CN113447927B - Time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis - Google Patents

Time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis Download PDF

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CN113447927B
CN113447927B CN202110724307.6A CN202110724307A CN113447927B CN 113447927 B CN113447927 B CN 113447927B CN 202110724307 A CN202110724307 A CN 202110724307A CN 113447927 B CN113447927 B CN 113447927B
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coherence
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CN113447927A (en
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何倩
王瑞
吴志聪
杨超云
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China University of Mining and Technology CUMT
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    • 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
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C5/00—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization

Abstract

The invention provides a time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis, which is suitable for earth surface subsidence monitoring. Firstly, identifying homogeneous pixels according to the average amplitude of a time sequence SAR image, and constructing a coherent matrix; then optimizing the phase and calculating the time coherence based on the PT algorithm of coherence weighting; and taking a pixel larger than a homogeneous pixel threshold as a DS candidate point, carrying out layered extraction on DS by utilizing a time coherence threshold, carrying out point-by-point analysis by adopting a pearson correlation coefficient, and inhibiting error propagation of a low-quality point target. And finally, selecting a PS point by using an amplitude deviation threshold value, and carrying out deformation calculation by combining DS and PS. Under the condition of ensuring the measurement accuracy, the method increases the space density of the measurement points, obtains more detailed deformation results, and can provide real and reliable information for the protection and treatment of ground subsidence.

Description

Time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis
Technical field:
the invention relates to a time sequence InSAR earth surface subsidence monitoring method, which is particularly suitable for the technical field of earth surface subsidence monitoring and is based on point target layering analysis.
The background technology is as follows:
earth surface subsidence is a common geological disaster, and various scholars at home and abroad apply the time sequence SAR interferometry (Interferometric Synthetic Aperture Radar, inSAR) to earth surface subsidence monitoring. After the second generation permanent scatterer technique SqueeSAR was proposed since 2011, the time series InSAR technique combining a permanent scatterer (Permanent scatterer, PS) and a distributed scatterer (Distributed Scatterers, DS) was greatly developed. Unlike PS, DS belongs to image pixels of medium coherence regions in interference pairs, where many adjacent pixels have similar reflectivity. Notably, DS is affected by temporal, geometric decorrelation, resulting in a low signal-to-noise ratio of the interference phases. Therefore, DS selection and phase optimization are extremely critical steps.
Studies have shown that the interference phase reconstructed by the phase triangle (Phase Triangulation, PT) algorithm is less than the interference phase noise of the spatial filtering, and the phase unwrapping process is more robust; the phase weights with higher temporal coherence are larger and the coherence weighted PT algorithm is more robust to decorrelation than the equal weight PT algorithm. Thus, the coherence weighted PT algorithm is more advantageous in terms of phase optimization. In the selection of DS, temporal coherence is an important parameter.
However, the number of DSs is different at different temporal coherence thresholds. Specifically, the larger the value of the time coherence κ, the smaller the DS, and detailed deformation information cannot be provided. The smaller the kappa number, the more DS, which has an adverse effect on the measurement accuracy. Therefore, there is an urgent need for a method of selecting as many DS pixels as possible from the medium-phase dry pixels without losing measurement accuracy.
Disclosure of Invention
Aiming at the defects of the prior art, the time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis is provided, more DS can be selected as much as possible from medium-phase related pixels under the condition of not losing measurement precision, a coherence matrix is constructed by utilizing the identified homogeneous pixels, DS phases are optimized based on a coherence weighted PT algorithm, layering processing is carried out according to time coherence, more measurement points are extracted, the correlation of data in time and space is measured by utilizing a Pirson correlation coefficient PCC, and the reliability of monitoring is effectively ensured.
