CN117826148A - Method and system for identifying coherent point - Google Patents
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Abstract
The invention discloses a method and a system for identifying a coherent point, which belong to the technical field of radio measurement, wherein the method comprises the following steps: obtaining image data of a synthetic aperture radar; judging whether the target point of the image data meets the following conditions: the amplitude dispersion index of the time series is smaller than a first threshold, the autocorrelation coefficient is larger than a second threshold, and the average coherence coefficient is larger than a third threshold; if yes, the target point is a coherent point. The method adopts a mode of multiple detection indexes to identify the coherent points, so that the influence of time incoherence and space incoherence can be reduced, the non-permanent scatterer targets with serious incoherence can be removed, the density and the monitoring precision of the coherent points can be improved, and the precision of sedimentation monitoring can be improved.
Description
Technical Field
The invention relates to the technical field of radio measurement, in particular to a method and a system for identifying a coherent point.
Background
The coherence point, also called permanent scatterer (Persistent Scatterer, PS), is a point target (a single pixel or a group of pixels) that radiates stable in time. Such point targets are characterized by strong reflection (high backscattering) and high coherence during the observation period. The goal of radiation stabilization is urban infrastructure (e.g., buildings, bridges, dams, greenhouses, metal buildings, etc.) or natural objects (e.g., exposed rocks, etc.).
With the increase of subway operation mileage, the new subway scale is reduced. Gradually, the subway monitoring market is changed from construction period monitoring to subway operation and maintenance period monitoring. However, the deformation condition characteristics of the subway infrastructure (the overhead section and the roadbed section) are special during operation and maintenance. The identification and extraction of the coherent point targets of the subway infrastructure in the range of the urban built-up area have certain difficulty only by statistically analyzing the amplitude information of the images. Conventional PS-InSAR coherent point identification generally adopts a single permanent scatterer identification method, such as a coherence coefficient identification method, but the error rate of the single permanent scatterer identification method is higher; while coherence point identification is a precondition for sedimentation monitoring. Therefore, based on the traditional PS-InSAR coherent point identification method, some coherent points are difficult to identify, and the settlement monitoring precision of the track traffic area along the line is needed to be improved.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a method and a system for identifying coherent points, which adopt a mode of multiple detection indexes to improve the monitoring precision of the coherent points.
The invention discloses a method for identifying coherent points, which comprises the following steps: obtaining image data of a synthetic aperture radar; judging whether the target point of the image data meets the following conditions: the amplitude dispersion index of the time series is smaller than a first threshold, the autocorrelation coefficient is larger than a second threshold, and the average coherence coefficient is larger than a third threshold; if yes, the target point is a coherent point.
Preferably, the amplitude dispersion index is expressed as:
D＝σ/μ (1)
wherein D represents an amplitude discrete index of the target point time series, sigma is a standard deviation of the target point amplitude time series, and mu is a mean value of the target point amplitude time series.
Preferably, the average coherence coefficient is expressed as:
γ k expressed as the coherence coefficient of the xth pixel under the kth interference pair, M (i, j) and S (i, j) are the master-slave images constituting the kth interference pair, respectively, expressed as performing conjugate multiplication, M and n are the magnitudes of the sliding window in the lateral and longitudinal directions, respectively,n is the average coherence coefficient of the target point, and is expressed as the logarithm of the adjacent interference pair of the coherence point.
Preferably, the coherence coefficient is used to reject non-permanent scatterer targets that are severely uncorrelated.
Preferably, the image data includes a time series after registration processing;
the amplitude dispersion index is used for selecting a permanent scatterer with a change in time sequence smaller than a first threshold value so as to reduce the influence of time incoherence;
the autocorrelation coefficients are used to reduce the effects of spatial incoherence.
Preferably, the method of obtaining the autocorrelation coefficient includes:
preprocessing SAR images and registering images in sequence;
performing Fourier transformation on the SAR images after image registration to obtain the representation of the SAR images in a frequency domain;
an amplitude signal time series is extracted from the frequency domain representation and an autocorrelation coefficient of the amplitude signal time series is calculated.
Preferably, the autocorrelation coefficient is calculated by:
Y(t，s)＝E(X t -μ t )(X s -μ s ) (5)
wherein ρ (t, s) is expressed as an autocorrelation coefficient of the time series of amplitude signals, t and s respectively represent the moments of the time series, X t Phase denoted as time phase at time t, μ t Expressed as the mean of the time phases at time t, D () represents the taking of the variance, E () represents the mathematical expectation, and Y (t, s) represents the autocovariance function.
