CN113008202B - Ground settlement monitoring method integrating different synthetic aperture radar interferometry - Google Patents
Ground settlement monitoring method integrating different synthetic aperture radar interferometry Download PDFInfo
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- 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
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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
Abstract
The invention relates to a ground settlement monitoring method fusing different synthetic aperture radar interferometry. The method aims to solve the problem of accuracy errors caused by the density difference of permanent scattering points (PS points) or slow decorrelation filtering phase pixels in different areas in a single interferometric measurement method, and realize that different interferometric measurement methods are automatically selected in different areas in the same measurement area according to the density of the PS points. The method comprises the following steps: selecting N SAR image data, and independently resolving by adopting PS-InSAR and SBAS-InSAR methods respectively; obtaining PS points in a PS-InSAR resolving process, and screening out the PS points with high coherence values; taking the distribution barycentric coordinates of the PS points as an initial central point, carrying out DBSCAN cluster analysis on the spatial distribution of the PS points, and finally finding out a boundary between a PS point distribution dense area and a PS point distribution sparse area; vectorizing the SBAS-InSAR result; and changing the PS-InSAR monitoring result in the sparse area into vector monitoring data of the SBAS-InSAR. The invention effectively improves the ground settlement monitoring precision of the complex ground object reflection area.
Description
Technical Field
The invention relates to a ground settlement monitoring method for a complex ground object reflection area, which integrates two radar satellite interferometry methods of PS-InSAR (permanent scatterer synthetic aperture radar interferometry) and SBAS-InSAR (short-baseline collective synthetic aperture radar interferometry), and belongs to the field of ground settlement monitoring.
Background
At present, the monitoring method of ground settlement mainly comprises precision leveling measurement, bedrock mark-layered mark measurement, GPS (global positioning system) measurement and InSAR (synthetic aperture radar differential interferometry).
The ground settlement information obtained by the method has high precision and reliability, but the repeated measurement period is long, the manpower and material resources consumption is huge, the requirement on real-time dynamic monitoring of the ground settlement cannot be met, and the obtained monitoring information is discontinuous and the like, so that the wide application of the method is limited. However, in terms of the existing ground settlement monitoring technology, the precision leveling measurement still has incomparable advantages of high precision and is often used for verifying the precision of the novel ground settlement monitoring technology.
The bedrock mark-layered mark monitoring method can acquire the vertical layered ground settlement deformation information with high precision, and the precision reaches 0.01-0.1 mm. However, the method is complex to operate, high in construction process, high in cost and the like, so that the method is limited to be widely applied to monitoring the regional ground settlement and is commonly used for researching the ground settlement mechanism at present.
The GPS measurement technology plays an important role in ground settlement monitoring along with continuous improvement of instruments and unwrapping algorithms. The GPS measurement has the advantages of short period, high positioning accuracy, rapid net distribution, all-day and the like, has higher accuracy in the aspect of horizontal deformation monitoring, but is still a defect that the vertical deformation monitoring accuracy is difficult to avoid because of the limitation of atmospheric delay, net distribution form, measurement method and unwrapping algorithm in the aspect of vertical deformation monitoring. Moreover, the deformation information of the ground monitoring points distributed in a point shape is acquired by the GPS measurement, and the elevation value of the monitoring points is difficult to acquire in the areas with poor signals or blocked by obstacles, so that the use of the method is limited.
The geosynchronous orbit satellite synthetic aperture radar has the characteristics of all-time, all-weather, wide coverage area, high observation precision and the like, and the interference monitoring of the synthetic aperture radar is a space-to-ground observation technology with potential of machines and tools and is widely applied to the aspects of ground settlement monitoring, glacier displacement, earthquake ground surface deformation detection and the like. Particularly, due to the occurrence of time sequence radar interference, the monitoring capability of the InSAR technology on the surface micro-deformation is greatly improved. Among them, the synthetic aperture radar interference technology and the short-baseline radar interference technology with permanent scatterers are most mature and widely applied.