In order to achieve the technical effects, the time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis comprises the following steps:
firstly, identifying homogeneous pixels according to the average amplitude of a time sequence SAR image, and constructing a coherent matrix according to the homogeneous pixels; then optimizing the phase and calculating the time coherence based on the PT algorithm of coherence weighting; and taking a pixel larger than a homogeneous pixel threshold as a DS candidate pixel, carrying out layered extraction on the DS pixel by utilizing a time coherence threshold, carrying out point-by-point analysis by adopting a pearson correlation coefficient to obtain a reliable DS pixel, inhibiting error propagation of a low-quality point target, replacing a corresponding phase value of an original SAR image by a phase optimization value of the DS pixel selected by a layered strategy, selecting a permanent scatterer pixel PS by utilizing an amplitude dispersion threshold, and finally carrying out deformation calculation on the phases of the selected DS pixel and PS pixel according to a conventional small baseline method to estimate the surface subsidence rate of the DS pixel and the PS pixel.
The method comprises the following specific steps:
s1, selecting a time sequence SAR image covering a research area, performing multi-view and fine registration by taking a first scene image in the time sequence SAR image as a public main image, cutting all images, identifying statistical homogeneous pixels SHP by using a fast statistical homogeneous pixel selection FaSHPS algorithm according to the average amplitude of the time sequence SAR image, and selecting candidate pixels of a distributed scattering body DS by using a preset homogeneous pixel threshold;
s2, constructing a coherent matrix T of SAR data according to the homogeneous pixels;
s3, optimizing the DS phase of the distributed scatterer and calculating the time coherence kappa by utilizing a phase triangulation PT algorithm of coherence weighting according to a coherence matrix T of SAR data;
s4, selecting a reliable DS pixel by adopting a layering strategy in a time coherence value in candidate pixels of which the pixels are distributed scatterers DS: pixels with temporal coherence kappa satisfying 0.7-1 in the whole research area are defined as high-coherence pixels, pixels with temporal coherence kappa satisfying 0.5-0.7 are defined as medium-coherence pixels, pixels with temporal coherence kappa satisfying 0-0.5 are defined as low-coherence pixels, and pixels with temporal coherence kappa smaller than 0-0.5 have adverse effects on subsequent deformation estimation due to lower coherence of the pixels with 0-0.5 are abandoned; then selecting high-coherence pixels in the whole research area, dividing the medium-coherence pixels into two layers with 0.5-0.6,0.6-0.7 by a step length, judging the relative phase quality of two adjacent pixels in the image of the research area by using the pearson correlation coefficient PCC, and taking pixels with the pearson correlation coefficient PCC larger than 0.5 as reliable DS pixels, thereby increasing the space density of deformation measurement of the research area;
s5, replacing the corresponding phase value of the original SAR image with the phase optimization value of the DS pixel selected by the layering strategy;
s6, generating an interference pattern according to free combination of a preset time base line and a vertical base line threshold value, and establishing a small base line set of multiple main images. And selecting a permanent scatterer pixel PS by using an amplitude deviation threshold, performing deformation calculation on the phases of the selected DS pixel and the selected PS pixel according to a conventional small baseline method, and estimating the earth surface subsidence rates of the DS pixel and the PS pixel.
Further, the identification method for counting the homogeneous pixels SHP comprises the following steps: average amplitude of N SAR images according to the central limit theoremObeying a normal distribution of expected u (p), variance Var (A (p))/N; for multi-view images, L is the number of views,average amplitude->The confidence interval of (2) is:
wherein P {.cndot. } represents the probability,is the 1-alpha/2 upper quantile in the standard normal distribution. Given a level of saliency α, the pixels falling within that interval are considered homogenous with pixel p.
Further, the calculation formula of the coherence matrix T is:
wherein E [ YY ] H ]Represents YY H Y= [ Y ] 1 ,y 2 ,…,y N ] T Complex data representing N SAR images normalized, E [ |y i | 2 ]＝1，N P Representing the number of homogeneous pixels, Ω represents the SHP set, H the conjugate transpose operator, the coherence matrix T is an N multiplied by N Hermitian matrix, and the matrix elements comprise the coherence and the interference phase between SAR images.
Further, the calculation process of the phase triangulation PT algorithm weighted by the coherence matrix is as follows:
where θ represents the optimized phase of DS,by giving a larger weight to the interference phase with high coherence, the optimized phase has strong robustness, and the influence of decorrelation is reduced.