Preferably, the target point is used for detecting ground subsidence, and the method for detecting ground subsidence comprises the following steps:
after removing phase shift and atmospheric effect in the target point interferogram, calculating the deformation rate of the target point;
the ground subsidence is calculated from the vertical component of the deformation rate.
The invention also provides a system for realizing the method, which comprises an acquisition module and an identification module, wherein the acquisition module is used for acquiring image data of the synthetic aperture radar; the identification module is used for judging whether the target point of the image data meets the following conditions: the amplitude dispersion index of the time series is smaller than a first threshold, the autocorrelation coefficient is larger than a second threshold, and the coherence coefficient is larger than a third threshold; if yes, the target point is a coherent point.
Preferably, the system further comprises a subsidence analysis module, wherein the subsidence analysis module is used for calculating the deformation rate of the target point after removing the phase shift and the atmospheric effect in the target point interferogram; the ground subsidence is calculated from the vertical component of the deformation rate.
Compared with the prior art, the invention has the beneficial effects that: the method adopts a mode of multiple detection indexes to identify the coherent points, so that the influence of time incoherence and space incoherence can be reduced, the non-permanent scatterer targets with serious incoherence can be removed, the density and the monitoring precision of the coherent points can be improved, and the precision of sedimentation monitoring can be improved.
Drawings
FIG. 1 is a flow chart of a method of coherent point identification of the present invention;
fig. 2 is a system logic block diagram of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is described in further detail below with reference to the attached drawing figures:
a method of coherent point identification, as shown in fig. 1, comprising the steps of:
step 101: image data of the synthetic aperture radar, also referred to as SAR image, is obtained. The image data includes a time sequence after registration processing, and the registration processing is the prior art, which is not described in detail in the present invention.
Step 102: judging whether the target point of the image data meets a first condition or not: the amplitude dispersion index of the time series is smaller than a first threshold, the autocorrelation coefficient is larger than a second threshold, and the average coherence coefficient is larger than a third threshold;
if yes, go to step 103: the target point is a coherent point.
If not, the target point is not a coherent point.
The density and the monitoring precision of the coherent points are improved by adopting a mode of multiple detection indexes, and the precision of sedimentation monitoring is improved. The amplitude discrete index threshold method and the autocorrelation coefficient threshold method can improve the coherent point density, and the coherence coefficient threshold method can further improve the monitoring precision.
The amplitude discrete index is used for selecting a permanent scatterer with a change in time sequence smaller than a first threshold T1, and selecting a target point with a small change in time sequence so as to reduce the influence of time incoherence. And selecting a high-reliability permanent scatterer based on the statistical relationship between amplitude deviation and phase deviation under the SAR image with large data volume.
The amplitude dispersion index can be expressed as:
D＝σ/μ (1)
wherein D represents an amplitude discrete index of the target point time series, sigma is a standard deviation of the target point amplitude time series, and mu is a mean value of the target point amplitude time series. The method comprises the steps of calculating variance and mean of the same target point on a plurality of images of a time sequence for time sequence SAR data after registration processing, taking the ratio of the variance and the mean as a measure of a permanent scatterer identification method, and selecting the target point smaller than a first threshold value as a permanent scatterer, namely D < T1.
The autocorrelation coefficients are used to reduce the effects of spatial incoherence, also known as point-target detection. It is known from the principle analysis of the permanent scatterer that there is high brightness due to the corner reflector effect. The echo signals in time are kept constant, and the target point with stable phase characteristics can be identified as a candidate permanent scatterer. The SAR image can be processed to obtain the time sequence autocorrelation coefficient between amplitude signals, and a certain average spectral coherence is set to screen out high-quality point targets. The method comprises the following specific steps:
step 301: preprocessing the SAR image (such as radiation correction, atmospheric correction, terrain correction and the like), and performing image registration on the time sequence SAR image to obtain a time sequence interference pair. The preprocessing can improve the quality and comparability of the images; image registration ensures that it is under the same geographical coordinates, which is a precondition for calculating spectral correlation.
Step 302: and carrying out Fourier transformation on the SAR images after image registration to obtain the representation of the SAR images in a frequency domain so as to carry out further spectral analysis and processing. SAR images record scattered signals of radar waves on ground features, and the amplitude and phase information of the signals actually comprise frequency domain characteristics of the ground features.
