CN107218923A - Surrounding enviroment history settles methods of risk assessment along subway based on PS InSAR technologies - Google Patents
Surrounding enviroment history settles methods of risk assessment along subway based on PS InSAR technologies Download PDFInfo
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- G—PHYSICS
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- 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
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
Methods of risk assessment is settled the present invention relates to surrounding enviroment history along a kind of subway based on PS InSAR technologies.Methods described includes：Using N width SAR image as input, all SAR images are registrated in identical grid by the method for image registration；Selection time and the less SAR image of Space Baseline remove the interferometric phase introduced by hypsography to generation interference pattern, and by two rail methods；Selection candidate's PS points (PSC) in the picture, and the error phase that is introduced by Atmosphere changes of information compensation using PSC and the error phase that is inaccurately introduced by orbital data；Point-to-point analysis is carried out to all pixels point in image using the phase information after compensation, PS points are re-recognized, and estimate its deformation data and vertical error information.
Description
Technical field
Methods of risk assessment is settled the present invention relates to surrounding enviroment history along a kind of subway based on PS-InSAR technologies.
Background technology
City track traffic engineering induces house crack, line break, road and the surrounding enviroment accident such as caved in, except with rail
Surrounding enviroment deformation and displacement have outside the Pass caused by road transport development, also have with the deformation of surrounding enviroment itself early stage history and displacement
Larger association.One of major reason of Railway Transit Construction surrounding enviroment Frequent Accidents be to along road, build (structure) and build
Thing early stage history sedimentation and deformation is in confused situation, and its risk is recognized not enough：1) it is existing to road, building construction, bridge etc. to become
Position and follow-up allowable displacement degree more than needed lack understanding；2) unfavorable geological condition for causing ground settlement to deform excessive behind is investigated
It is not enough.Particularly, when larger accumulative deformation has occurred for the preceding periphery buildings or structures of urban rail transit construction, road, pipeline with displacement
(cause conjugate more than needed degree smaller), extreme difficulties and risk will be come to urban rail transit construction surrounding enviroment protection band.
Conventional deformation monitoring technology includes determining using conventional measuring equipments such as theodolite, spirit level, rangefinder, total powerstations
The deformation values of point, its advantage is：(1) it can provide deformable body overall deformation state；(2) it is applied to different monitoring accuracies
It is required that, various forms of deformable bodys and different monitoring of environmental；(3) absolute deformation information can be provided.But field process amount is big,
Layout is influenceed by orographic condition, is difficult to realize automatic monitoring.Special measurement means include strain measurement, alignment measurement and inclination
Measurement, it has the advantages that measurement process is simple, can deform, easily realize automatic monitoring inside Deformation Monitoring body, but logical
Local and relative deformation information can only be often provided.Rhythm more and more fast today, various regions government are built in track transport development
With the construction unit of track traffic have increasing need for accurately, timely surface subsidence basic data carry out railway traffic engineering rule
Draw, meanwhile, also have increasing need for modern high technology provide more comprehensively, in time, the monitoring means of science.
Repeat track SAR interferometry (InSAR) be one measurement Ground Deformation (collapse, come down, earthquake, volcanic activity
Deng caused by) effective technology.This PS-InSAR technology, by SAR images, can detect the precision of Ground Deformation
Reach grade.The general principle of the technology is：The phase difference for the image that different times, different angles are obtained and landform, acquisition
The Ground Deformation and Atmosphere changes of period has certain association.Measured by some points, these points are scattering properties ratios
Relatively stable point, referred to as PS points, are typically distributed on the rare area of vegetation, correspond to the facilities such as building, highway, water conservancy.