Further, the calculation process of the time coherence κ is:
wherein θ= [ θ ] 1 ,θ 2 ,…θ N ] T ，θ m And theta n Respectively representing optimized phases of the mth SAR image and the nth SAR image; the time coherence kappa is also called as goodness of fit and is an evaluation index of DS phase optimization, and the value range of kappa is [0,1 ]]The larger the kappa number is, the better the coherence of the interference phase observance quantity is and the higher the signal to noise ratio is.
Further, the calculation formula of the pearson correlation coefficient PCC is:
wherein ρ is i,j The pearson correlation coefficient for pixel i, j,and->The phase values of picture elements i, j in the s Jing Chafen interferogram, +.>And->The phase averages of pixels i, j over the differential interferograms used for the small baseline processing, respectively. PCC describes the correlation and phase dispersion of two pixels in time and space, with a value range of [0,1]0 represents complete uncorrelation and 1 represents complete correlation. ρ i,j And the pixel values are more than or equal to 0.5, so that the pixels i and j have stronger correlation and the phase is more stable. The invention has the beneficial effects that:
the method effectively increases the space density of the measuring points, builds the coherent matrix according to the identified homogeneous pixels, and is beneficial to avoiding the problem of deviation caused by participation of heterogeneous pixels in matrix estimation. And a layering analysis strategy is adopted, so that the increase of measurement points is facilitated. The PCC is utilized for quality control, so that error propagation is avoided, and point location resolving quality is ensured. The method can obtain more monitoring points on the ground surface, so that the reliable data volume is increased, more detailed deformation information is provided for the protection and treatment of ground subsidence under the condition of ensuring the measurement precision, the steps are simple, the monitoring precision is high, and the method has wide practicability.
Drawings
FIG. 1 is a flow chart of a time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis;
FIG. 2 (a) is a graph of average amplitude for an embodiment of the present invention;
FIG. 2 (b) is a diagram of the number of SHPs in an embodiment of the present invention;
FIG. 3 is a time coherence map of an embodiment of the present invention;
FIG. 4 (a) is a schematic representation of the surface subsidence rate obtained by a conventional treatment process;
FIG. 4 (b) is a schematic representation of the surface subsidence rate obtained by the method of the present invention.
FIG. 5 is a plot of scattered points and regression for the same name point sedimentation rate obtained by the conventional treatment method and the two methods of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be fully described below with reference to the accompanying drawings.
As shown in FIG. 1, the time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis comprises the following steps: firstly, identifying homogeneous pixels according to the average amplitude of a time sequence SAR image, and constructing a coherent matrix according to the homogeneous pixels; then optimizing the phase and calculating the time coherence based on the PT algorithm of coherence weighting; and taking a pixel larger than a homogeneous pixel threshold as a DS candidate pixel, carrying out layered extraction on the DS pixel by utilizing a time coherence threshold, carrying out point-by-point analysis by adopting a pearson correlation coefficient to obtain a reliable DS pixel, inhibiting error propagation of a low-quality point target, replacing a corresponding phase value of an original SAR image by a phase optimization value of the DS pixel selected by a layered strategy, selecting a permanent scatterer pixel PS by utilizing an amplitude dispersion threshold, and finally carrying out deformation calculation on the phases of the selected DS pixel and PS pixel according to a conventional small baseline method to estimate the surface subsidence rate of the DS pixel and the PS pixel.