Step 303: an amplitude signal time series is extracted from the frequency domain representation and an autocorrelation coefficient of the amplitude signal time series is calculated. The autocorrelation coefficients are used to calculate the temporal autocorrelation between amplitude signals and measure the degree of correlation of the time feature amplitude information (backscatter signal) in different SAR images. The autocorrelation coefficients reflect the spectral correlation. The correlation coefficient calculation method has wide application in the fields of signal processing, data analysis and the like.
The autocorrelation coefficients are calculated in the following manner:
Y(t，s)＝E(X t -μ t )(X s -μ s ) (5)
wherein ρ (t, s) is expressed as an autocorrelation coefficient of the time series of amplitude signals, t and s respectively represent different moments of the time series, X t Phase denoted as time phase at time t, μ t Expressed as the mean of the time phases at time t, D () represents the taking of the variance, E () represents the mathematical expectation, and Y (t, s) represents the autocovariance function. The amplitude signal time series is expressed as: { X t ,t∈T}。
The average coherence coefficient is used to reject non-permanent scatterer targets that are severely uncorrelated. Ensuring that a high density, high quality permanent scatterer is ultimately obtained. The points with high spatial coherence on the SAR image can be considered to correspond to the high coherence areas of buildings and the like on the ground through statistical analysis. The average coherence coefficient can be further calculated to select a point target with a higher coherence coefficient. Coherence is the most intuitive criterion for measuring interference phase noise, and the coherence coefficient method estimates its average coherence coefficient from values of neighboring pixels around a target pixel.
The average coherence coefficient is expressed as:
γ k expressed as the coherence coefficient of the target point under the kth interference pair, M (i, j) and S (i, j) are the master-slave images constituting the kth interference pair, respectively, expressed as performing conjugate multiplication, M and n are the sizes of the sliding window in the transverse and longitudinal directions, respectively,n is the average coherence coefficient of the target point, and is expressed as the logarithm of the adjacent interference pair of the coherence point. The coherence coefficient of the N interference pairs is expressed as: (gamma) 1 ,γ 2 ，…,γ N )
At the surface objects such as vegetation or water, different scattering mechanisms can cause the phase of radar waves to change greatly, so that the phase loss phenomenon is caused. Multiple scattering sources such as vegetation leaves, branches and the like cause multiple scattering in different directions, so that the phases of radar waves are inconsistent; second, the growth state and structural changes of vegetation also cause the phase of radar waves to change. The main reason for the incoherence of the water body is that the phase difference exists between radar waves from different directions and positions due to factors such as multiple reflections of the surface, and the like, so that the coherence of the water body area is reduced. The average coherence coefficient can quickly identify and reject such incoherent data.
The target point is used for detecting ground subsidence, and the method for detecting ground subsidence comprises the following steps:
step 201: and removing phase shift and atmospheric effect in the interference pattern of the target point, obtaining the interference pattern, and calculating the deformation rate of the target point through the interference pattern. The removal of the phase shift and the atmospheric effect in the target point interferogram is the prior art, and is not described in detail in this application.
Step 202: the ground subsidence is calculated from the vertical component of the deformation rate.
The permanent scatterer candidate points which are less affected by time incoherence and space incoherence are selected by a time sequence amplitude discrete index threshold method and a point target detection method, so that the permanent scatterer density of a single monitoring method is greatly improved. On the basis, the coherence coefficient threshold method is adopted to optimize the identification of the coherence point target, so that the coherence points which are incorrectly identified are effectively removed, and the reliability of the traditional coherence point identification method is improved. And finally, the terrain phases on the target points are separated so as to monitor ground subsidence, and high-quality monitoring of subway operation and maintenance period is realized. The method for selecting multiple information fully considers various characteristics of the coherent points by selecting the points meeting the first threshold, the second threshold and the third threshold simultaneously as the coherent points (PS points).
A coherence coefficient above 0.8 indicates higher coherence and an amplitude dispersion index below 0.15 indicates more stable amplitude information. The spectral autocorrelation coefficient of an artificial structure such as a building is generally 0.6 or more. Thus, in one embodiment, the first threshold T1 is 0.1, the second threshold T2 is 0.8, and the third threshold T3 is 0.9, but is not limited thereto.