Distribution is moved towards because track traffic is linear in urban environment, during its sinking deformation monitoring, is had
Feature is：1) monitoring distance is long, more than ten kilometers at least, at most tens kilometers of a line；2) monitoring project is more, including foundation ditch main body
Structure, along the line ground, surrounding buildings or structures, pipeline, bridge etc..Therefore, deformation monitoring is carried out using conventional Levelling
Asked when needing to expend substantial amounts of, human and material resources and financial resources, and be difficult the deformation effect scope along accurate determine.Meanwhile, by
There is the problem of measuring point is distributed sparse, length duty cycle, labor intensity is big in traditional Levelling observation and GPS observations, it is difficult
With in good time, objectively respond the regional surface subsidence variation tendency expanded day by day.
The content of the invention
In order to solve the above technical problems, the purpose of the present invention is to find a kind of precision for improving subway sedimentation for oil depot
Methods of risk assessment is settled with surrounding enviroment history along the subway based on PS-InSAR technologies of accuracy.
Surrounding enviroment history sedimentation methods of risk assessment along subway of the invention based on PS-InSAR technologies, including：
Using N width SAR image as input, all SAR images are registrated in identical grid by the method for image registration；
The SAR data of all acquisitions is combined into several set, principle is：SAR image baseline distance in set is small, collection
Baseline distance between conjunction is big, and removes the interferometric phase introduced by hypsography by two rail methods；
Selection candidate's PS points (PSC), and utilize the PSC error phase that is introduced by Atmosphere changes of information compensation in the picture
With the error phase inaccurately introduced by orbital data；
Point-to-point analysis is carried out to all pixels point in image using the phase information after compensation, PS points are re-recognized, and estimate
Count its deformation data and vertical error information.
Further, SAR image registration is specifically included：During registration, the width SAR in selection N width SAR images
Image on the basis of image, other N-1 width SAR images are all registrated in the grid of benchmark SAR image；The selection of benchmark image is needed
Consider Space Baseline and time reference line two indices, optimal benchmark image be to other SAR image Space Baselines and
Minimum that width SAR images of the weighted average of time reference line；Using the method for three-level registration in processing procedure：(1) it is based on
The registration of satellite orbit data；(2) registration based on Pixel-level；(3) registration based on sub-pixel.
Further, two rail methods processing is specifically included：First with the satellite orbit data and outside DEM of major-minor SAR image
Information, calculates the interferometric phase of each pixels of DEM, and each pixel is projected in the coordinate system of SAR images；This
When, DEM pixel is non-uniformly distributed in the grid of SAR image；Then, the method pair of Delaunay trigonometric interpolations is utilized
The uniform grid of SAR image carries out resampling, obtains the interferometric phase image simulated by terrain information；Finally, then really doing
Relate to the interferometric phase for subtracting and being simulated by outside DEM in phase.
Further, specifically included with reference to the interferometric phase that PS points (PSC) are selected and extracted at PSC：PSC selection is adopted
With the system of selection based on amplitude statistics characteristic, the method selects PS points using the amplitude deviation information of target point, amplitude from
The calculation formula of difference is as follows：
In formula, σARepresent the standard deviation of target point amplitude in the N width SAR images of input, mARepresent target point in input
The average of amplitude in N width SAR images；
First set amplitude deviation thresholding DThreshold, those are then met into condition DA＜ DThresholdPixel elect as
PS points, amplitude deviation thresholding DThresholdIt is set to 0.3；The SAR image of input is more than 25~30 width as far as possible；
Select after PSC, extract the interferometric phase at PSC, now, the interferometric phase of extraction is after the processing of two rail methods
Phase.
Further, phase unwrapping is specifically included during three-dimensional space：The phase data that radar is obtained be wound around it is interval [- π,
Data in π)；Therefore, in order to recover target point true phase, it is necessary to phase data carry out unwrapping processing.Target point
True phase and the mathematic(al) representation of winding phase relation are as shown in formula：
N is integer
The process of unknown Integer n is namely estimated in phase unwrapping processing；
, it is necessary to carry out phase unwrapping to space-time three-dimensional in PSInSAR processing procedure, in the image area of space two-dimensional,
Delaunay triangulation network lattice are first set up according to PSC position, then recycle MCF algorithms to obtain the disentanglement fruit of space two-dimensional.