The method comprises the following specific steps:
s1, selecting a time sequence SAR image covering a research area, performing multi-view and fine registration by taking a first scene image in the time sequence SAR image as a public main image, cutting all images, identifying statistical homogeneous pixels (Statistically Homogeneous Pixels, SHP) by utilizing a fast statistical homogeneous pixel selection (Fast Statistically Homogeneous Pixel Selection, faSHPS) algorithm according to the average amplitude of the time sequence SAR image, and selecting candidate pixels of a distributed scatterer (Distributed Scatterers, DS) by using a preset homogeneous pixel threshold;
the identification method for the statistical homogeneous pixel SHP comprises the following steps: average amplitude of N SAR images according to the central limit theoremObeying a normal distribution of expected u (p), variance Var (A (p))/N; for multi-view images, L is the number of views,average amplitude->The confidence interval of (2) is:
wherein P {.cndot. } represents the probability,is the 1-alpha/2 upper quantile in the standard normal distribution. Given a significance level α, consider that the pixels falling within the interval are homogenous with the pixel p;
s2, constructing a coherent matrix T of SAR data according to the homogeneous pixels;
the calculation formula of the coherence matrix T is:
wherein E [ YY ] H ]Represents YY H Y= [ Y ] 1 ,y 2 ,…,y N ] T Complex data representing N SAR images normalized, E [ |y i | 2 ]＝1，N P Representing the number of homogeneous pixels, Ω represents the SHP set, H the method is a conjugate transposed operator, a coherent matrix T is an N multiplied by N Hermitian matrix, and matrix elements comprise coherence and interference phases between SAR images;
s3, optimizing the DS phase of the distributed scatterer and calculating the time coherence kappa by utilizing a phase triangulation (Phase Triangulation, PT) algorithm of coherence weighting according to a coherence matrix T of SAR data;
the phase triangulation (Phase Triangulation, PT) algorithm using coherent matrix weighting is calculated as:
where θ represents the optimized phase of DS,by giving a larger weight to the interference phase with high coherence, the optimized phase has strong robustness, and the influence of decorrelation is reduced;
the calculation process of the time coherence kappa is as follows:
wherein θ= [ θ ] 1 ,θ 2 ,…θ N ] T ，θ m And theta n Respectively representing optimized phases of the mth SAR image and the nth SAR image; the time coherence kappa is also called as goodness of fit and is an evaluation index of DS phase optimization, and the value range of kappa is [0,1 ]]The larger the kappa number is, the better the coherence of the interference phase observed quantity is, and the higher the signal-to-noise ratio is;
s4, selecting a reliable DS pixel by adopting a layering strategy in a time coherence value in candidate pixels of which the pixels are distributed scatterers DS: pixels with temporal coherence kappa satisfying 0.7-1 in the whole research area are defined as high-coherence pixels, pixels with temporal coherence kappa satisfying 0.5-0.7 are defined as medium-coherence pixels, pixels with temporal coherence kappa satisfying 0-0.5 are defined as low-coherence pixels, and pixels with temporal coherence kappa smaller than 0-0.5 have adverse effects on subsequent deformation estimation due to lower coherence of the pixels with 0-0.5 are abandoned; then selecting high-coherence pixels in the whole research area, dividing the medium-coherence pixels into two layers of 0.5-K <0.6,0.6-K <0.7 by a step length of 0.1, judging the relative phase quality of two adjacent pixels in the image of the research area by using a pearson correlation coefficient (Pearson Correlation Coefficient, PCC), and taking pixels with the pearson correlation coefficient PCC larger than 0.5 as reliable DS pixels, thereby increasing the space density of deformation measurement of the research area and inhibiting error propagation of a low-quality point target; the calculation formula of the pearson correlation coefficient PCC is:
wherein ρ is i,j The pearson correlation coefficient for pixel i, j,and->The phase values of picture elements i, j in the s Jing Chafen interferogram, +.>And->The phase averages of pixels i, j over the differential interferograms used for the small baseline processing, respectively. PCC describes the correlation and phase dispersion of two pixels in time and space, with a value range of [0,1]0 represents complete uncorrelation and 1 represents complete correlation. ρ i,j More than or equal to 0.5 shows that the pixels i and j have stronger correlation and more stable phase;
s5, replacing the corresponding phase value of the original SAR image with the phase optimization value of the DS pixel selected by the layering strategy;
s6, selecting permanent scatterer pixels (Permanent Scatterers, PS) by using an amplitude dispersion threshold, performing deformation calculation on the selected DS pixels and the selected PS pixels according to a conventional small baseline method, estimating the rates of surface subsidence deformation of the DS pixels and the PS pixels, and finally obtaining the surface subsidence information of the whole research area.
Embodiment 1,
A time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis comprises the following steps:
step 1: a 16 Jing Shixu Sentinel-1A image (n=16) covering the investigation region was selected and registered with the first scene image 20161004 as the common main image. After multi-view and fine registration, 800 x 800 pels are cropped.