The method of the invention makes it possible to identify the point of radiation stabilization in time. The level point monitoring data, as well as other field data, may also provide a reference for identification of the coherence point. These point targets are characterized by strong reflection (high backscattering) and high coherence during the observation period. Once these points are determined as stable PS candidate points, a displacement model is used to remove phase shift and atmospheric effects from the flattened interferogram, resulting in a final deformation rate for each pixel.
The invention also provides a system for realizing the method, as shown in fig. 2, comprising: the system comprises an acquisition module 1 and an identification module 2, wherein the acquisition module 1 is used for acquiring image data of a synthetic aperture radar; the identification module 2 is configured to determine whether the target point of the image data meets the following conditions: the amplitude dispersion index of the time series is smaller than a first threshold, the autocorrelation coefficient is larger than a second threshold, and the coherence coefficient is larger than a third threshold; if yes, the target point is a coherent point.
The system further comprises a subsidence analysis module 3, wherein the subsidence analysis module is used for calculating the deformation rate of the target point after removing the phase shift and the atmospheric effect in the target point interferogram; the ground subsidence is calculated from the vertical component of the deformation rate.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of coherent point identification, comprising the steps of:
obtaining image data of a synthetic aperture radar;
judging whether the target point of the image data meets the following conditions: the amplitude dispersion index of the time series is smaller than a first threshold, the autocorrelation coefficient is larger than a second threshold, and the average coherence coefficient is larger than a third threshold;
if yes, the target point is a coherent point.
2. The method of claim 1, wherein the amplitude dispersion index is expressed as:
D＝σ/μ (1)
wherein D represents an amplitude discrete index of the target point time series, sigma is a standard deviation of the target point amplitude time series, and mu is a mean value of the target point amplitude time series.
3. The method of claim 1, wherein the average coherence coefficient is expressed as:
γ k expressed as the coherence coefficient of the target point under the kth interference pair, M (i, j) and S (i, j) are the master-slave images constituting the kth interference pair, respectively, expressed as performing conjugate multiplication, M and n are the sizes of the sliding window in the transverse and longitudinal directions, respectively,n is the average coherence coefficient of the target point, and is expressed as the logarithm of the adjacent interference pair of the coherence point.
4. A method according to claim 3, wherein the coherence coefficient is used to reject non-permanent scatterer targets that are severely out of correlation.
5. The method of claim 1, wherein the image data comprises a time series after registration processing;
the amplitude dispersion index is used for selecting a permanent scatterer with a change in time sequence smaller than a first threshold value so as to reduce the influence of time incoherence;
the autocorrelation coefficients are used to reduce the effects of spatial incoherence.
6. The method of claim 1, wherein the method of obtaining the autocorrelation coefficients comprises:
preprocessing image data and registering images in sequence;
performing Fourier transform on the image data after image registration to obtain the representation of the image data in a frequency domain;
an amplitude signal time series is extracted from the frequency domain representation and an autocorrelation coefficient of the amplitude signal time series is calculated.
7. The method of claim 6, wherein the autocorrelation coefficients are calculated by:
Y(t，s)＝E(X t -μ t )(X s -μ s ) (5)
wherein ρ (t, s) is expressed as an autocorrelation coefficient of the time series of amplitude signals, t and s respectively represent the moments of the time series, X t Phase denoted as time phase at time t, μ t Expressed as the mean of the time phases at time t, D () represents the taking of the variance, E () represents the mathematical expectation, and Y (t, s) represents the autocovariance function.
8. The method of claim 1, wherein the target point is for detecting ground subsidence, the method of detecting ground subsidence comprising:
after removing phase shift and atmospheric effect in the target point interferogram, calculating the deformation rate of the target point;
the ground subsidence is calculated from the vertical component of the deformation rate.
9. A system for coherence point identification for implementing the method of any of claims 1-8, the system comprising: the acquisition module and the identification module are used for acquiring the data of the data,
the acquisition module is used for acquiring image data of the synthetic aperture radar;
the identification module is used for judging whether the target point of the image data meets the following conditions: the amplitude dispersion index of the time series is smaller than a first threshold, the autocorrelation coefficient is larger than a second threshold, and the coherence coefficient is larger than a third threshold; if yes, the target point is a coherent point.
10. The system of claim 9, further comprising a dip analysis module for calculating a deformation rate of the target point after removing phase shifts and atmospheric effects in the target point interferogram; the ground subsidence is calculated from the vertical component of the deformation rate.
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