Further, estimate and compensate air and orbit error phase is specifically included：Atmospheric phase and orbit error phase
It is gradual with space, is modeled as the function of first order using space two-dimensional coordinate as independent variable or is established as second order or high-order letter
Number)
In formula, parameter to be estimated has three A, B and a C, and ε and η represent respectively PSC distances corresponding in SAR image and
Orientation two-dimensional coordinate.
Further, PS points re-recognize and deformation inverting and vertical error estimation specifically include：Whether one point of identification
It is that PS points are to see whether this point matches with known deformation and vertical error model compared with, the method that judgment models are matched can be with
According to the time correlation coefficient of this point, its calculation formula is as shown in formula：
In formula,It is compensation air and orbit error to represent interferometric phases of the target point P in the i-th width interference image
After phase, mi(P) phase estimated by target point P distorted movement model and vertical error model is represented.Finally by setting
Coefficient correlation thresholding is put, the pixel that will be greater than thresholding elects final PS points as, and estimates the deformation quantity and elevation of this PS point
Error.
Compared with prior art, surrounding enviroment history sedimentation risk is commented along the subway of the invention based on PS-InSAR technologies
The method of estimating has advantages below：
Radar satellite interferometry (PS-InSAR) technology has non-cpntact measurement, need not lay monitoring control network, milli
Meter accuracy, efficiency high, cost are low, wide coverage, be protected from weather influences, the features such as spatial resolution is high, can overcome above-mentioned
Conventional fine leveling observation and the defect of GPS observations.
The present invention obtains the sedimentation and deformation historical archive number of surrounding enviroment before subway engineering construction using PS-InSAR technologies
According to, high-precision sedimentation and deformation achievement is obtained by Inversion Calculation, according to this to the earth's surface along subway in band buffering area, building,
The facilities such as bridge carry out sedimentation analysis of trend, to supplement reconnoitre, pipeline and house investigation, design, construction in terms of need
The section that emphasis considers is pointed out, and construction unit can be assisted to carry out subway risk prevention work comprehensively.
The present invention by being distributed in along earth's surface diverse location SAR interferometry monitoring point, resolving analyze each monitoring
The three-dimensional coordinate of point, and sequence situation is settled according to history, pass through each monitoring point of target facility in Data analysis region
Variable quantity, variation tendency are settled, and combines other data stability and sedimentation situation of entirety to along and is analyzed, solves to pass
The problem of system monitoring technology can not monitor subway surrounding enviroment history sedimentation risk, improves the precision and accuracy of subway sedimentation.
Described above is only the general introduction of technical solution of the present invention, in order to better understand the technological means of the present invention,
And can be practiced according to the content of specification, below with presently preferred embodiments of the present invention and coordinate accompanying drawing describe in detail as after.
Brief description of the drawings
Fig. 1 is the Processing Algorithm basic flow sheet of PS-InSAR distortion measurements；
Fig. 2 is that interference pattern generation schematic diagram compares；(a) traditional PSInSAR interference patterns generation schematic diagram；(b) it is new
PSInSAR interference patterns generate schematic diagram；
Fig. 3 is the space two-dimensional Delaunay triangulation network lattice of Berlin, Germany subregion PS points.
Embodiment
With reference to the accompanying drawings and examples, the embodiment to the present invention is described in further detail.Implement below
Example is used to illustrate the present invention, but is not limited to the scope of the present invention.
As shown in figure 1, surrounding enviroment history sedimentation risk is commented along a kind of subway based on PS-InSAR technologies of the present invention
Estimate the most preferred embodiment of method, including：
Using N width SAR image as input, all SAR images are registrated to identical grid by the method for first passing through image registration
It is interior.Then, selection time and the less SAR image of Space Baseline are removed by landform to generation interference pattern, and by two rail methods
Lie prostrate the interferometric phase introduced.Meanwhile, candidate's PS points (PSC) are selected in the picture, and utilize PSC information compensation by Atmosphere changes
The error phase of introducing and the error phase inaccurately introduced by orbital data.Finally, using the phase information after compensation to figure
All pixels point carries out point-to-point analysis as in, re-recognizes PS points, and estimate its deformation data and vertical error information.