Compared with the PS-InSAR and the SBAS-InSAR, the analysis object is a permanent scatterer, the phase and amplitude of the points can keep stability in a long-time sequence, and the reliability and the accuracy are higher in the aspect of coherence. The PS-InSAR vector monitoring points which keep strong scattering property and stability are used as research objects to solve the problems of temporal decoherence, null decoherence and the like. High-density vector monitoring points can be obtained in the urban area, and the monitoring precision and the reliability are high. The PS-InSAR technique, however, also has its own drawbacks and limitations. In terms of original data, the PS-InSAR technology needs to research images with not less than 20 scenes in a region; the PS-InSAR technology is suitable for areas with more ground surface hard ground coverage, such as cities, in the aspects of the type and the range size of the research area. In these areas where vegetation covers are more distributed, the technology needs more relevant experimental research to find out a reasonable method. The scope of the PS process cannot be too large because of the atmospheric model. In the aspect of deformation speed analysis, the method is limited by the existing algorithm at present, and is suitable for researching objects with smaller change rate.
The SBAS-InSAR utilizes a small base line set technology to combine image pairs into a plurality of small differential interference sets, and utilizes a least square method to obtain a ground surface deformation time sequence of each small set, so that the required data volume is small (at least more than 8-period images are required), the time sampling rate is high, the influence of space-time incoherent can be effectively weakened, and the obtained monitoring result is more continuous in time and space. Although the monitoring precision is lower than that of the PS-InSAR, more monitoring point information can be obtained. In suburbs outside cities, a large number of nonlinear ground subsidences such as seasonal ground subsidences exist, decoherence of monitoring points can be caused when the time base line is too large, and the phenomenon can be effectively overcome by adopting a small base line technology. Secondly, when SBAS-InSAR performs phase unwrapping, high resolution data unwrapping is difficult for urban areas with dense buildings. High-rise buildings are usually blocked and the phase "decreases" from near to far, for example, the phase of an overhead section is different from the phase of the street in the flat ground. If the elevation difference between the overhead section and the ground is large, the winding-off process is confused with strong phase jump, the phase jump is removed because the phase jump is not practical, the phases of the overhead section and the adjacent sections are lost, and the PS-InSAR technology is suitable for the areas, because the PS-InSAR does not relate to the winding-off problem of the whole area, and the practical situation can be better reflected.
In a complex ground object reflection area, the image data volume of each time period may change greatly, in order to fully extract vector monitoring points with high precision and reliability in the image data, a PS-InSAR method is preferably adopted for high coherence of PS points in an urban area, and an SBAS-InSAR method is preferably adopted for low coherence of PS points outside the city. The single calculation method is adopted to have great monitoring area local errors caused by the complex ground object reflecting area, so the ground settlement monitoring method of the complex ground object reflecting area integrating the PS-InSAR and the SBAS-InSAR has very important engineering significance.
Disclosure of Invention
The technical problem is as follows: the invention aims to provide a ground settlement monitoring method fusing different synthetic aperture radar interferometry, aiming at the current situation that a high-speed rail line passes through urban areas and suburban areas, and fusing PS-InSAR and SBAS-InSAR technologies to solve the problem of accuracy errors caused by density differences of vector monitoring points when a single resolving method is used for monitoring the ground settlement of the high-speed rail line.