Step 2: the average amplitude was calculated from the trimmed Sentinel-1A image, as shown in FIG. 2 (a). SHP is selected based on the average amplitude, and a foundation is laid for constructing a coherent matrix. Fig. 2 (b) is the number of SHPs identified at α=5%, and each pixel value in the figure represents the number of SHPs within a 15×15 window centered on that pixel. As can be seen from the figure, the number of SHPs is related to the type of surface coverage to which it belongs. The scattering of the building structure is strong, and the SHP quantity is small; the farmland is a good distributed scatterer, and the SHP quantity is large.
Step 3: and constructing a coherence matrix according to the identified SHP, and optimizing the phase by using a coherence weighted PT algorithm. And then calculating the time coherence kappa, and carrying out quality evaluation on the optimized phase. As can be seen from fig. 3, the time coherence of the built area is greater, while the time coherence of the vegetation is less.
Step 4: the SHP threshold of this embodiment is set to 20, and pixels with SHP greater than 20 are used as DS candidate points. Among the candidate points, pixels with 0.ltoreq.κ <0.5 are discarded. First, pixels with 0.7.ltoreq.kappa.ltoreq.1 are selected, which are generally considered to have high coherence. Then dividing the pixels with medium coherence of 0.5-0.7 into two layers [0.5,0.6 ], [0.6, 0.7), measuring the correlation of the two pixels in time and space by PCC, and controlling the quality with ρ of 0.5 or more. And for the selected DS, replacing the corresponding phase value of the original SAR image with the optimized value of the DS.
Step 5: and generating an interference pattern according to the free combination of the preset time base line threshold (120 days) and the vertical base line threshold (150 m), and establishing a small base line set of multiple main images.
Step 6: the PS points are extracted using an amplitude dispersion threshold (0.22). And connecting the selected DS and PS by adopting a Delaunay triangle network, then performing space-time filtering and phase unwrapping, and calculating the elevation error and sedimentation rate of each reliable pixel by using a weighted least square method. FIG. 4 (a) shows the sedimentation rate obtained by conventional treatment, namely, DS selected by using only a high temporal coherence value (0.7. Ltoreq.kappa.ltoreq.1), and obtained by combining PS. FIG. 4 (b) shows the sedimentation rate obtained by the method of the present invention; i.e. based on the results obtained by the hierarchical analysis of the point targets. It is apparent that fig. 4 (b) has more measurement points than fig. 4 (a), showing the sedimentation information of the present embodiment in more detail. The density of the measurement points in fig. 4 (b) is about 1.3 times that of fig. 4 (a).
Step 7: and taking the completely coincident measuring points as homonymous points, and carrying out correlation analysis according to the sedimentation rate of the homonymous points, thereby comparing the difference between monitoring results of the two methods (the conventional processing method and the method of the invention). As can be seen from fig. 5, the correlation coefficient R of the sedimentation rate is 0.968, which indicates that the monitoring results of the two methods of fig. 4 (a-b) are highly similar, thereby demonstrating that the method of the present invention is feasible and reliable.