SAR image registration
Due to Existential Space baseline between different SAR images, the satellite position corresponding to it has differences, and causes same mesh
Position of the punctuate in different SAR images is different, and this deviation is likely to be breached the degree of hundreds of pixels.And InSAR deformation
The input of e measurement technology is phase data of the same target point between different SAR images.Therefore, in the most elementary of data processing
Section is, it is necessary to which the N width SAR image to input carries out registration process.
SAR image registration
Due to Existential Space baseline between different SAR images, the satellite position corresponding to it has differences, and causes same mesh
Position of the punctuate in different SAR images is different, and this deviation is likely to be breached the degree of hundreds of pixels.And InSAR deformation
The input of e measurement technology is phase data of the same target point between different SAR images.Therefore, in the most elementary of data processing
Section is, it is necessary to which the N width SAR image to input carries out registration process.
In the picture, longitudinal axis representation space baseline, transverse axis represents time reference line, and each point represents a width SAR images, often
Bar line represents the interference pattern generated with two width SAR images.
During interference pattern is generated, if the Space Baseline or time reference line of two width SAR images are excessive, generation it is dry
Relating to phase has very maximum probability to have larger error.Generally, when the time range of deformation monitoring is larger, if still adopted
Interfere drawing generating method with traditional PS-InSAR, inevitably two width SAR image Space Baselines or time reference line
Excessive situation, and then final deformation inversion accuracy will certainly be reduced.
In order to solve this problem, such as shown in (b), in new PS-InSAR interference pattern generating process, no longer only adopt
With certain width SAR image as master image, but selection Space Baseline and the less SAR images of time reference line are to generation interference pattern,
And the time reference line and Space Baseline plane in (b) build grid.Due to generating more interference patterns, interference data meeting
There is redundancy, multi-frame interferometry figure constitutes " closed loop " in the plane of time reference line and Space Baseline, by dry in " closed loop "
Relate to data to be handled, phase error can be calibrated, phase accuracy is finally improved.
The processing of two rail methods
In the actual process of two rail methods, believe first with the satellite orbit data and outside DEM of major-minor SAR image
Breath, calculates the interferometric phase of each pixels of DEM, and each pixel is projected in the coordinate system of SAR image.Now,
DEM pixel is non-uniformly distributed in the grid of SAR image.Then, using the method for Delaunay trigonometric interpolations to SAR
The uniform grid of image carries out resampling, obtains the interferometric phase image simulated by terrain information.Finally, then in really interference phase
The interferometric phase simulated by outside DEM is subtracted in position.
The interferometric phase at PSC is selected and extracted with reference to PS points (PSC)
PSC selection mainly includes two class methods：(1) system of selection based on amplitude statistics characteristic；(2) it is based on phase relation
Several systems of selection.In general, the system of selection based on amplitude statistics characteristic is most widely used.The method mainly utilizes mesh
The amplitude deviation information of punctuate selects PS points.The calculation formula of amplitude deviation is as follows：
In formula, σARepresent the standard deviation of target point amplitude in the N width SAR images of input, mARepresent target point in input
The average of amplitude in N width SAR images.In actual process, amplitude deviation thresholding D is first setThreshold, then by those
Meet condition DA＜ DThresholdPixel elect PS points, amplitude deviation thresholding D asThresholdIt may be configured as 0.3.Meanwhile, in order to
Ensure the quality of PSC selections, the SAR image of input is more than 25~30 width as far as possible, otherwise, it is necessary to using other information, using more
Plus complicated method selects PSC.
Select after PSC, the interferometric phase at PSC can be extracted, now, the interferometric phase of extraction is to be handled by two rail methods
Phase afterwards.