The technical scheme is as follows: the invention discloses a ground settlement monitoring method fusing different synthetic aperture radar interferometry, which comprises the following steps:
step 1, obtaining N synthetic aperture radar SAR image data to form an SAR image data set, wherein N is more than or equal to 20; the image data is data obtained by imaging the same earth surface observation scene by the SAR and completing image registration;
step 2, carrying out permanent scatterer synthetic aperture radar interferometry (PS-InSAR) processing on the SAR image data set obtained in the step 1, resolving a ground settlement monitoring result, and obtaining PS points which meet the condition that a coherence threshold C is a screening condition;
step 3, sorting longitude and latitude coordinates (x, y) of all PS points and corresponding coherence values c (reflecting the reliability or confidence of the calculated result of the PS points) of each PS point, and obtaining barycentric coordinates G (a, b) of the PS point spatial distribution by adopting a barycentric method;
step 4, taking the gravity center coordinates G (a, b) as an initial clustering central point of the DBSCAN clustering algorithm, carrying out clustering analysis on longitude and latitude coordinates (x, y) of all PS points, and dividing the spatial distribution of all PS points into a dense area and a sparse area;
step 5, solving the minimum geometric boundary of the discrete points in the PS point dense region, establishing an irregular triangular grid, and correcting the minimum geometric boundary by taking the maximum side length D of the surface of the triangular grid as a constraint condition, wherein the corrected minimum geometric boundary is the boundary L between the PS point distribution dense region and the PS point distribution sparse region;
step 6, carrying out short-baseline set integration aperture radar interferometry (SBAS-InSAR) processing on the SAR image data set obtained in the step 1, resolving a ground settlement monitoring result, and converting grid data of the ground settlement monitoring result obtained by the SBAS-InSAR method into vector data;
and 7, updating the PS-InSAR monitoring result data in the sparse area into vector data of the SBAS-InSAR method of the corresponding area according to the boundary L, and obtaining the final ground settlement monitoring result of the whole research area.
And the coherence threshold C in the step 2 is more than or equal to 0.9.
The gravity center method in the step 3 is as follows:
wherein, a and b are the abscissa and ordinate of the barycentric coordinate; x and y are the abscissa and the ordinate of each PS point; c is the corresponding coherence value for each PS point.
And in the step 5, the maximum side length D is 4000-5000 m according to the discrete degree of the detection points.
Has the beneficial effects that: compared with the prior art, the invention has the beneficial effects that:
(1) The traditional single InSAR technology is usually used for mutual comparison and verification, the quality and the quantity of vector monitoring points can not be considered, and the relevance between the respective advantages and the area distribution is neglected. The method considers the technical fusion and complementarity of the PS-InSAR and SBAS-InSAR technologies in monitoring the ground settlement of the complex ground object reflection area, and utilizes the characteristics and advantages of the PS point high-coherence permanent scatterer and the SDFP pixel solution.
(2) The method disclosed by the invention integrates PS-InSAR and SBAS-InSAR technologies, corresponds to a complex ground object reflecting area, and overcomes the respective technical defects that the monitoring data loss is caused by phase jump in a building dense area of the SBAS-InSAR technology and the monitoring data loss is caused by the coherence of the space-time loss of the PS-InSAR technology in suburbs.
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FIG. 1 is a block flow diagram of the method of the present invention.
Detailed Description
As shown in fig. 1, which is a flow chart of the method of the present invention, it can be seen from fig. 1 that the ground settlement monitoring method fusing different synthetic aperture radar interferometry provided by the present invention includes the following steps:
(1) Obtaining N SAR image data to form an SAR image data set, wherein N is more than or equal to 20; the image data is data of imaging the same earth surface observation scene by the SAR;
(2) Carrying out permanent scatterer InSAR processing, namely PS-InSAR processing on the SAR image data set obtained in the step (1), solving a ground settlement monitoring result, and obtaining PS points which meet the condition that a coherence threshold C is a screening condition, wherein C is preferably more than or equal to 0.9;
the buildings based on urban areas are concentrated and the surface deformation amount is small, so that more stable PS points can be obtained in the PS-InSAR interference processing process, and the high-coherence PS points can exist on both the buildings and the vegetation. Because the vegetation coverage rate of the urban area is far lower than that of the suburban area, the probability ratio of the distribution of the buildings and the vegetation can be obtained according to the probability density distribution-coherence coefficient histogram of the PS points reflected on the buildings and the vegetation, the coherence coefficient C corresponding to the maximum probability ratio is taken as a threshold value to screen all the PS points, and the screening result can ensure that the probability of the PS points appearing on the buildings is maximum.
According to the probability density distribution-correlation coefficient diagram obtained from the previous experimental results, the probability that the correlation coefficient of a building is greater than 0.9 is about 75%, the probability that the correlation coefficient of vegetation is greater than 0.9 is about 15%, the probability ratio is maximum 5.
