algorithm to obtain atmospheric delay, orbit error and DEM error of the secondary image;
and removing the atmospheric delay and orbit error of the main image, the atmospheric delay and orbit error of the auxiliary image and the DEM error from the interference phase, and performing refined atmospheric correction on the SAR sequence image data.
10. A surface deformation monitoring system, comprising:
a homogeneity point extraction module: the method is used for extracting the homogeneous points of SAR sequence image data in a monitoring area by adopting a Gamma confidence interval discrimination method based on a StaMPS algorithm to generate a homogeneous point set of the SAR sequence image data;
a multi-view processing module: the interference coherence coefficient is used for calculating the interference coherence coefficient between the secondary image and the main image in the SAR sequence image data based on the homogeneous point set, and self-adaptive multi-view processing is carried out on an interferogram of the SAR sequence image data according to the interference coherence coefficient calculation result to obtain a multi-view interference phase;
initial PS spot screening module: the amplitude dispersion degree of the SAR sequence image is calculated, and PS candidate point screening is carried out according to the amplitude dispersion degree to obtain PS candidate points;
effective PS point screening module: the adaptive threshold value is used for calculating interference coherence coefficients based on the homogeneous point set, and the PS candidate points are screened by adopting the adaptive threshold value to obtain an effective PS point mask;
an atmosphere correction module: the system is used for performing atmospheric correction on the SAR sequence image data by adopting a GACOS model and an InSAR space-time filtering algorithm based on the effective PS point mask and the multi-view interference phase;
a sequence inversion module: and the method is used for performing time series inversion on the deformation rate and the accumulated deformation of the atmospheric corrected SAR sequence image data based on the StaMPS algorithm to obtain the surface deformation monitoring result of the monitoring area.
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