Phase unwrapping during three-dimensional space
During InSAR is measured, the phase data that radar is obtained be wound around it is interval [- π, π) in data.Cause
This, in order to recover the true phase of target point, it is necessary to carry out unwrapping processing to phase data.Target point true phase and winding
The mathematic(al) representation of phase relation is as shown in formula：
N is integer
Phase unwrapping processing is it also will be understood that be the process for estimating unknown Integer n.
The step of phase unwrapping is most critical during InSAR data is handled, its error can spread in the range of room and time,
Eventually influence whole deformation inversion result.Phase unwrapping algorithm is generally divided into three classes, L0Norm method, L1Norm method and L2Norm
Method.
L0Most basic in norm method is Branch cut, and main thought is to find optimal solution to twine path.Although this kind of method
It is higher that solution twines efficiency, but be likely to occur solution in some regions and twine unsuccessfully.
L1Norm method is grid stream method, and the method for representative is statistics minimum cost flow method.Its basic ideas are to find view picture
The optimal solution of image, because setting up for grid is complex, computational efficiency is not high, but the precision that its solution is twined is fine.
L2Norm method and L1Norm method is also to find optimal solution, but its computational efficiency is higher than grid stream method, many times
Also play the role of very big.
In general, based on L1The statistics minimum cost flow algorithm (Minimum Cost Flow, MCF) of norm method is property
Can comparative superiority algorithm, it is twined in solution very big guarantee in precision, and twines without solution the region of failure.
, it is necessary to carry out phase unwrapping to space-time three-dimensional in PSInSAR processing procedure, in the image area of space two-dimensional,
Delaunay triangulation network lattice are first set up according to PSC position, then recycle MCF algorithms to obtain the disentanglement fruit of space two-dimensional.
Wherein, bottom panel show the space two-dimensional Delaunay triangulation network lattice of Berlin, Germany subregion PS points.In time one-dimensional domain,
As shown in Fig. 2 (b), due to being generated in interference pattern during establish grid (" closed loop ") in time dimension, MCF can also be utilized
Algorithm realizes phase unwrapping.
Estimate and compensate air and orbit error phase
After phase data solution is twined, just atmospheric phase and orbit error phase can be estimated and compensated.In general, big
Gas phase and orbit error phase are gradual with space, and therefore, they can be modeled as using space two-dimensional coordinate as independent variable
Function of first order (second order or higher-order function if desired, can also be established as)：
In formula, parameter to be estimated has three A, B and a C, and ε and η represent respectively PSC distances corresponding in SAR image and
Orientation two-dimensional coordinate.Therefore, the problem of orbit error phase and atmospheric phase are estimated can be converted into estimating for three parameters to be estimated
Meter problem.According to parameter estimation theories, least square method can be used, orbit error phase and atmospheric phase is estimated, and
Most it is compensated at last.
PS points are re-recognized and deformation inverting and vertical error estimation
After air and orbit error phase compensation, can just point-to-point analysis be carried out to each pixel of SAR image, confirm it
Whether it is PS points, and estimate its deformation quantity and vertical error.In general, whether recognize point is that the keys of PS points is to see this
Whether individual point matches compared with known deformation and vertical error model.The method of judgment models matching can be according to this point
Time correlation coefficient, its calculation formula is as shown in formula：
In formula,Represent interferometric phase (compensation air and orbit error phases of the target point P in the i-th width interference image
Behind position), mi(P) phase estimated by target point P distorted movement model and vertical error model is represented.Finally by setting
Coefficient correlation thresholding, the pixel that will be greater than thresholding elects final PS points as, and estimates that the deformation quantity and elevation of this PS point are missed
Difference.
The present invention：Without ground survey station：Because radar satellite interferometry monitoring is without ground survey station, thus monitoring can be made
The design of space-time unique is more free, conveniently.Meanwhile, the limitation of ground control point can be avoided, especially many middle transitions
Point (when carrying out deformation monitoring using conventional Geodetic surveying method, many mid-transition points will be often set up for transmission coordinate), and
Mark need not be built, so as to save substantial amounts of manpower and materials, monitoring efficiency is greatly improved.