(3) Arranging longitude and latitude coordinates (x, y) of all the PS points and corresponding coherence values c (reflecting the reliability or confidence of the calculation results of the PS points) of each PS point, and obtaining gravity center coordinates G (a, b) of the spatial distribution of the PS points by adopting a gravity center method; the formula of the center of gravity method is as follows:
the high coherence PS points satisfy the following density characteristics: mainly concentrated in urban areas, the distance between urban PS points is small, suburban PS points are dispersed, and the distance between PS points is large. Based on the characteristics, the DBSCAN clustering algorithm can be adopted to perform clustering analysis on the spatial distribution of all PS points, and finally the PS point distribution dense area meeting the density requirement is obtained.
(4) The DBSCAN clustering analysis is a clustering algorithm based on a high-density connected region, and barycentric coordinates of PS point space distribution are used as initial central points of the DBSCAN algorithm. Performing cluster analysis on longitude and latitude coordinates of all PS points, and dividing all PS points into a dense area and a sparse area;
firstly, selecting epsilon =750 and MinPts =50, wherein epsilon is a parameter of a field radius; a circle with one point as the center of a circle and epsilon as the radius is called an epsilon field of the point; the parameter MinPts is the minimum core object number; if the number of points contained in the epsilon area of a certain point is more than or equal to MinPts, the point is called a core object; the values of parameters epsilon and MinPts in the specific engineering need to be adjusted; taking G as a starting point, calculating a point G n The distance d to the point G, if d is less than or equal to 750, the point G is set n Due to the epsilon area of point G.
Inputting all PS points, and finding out all data object points with the sending density reaching from the G point to form a cluster;
if the selected data object point p is an edge point, selecting another data object point; if the selected data object point p is a core point, then starting from the point p, finding out all data object points with the sending density reaching from the point p, and repeating the steps to obtain a data object point set U with the sending density reaching from the point G;
(5) Extracting a minimum geometric boundary of a data object point set U, establishing an irregular triangular mesh, and correcting the minimum geometric boundary by taking a surface maximum side length D as a constraint condition, wherein the corrected minimum geometric boundary is a boundary L between a PS point distribution dense area and a PS point distribution sparse area, and D is preferably 4000-5000 m according to the discrete degree of detection points;
and establishing an outer envelope line of the data object point set U as a reference line for dividing the PS point distribution dense area and the sparse area. The method comprises the following specific steps: the minimum convex surface of the data object point set U is drawn first, and then a triangular mesh is created with all PS points in the minimum convex surface as nodes. Calculating the triangular mesh, wherein any side of any triangle is erased if the side is larger than the maximum side D, and the maximum side is about 4500m by measuring the side of the triangle on the edge of the minimum convex surface, and the actual maximum side is set to 4600m, so as to avoid erasing the triangular plate which is not erased inside; each triangle is traversed inward from the outer extent of the triangular mesh, and if the edge of the bounding triangle is less than the maximum edge length in the current iteration, the traversal will stop. And finally obtaining the minimum geometric boundary L after the data object point set U is corrected. In the concrete engineering, according to the discrete degree of the distribution of the high-coherence PS points, the maximum side length D is preferably 4000-5000 m.
(6) Carrying out SBAS-InSAR processing on the SAR image data set obtained in the step (1), calculating a ground settlement result of the small baseline set, and converting grid matrixes of the settlement monitoring result obtained by the SBAS-InSAR method into vector data;
based on a short baseline space set processing principle of the SBAS-InSAR technology, relevant space baseline values and space baseline values are set, and control parameters of all estimated and generated image interference pairs are screened to form an interference baseline set. A large number of stable high coherence points can be obtained in the image interference process. And eliminating interference pairs which do not meet the requirements by checking the coherence strength of each interference pair in the research area. The differential interference process is to perform conventional D-InSAR (differential synthetic aperture radar interferometry) processing on each interference pair in the small baseline set, and includes the following steps: removing the flat ground effect and the terrain effect to generate an original interference fringe pattern, filtering the interference pattern and unwrapping the phase. In the first step of interference process, interference influences the land leveling effect based on the track information between every two images, and the influence of a DEM (digital elevation) model on the land leveling effect is removed to generate an original interference fringe image and a coherence coefficient image. And secondly, carrying out noise reduction and filtering processing on the original interference fringe pattern to generate a filtered interference pattern. And thirdly, performing phase unwrapping on the filtered interference pattern to separate deformation information. And finally converting all the grid matrixes of the settlement monitoring result obtained by the SBAS-InSAR method into vector data.