Claims (6)

1. A time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis is characterized by comprising the following steps:
s1, selecting a time sequence SAR image covering a research area, performing multi-view and fine registration by taking a first scene image in the time sequence SAR image as a public main image, cutting all images, identifying statistical homogeneous pixels SHP by using a fast statistical homogeneous pixel selection FaSHPS algorithm according to the average amplitude of the time sequence SAR image, and selecting candidate pixels of a distributed scattering body DS by using a preset homogeneous pixel threshold;
s2, constructing a coherent matrix T of SAR data according to the homogeneous pixels;
s3, optimizing the DS phase of the distributed scatterer and calculating the time coherence kappa by utilizing a phase triangulation PT algorithm of coherence weighting according to a coherence matrix T of SAR data;
s4, selecting a reliable DS pixel by adopting a layering strategy in a time coherence value in candidate pixels of which the pixels are distributed scatterers DS: pixels with temporal coherence kappa satisfying 0.7-1 in the whole research area are defined as high-coherence pixels, pixels with temporal coherence kappa satisfying 0.5-0.7 are defined as medium-coherence pixels, pixels with temporal coherence kappa satisfying 0-0.5 are defined as low-coherence pixels, and the pixels with the temporal coherence kappa being 0-0.5 have adverse effects on subsequent deformation estimation, so that pixels with the temporal coherence kappa being 0-0.5 are abandoned; then selecting high-coherence pixels in the whole research area, dividing the medium-coherence pixels into two layers with 0.5-k <0.6,0.6-k <0.7 by a step length of 0.1, judging the relative phase quality of two adjacent pixels in the image of the research area by using the pearson correlation coefficient PCC, and taking pixels with the pearson correlation coefficient PCC larger than 0.5 as reliable DS pixels, thereby increasing the space density of deformation measurement of the research area;
s5, replacing the corresponding phase value of the original SAR image with the phase optimization value of the DS pixel selected by the layering strategy;
s6, generating an interference pattern according to free combination of a preset time base line and a vertical base line threshold value, establishing a small base line set of multiple main images, selecting a permanent scatterer pixel PS by using an amplitude dispersion threshold value, performing deformation calculation on phases of the selected DS pixel and PS pixel according to a conventional small base line method, and estimating the earth surface subsidence rates of the DS pixel and the PS pixel.
2. The method for monitoring the surface subsidence of the time sequence InSAR based on the point target layering analysis is characterized in that the method for identifying the statistical homogeneous pixel SHP is as follows: average amplitude of N SAR images according to the central limit theoremObeying a normal distribution of expected u (p), variance Var (A (p))/N; for multi-view images, L is the number of views,average amplitude->The confidence interval of (2) is:
wherein P {.cndot. } represents the probability,is 1-alpha/2 upper part of the site in standard normal distribution; given a level of saliency α, the pixels falling within that interval are considered homogenous with pixel p.
3. The method for monitoring the surface subsidence of the time sequence InSAR based on the point target layering analysis according to claim 1, wherein a calculation formula of a coherence matrix T is as follows:
wherein E [ YY ] H ]Represents YY H Y= [ Y ] 1 ,y 2 ,…,y N ] T Complex data representing N SAR images normalized, E [ |y i | 2 ]＝1，N P Representing the number of homogeneous pixels, Ω represents the SHP set, H the conjugate transpose operator, the coherence matrix T is an N multiplied by N Hermitian matrix, and the matrix elements comprise the coherence and the interference phase between SAR images.
4. The method for monitoring the surface subsidence of the time sequence InSAR based on the point target layering analysis according to claim 1, wherein the calculation process of a phase triangulation PT algorithm weighted by a coherence matrix is as follows:
where θ represents the optimized phase of DS,by giving a larger weight to the interference phase with high coherence, the optimized phase has strong robustness, and the influence of decorrelation is reduced.
5. The method for monitoring the surface subsidence of the time sequence InSAR based on the point target layering analysis, which is disclosed by claim 4, is characterized in that the calculation process of the time coherence kappa is as follows:
wherein θ= [ θ ] 1 ,θ 2 ,…θ N ] T ，θ m And theta n Respectively representing optimized phases of the mth SAR image and the nth SAR image; the time coherence kappa is also called as goodness of fit and is an evaluation index of DS phase optimization, and the value range of kappa is [0,1 ]]The larger the kappa number is, the better the coherence of the interference phase observance quantity is and the higher the signal to noise ratio is.
6. The time sequence InSAR surface subsidence monitoring method based on point target layering analysis according to claim 1, wherein the calculation formula of the pearson correlation coefficient PCC is as follows:
wherein ρ is i,j The pearson correlation coefficient for pixel i, j,and->The phase values of picture elements i, j in the s Jing Chafen interferogram, +.>And->The phases of pixels i, j on the differential interferograms used for the small baseline processing, respectivelyThe average value; PCC describes the correlation and phase dispersion of two pixels in time and space, with a value range of [0,1]0 represents complete uncorrelation, 1 represents complete correlation; ρ i,j And the pixel values are more than or equal to 0.5, so that the pixels i and j have stronger correlation and the phase is more stable.
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