Actively launch microwave：Radar satellite interferometry, according to monitoring task arrangement, formulates satellite number by ground control station
According to plan is obtained, satellite is according to programming instruction, and the earth that detours actively is launched microwave by formulating during region, earthward and is received back to
Ripple completes measurement.
Density of observation point is high：Under the conditions of routine monitoring, the monitoring point quantity in 1 square kilometre is generally 1-100, from
Isolated monitoring point is dissipated, the situation of approx reflecting regional deformation is only capable of.SAR interferometry monitoring points averag density is reachable
20000/square kilometre, the observation station of high density distribution, objective number is provided for the deformation analysis of different target in observation area
According to support, and then realize continuous deformation signature analysis in region.
Round-the-clock observation：The limitation of radar satellite interferometry not climate condition, in night or wind and snow misty rain condition
Under can still provide for effectively observation.This point is highly beneficial for Geological Hazards Monitorings such as the avalanche in flood season, landslide, mud-rock flows
's.
Full-automation observation：Because the data collection task of radar satellite interferometry is carried out automatically, while satellite
Contacted between receiving station, receiving station and user by data link, therefore user can be with more convenient radar satellite
Interferometry monitoring system builds up the monitoring system of full-automation.This system can not only ensure long-term continuous operation, Er Qieke
Deformation monitoring cost is greatly lowered, the reliability of monitoring materials is improved.
Historical archive data：National big and medium-sized cities historical archive radar data of 5 years so far from 2011, is multiple timings point
Analyse history deformation analysis and support is provided.
Support GIS visual analyzing interfaces：Radar satellite interferometry monitoring result possesses support GIS-Geographic Information System
(GIS) platform interface, can help that user will can not embody pattern between data in electrical form and database and change becomes
Gesture is showed graphically clear and intuitively, and carries out spatial visualization analysis, realizes data visualization management, geography
Information analysis and with related service job applications organic integration, so as to meet the requirement of user's diversification.
Mm class precisions can be obtained：Mm grades of precision can meet the required precision of general slumped mass deformation monitoring.Need
Should increase during higher monitoring accuracy observation time and when hop count just because of GPS positioning technology has above-mentioned advantage, thus in cunning
It is widely used in the monitoring of the geological disasters such as slope, avalanche, mud-rock flow, as a kind of new effective monitoring means.
Described above is only the preferred embodiment of the present invention, is not intended to limit the invention, it is noted that for this skill
For the those of ordinary skill in art field, without departing from the technical principles of the invention, can also make it is some improvement and
Modification, these improvement and modification also should be regarded as protection scope of the present invention.
Claims (7)
1. surrounding enviroment history settles methods of risk assessment along a kind of subway based on PS-InSAR technologies, it is characterised in that
Including：
Using N width SAR image as input, all SAR images are registrated in identical grid by the method for image registration；
The SAR data of all acquisitions is combined into several set, principle is：SAR image baseline distance in set is small, between set
Baseline distance it is big, and remove the interferometric phase introduced by hypsography by two rail methods；
Selection candidate's PS points (PSC) in the picture, and the error phase that is introduced by Atmosphere changes of information compensation using PSC and by
The error phase that orbital data is inaccurately introduced；
Point-to-point analysis is carried out to all pixels point in image using the phase information after compensation, PS points are re-recognized, and estimate it
Deformation data and vertical error information.
2. surrounding enviroment history settles risk assessment side along the subway according to claim 1 based on PS-InSAR technologies
Method, it is characterised in that SAR image registration is specifically included：During registration, the width SAR figures in selection N width SAR images
The image as on the basis of, other N-1 width SAR images are all registrated in the grid of benchmark SAR image；The selection of benchmark image needs
Consider Space Baseline and time reference line two indices, optimal benchmark image be to other SAR image Space Baselines and when
Between baseline minimum that width SAR image of weighted average；Using the method for three-level registration in processing procedure：(1) it is based on defending
The registration of star orbital track data；(2) registration based on Pixel-level；(3) registration based on sub-pixel.