(7) Updating the vector monitoring data in the sparse area obtained by the PS-InSAR method into the vector data obtained by the SBAS-InSAR method according to the boundary L obtained by calculation in the step (5) to obtain a final ground settlement monitoring result of the research area; the method comprises the following specific steps:
the boundary L obtained by calculation in the step (5) is formed by connecting PS points with high coherence values, the boundary correction is carried out by establishing a triangular grid, and the reliability of dividing a dense area and a sparse area of the PS point distribution is improved by adjusting the maximum side length of the triangular grid; partitioning all PS point spatial distribution obtained by PS-InSAR processing in the step (2) by using a boundary L, and removing PS-InSAR monitoring results of all PS point distribution sparse areas; and (4) retaining the PS-InSAR vector monitoring result in the dense area and the SBAS-InSAR vector monitoring result in the sparse area in the step (6) by using a boundary L to finally obtain a fused ground settlement monitoring result.
The present invention has not been described in detail as is known to those skilled in the art.
Claims (4)
1. A ground settlement monitoring method fusing different synthetic aperture radar interferometry is characterized by comprising the following steps:
step 1, obtaining N synthetic aperture radar SAR image data to form an SAR image data set, wherein N is more than or equal to 20; the image data is data obtained by imaging the same earth surface observation scene by the SAR and completing image registration;
step 2, carrying out permanent scatterer synthetic aperture radar interferometry (PS-InSAR) processing on the SAR image data set obtained in the step 1, solving a ground settlement monitoring result, and obtaining PS points meeting the condition that a coherence threshold C is a screening condition;
step 3, sorting longitude and latitude coordinates (x, y) of all the PS points and a corresponding correlation value c of the reliability or confidence coefficient of each calculation result reflecting the PS points, and obtaining a gravity center coordinate G (a, b) of the spatial distribution of the PS points by adopting a gravity center method;
step 4, taking the gravity center coordinates G (a, b) as an initial clustering center point of the DBSCAN clustering algorithm, carrying out clustering analysis on longitude and latitude coordinates (x, y) of all PS points, and dividing the spatial distribution of all PS points into a dense area and a sparse area;
step 5, obtaining the minimum geometric boundary of the discrete points of the PS point dense region, establishing an irregular triangular grid, and correcting the minimum geometric boundary by taking the maximum side length D of the surface of the triangular grid as a constraint condition, wherein the corrected minimum geometric boundary is the boundary L between the PS point distribution dense region and the PS point distribution sparse region;
step 6, carrying out short-baseline set-forming aperture radar interferometry (SBAS-InSAR) processing on the SAR image data set obtained in the step 1, resolving a ground settlement monitoring result, and converting raster data of the ground settlement monitoring result obtained by the SBAS-InSAR method into vector data;
and 7, updating the PS-InSAR monitoring result data in the sparse area into vector data of the SBAS-InSAR method of the corresponding area according to the boundary L, and obtaining the final ground settlement monitoring result of the whole research area.
2. The method for monitoring ground subsidence by fusing interferometry of different synthetic aperture radars according to claim 1, wherein the method comprises the following steps: and the coherence threshold C in the step 2 is more than or equal to 0.9.
3. The method for monitoring ground subsidence by fusing interferometry of different synthetic aperture radars according to claim 1, wherein the method comprises the following steps: the gravity center method in the step 3 is as follows:
wherein, a and b are the abscissa and ordinate of the barycentric coordinate; x and y are the abscissa and the ordinate of each PS point; c is the corresponding coherence value for each PS point.
4. The method for monitoring ground subsidence by fusing interferometry of different synthetic aperture radars according to claim 1, wherein the method comprises the following steps: and in the step 5, the maximum side length D is 4000-5000 m according to the discrete degree of the detection points.
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