3. surrounding enviroment history settles risk assessment side along the subway according to claim 1 based on PS-InSAR technologies
Method, it is characterised in that the processing of two rail methods is specifically included：Believe first with the satellite orbit data and outside DEM of major-minor SAR image
Breath, calculates the interferometric phase of each pixels of DEM, and each pixel is projected in the coordinate system of SAR image；Now,
DEM pixel is non-uniformly distributed in the grid of SAR image；Then, using the method for Delaunay trigonometric interpolations to SAR
The uniform grid of image carries out resampling, obtains the interferometric phase image simulated by terrain information；Finally, then in really interference phase
The interferometric phase simulated by outside DEM is subtracted in position.
4. surrounding enviroment history settles risk assessment side along the subway according to claim 1 based on PS-InSAR technologies
Method, it is characterised in that specifically included with reference to PS points (PSC) selection and the interferometric phase extracted at PSC：PSC selection uses base
In the system of selection of amplitude statistics characteristic, the method selects PS points using the amplitude deviation information of target point, amplitude deviation
Calculation formula is as follows：
<mrow>
<msub>
<mi>D</mi>
<mi>A</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>&sigma;</mi>
<mi>A</mi>
</msub>
<msub>
<mi>m</mi>
<mi>A</mi>
</msub>
</mfrac>
</mrow>
In formula, σARepresent the standard deviation of target point amplitude in the N width SAR images of input, mARepresent N width of the target point in input
The average of amplitude in SAR image；
First set amplitude deviation thresholding DThreshold, those are then met into condition DA＜ DThresholdPixel elect PS points, width as
Spend deviation thresholding DThresholdIt is set to 0.3；The SAR image of input is more than 25-30 width as far as possible；
Select after PSC, extract the interferometric phase at PSC, now, the interferometric phase of extraction is the phase after the processing of two rail methods
Position.
5. surrounding enviroment history settles risk assessment side along the subway according to claim 1 based on PS-InSAR technologies
Method, it is characterised in that phase unwrapping is specifically included during three-dimensional space：The phase data that radar is obtained be wound around it is interval [- π, π) in
Data；Therefore, in order to recover target point true phase, it is necessary to phase data carry out unwrapping processing.Target point is true
The mathematic(al) representation of phase and winding phase relation is as shown in formula：
N is integer
The process of unknown Integer n is namely estimated in phase unwrapping processing；
, it is necessary to phase unwrapping be carried out to space-time three-dimensional, in the image area of space two-dimensional, first root in PSInSAR processing procedure
Delaunay triangulation network lattice are set up according to PSC position, then recycle MCF algorithms to obtain the disentanglement fruit of space two-dimensional.
6. surrounding enviroment history settles risk assessment side along the subway according to claim 1 based on PS-InSAR technologies
Method, it is characterised in that estimate and compensate air and orbit error phase is specifically included：Atmospheric phase and orbit error phase be with
Space is gradual, is modeled as the function of first order using space two-dimensional coordinate as independent variable or is established as second order or higher-order function)
In formula, parameter to be estimated has three A, B and C, and ε and η represent PSC distance and bearings corresponding in SAR image respectively
Two-dimensional coordinate.
7. surrounding enviroment history settles risk assessment side along the subway according to claim 1 based on PS-InSAR technologies
Method, it is characterised in that PS points re-recognize and deformation inverting and vertical error estimation specifically include：Recognize whether a point is PS
Point is to see whether this point matches compared with known deformation and vertical error model, and the method for judgment models matching can foundation
The time correlation coefficient of this point, its calculation formula is as shown in formula：
In formula,It is compensation air and orbit error phase to represent interferometric phases of the target point P in the i-th width interference image
Afterwards, mi(P) phase estimated by target point P distorted movement model and vertical error model is represented.Finally by setting phase
Relation number thresholding, the pixel that will be greater than thresholding elects final PS points as, and estimates the deformation quantity and vertical error of this PS point.
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107621636A (en) * | 2017-11-13 | 2018-01-23 | 河海大学 | A kind of Large-scale Railway Method of Bridge Health Monitoring based on PSI |
| CN108983233A (en) * | 2018-06-13 | 2018-12-11 | 四川大学 | The PS point of GB-InSAR Data processing combines choosing method |
| CN109541593A (en) * | 2018-10-30 | 2019-03-29 | 北京航空航天大学 | A kind of improved minimum cost flow InSAR phase unwrapping method |
| CN109584515A (en) * | 2018-12-21 | 2019-04-05 | 武汉万集信息技术有限公司 | Method for early warning, device and the readable storage medium storing program for executing of massif disaster |
| CN109991601A (en) * | 2018-11-29 | 2019-07-09 | 北京东方至远科技股份有限公司 | A kind of house methods of risk assessment based on PS-InSAR technology |
| CN110174044A (en) * | 2019-04-16 | 2019-08-27 | 中国矿业大学 | A method of the bridge length travel deformation monitoring based on PSI technology |
| CN110244298A (en) * | 2019-07-26 | 2019-09-17 | 北京东方至远科技股份有限公司 | A kind of InSAR data lift rail joint landslide analysis method |
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| CN111380504A (en) * | 2019-12-02 | 2020-07-07 | 中国铁路沈阳局集团有限公司科学技术研究所 | Boundary gridding management method for building equipment |
| CN111798135A (en) * | 2020-07-06 | 2020-10-20 | 天津城建大学 | High-speed rail settlement hazard assessment method based on multi-source data integration |
| CN112130134A (en) * | 2020-08-17 | 2020-12-25 | 河北汉光重工有限责任公司 | Real-time baseline correction method based on time compensation |
| CN112731389A (en) * | 2020-12-01 | 2021-04-30 | 西安电子科技大学 | Channel azimuth baseline error estimation method based on spatial characteristics of multi-channel complex image |
| CN114578357A (en) * | 2022-04-29 | 2022-06-03 | 北京东方至远科技股份有限公司 | High-precision deformation measurement method and device for infrastructure |
| CN117091565A (en) * | 2023-08-23 | 2023-11-21 | 中国公路工程咨询集团有限公司 | Bridge settlement detection method, equipment and system based on satellite positioning |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101339245A (en) * | 2008-08-08 | 2009-01-07 | 西安电子科技大学 | Multi- baseline interference synthetic aperture radar interference phase unwrapping method |
| CN104360332A (en) * | 2014-11-11 | 2015-02-18 | 河海大学 | Atmospheric phase screen extraction method based on ground-based SAR (synthetic aperture radar) interference |
-
2017
- 2017-05-23 CN CN201710366118.XA patent/CN107218923A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101339245A (en) * | 2008-08-08 | 2009-01-07 | 西安电子科技大学 | Multi- baseline interference synthetic aperture radar interference phase unwrapping method |
| CN104360332A (en) * | 2014-11-11 | 2015-02-18 | 河海大学 | Atmospheric phase screen extraction method based on ground-based SAR (synthetic aperture radar) interference |
Non-Patent Citations (7)
| Title |
|---|
| 刘媛媛 等: ""PS-InSAR技术用于太原市地面沉降形变分析"", 《大地测量与地球动力学》 * |
| 周朝栋 等: ""北京平原区地面沉降PS-InSAR监测"", 《遥感信息》 * |
| 张兆旭: ""基于PS-InSAR技术的高速铁路沉降监测研究"", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
| 张静: ""InSAR时序监测及应用中的质量控制研究"", 《中国博士学位论文全文数据库基础科学辑》 * |
| 朱叶飞 等: ""基于PS-InSAR等1995-2000年苏州地面沉降监测"", 《地球科学进展》 * |
| 杨阳: ""小基线子集长时间序列差分干涉技术研究"", 《中国博士学位论文全文数据库 信息科技辑》 * |
| 秦晓琼 等: ""高分辨率PS-InSAR在轨道交通形变特征探测中的应用"", 《测绘学报》 * |
Cited By (23)
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