CN116106904B - Facility deformation monitoring method and facility deformation monitoring equipment for object MT-InSAR - Google Patents
Facility deformation monitoring method and facility deformation monitoring equipment for object MT-InSAR Download PDFInfo
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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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- 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
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- G01S13/9023—SAR image post-processing techniques combined with interferometric techniques
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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- G01B7/16—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge
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Abstract
The invention discloses a facility deformation monitoring method and equipment for object-oriented MT-InSAR, wherein the method comprises the following steps: acquiring a building 3D model and a synthetic aperture radar image of an urban facility, and primarily dividing the synthetic aperture radar image to obtain a plurality of facility objects; constructing a two-dimensional resolving network based on a plurality of facility objects, and resolving deformation parameters and height parameters of the facility objects based on the two-dimensional resolving network; screening scattering points of each facility object according to the time coherence, and carrying out registration correction on the scattering points and the building 3D model based on deformation parameters and height parameters to obtain a corrected resolving result of each facility object; and constructing a global reference network, and connecting all the resolving results based on the global reference network to obtain the urban facility deformation monitoring result in the final area range. According to the invention, by introducing an object-oriented processing method, the space fineness and deformation accuracy required by urban infrastructure deformation monitoring are satisfied.
Description
Technical Field
The invention relates to the technical field of urban facility detection, in particular to an object-oriented MT-InSAR facility deformation monitoring method, an object-oriented MT-InSAR facility deformation monitoring system, an object-oriented MT-InSAR facility deformation monitoring terminal and a computer-readable storage medium.
Background
Cities and metropolitan areas contribute to the majority of the economic growth in modern countries. Global rapid urbanization causes large populations to migrate to cities, and urban infrastructures such as buildings, roads and bridges play a key role in coping with the pressure caused by rapid population growth. Thus, maintaining the security of the infrastructure is critical to achieving sustainable cities and communities, but there are various artificial and natural risk factors that can affect the health of the urban infrastructure and cause disasters such as deformation, collapse, etc. of the infrastructure. Therefore, it is critical to develop large area high resolution infrastructure deformation monitoring on a regular basis.
Multi-time-coherent synthetic aperture radar (Multi-temporal Interferometric Synthetic Aperture Radar, MT-InSAR) is a common technique for measuring ground deformation. As the spatial resolution and data availability of SAR images (synthetic aperture radar images) increases, many studies began to use MT-InSAR for infrastructure monitoring. The advantages of MT-InSAR compared to traditional on-site measurement methods include the large-scale, contactless, high-density monitoring features, which are more challenging for urban infrastructure deformation measurement than other MT-InSAR application scenarios (such as crust deformation measurement and mining subsidence measurement) due to the complex environment and specific engineering standards. On the one hand, urban as-built environments are very spatially heterogeneous, and the composition of the pixels therefore becomes complex: there are many types of diffusers including distributed diffusers, permanent diffusers, overlay-masked diffusers, shadows, and the like. In addition, some common assumptions based on homogeneity characteristics are broken. Infrastructure security assessment, on the other hand, requires a fine interpretation of the deformations. This means that a higher dot density, more accurate positioning and a higher accuracy of deformation measurement are required. Thus, MT-InSAR measurement of urban infrastructure deformation remains a significant challenge.
In the InSAR algorithm development history, two stages are essentially experienced. The first stage is an image-based process in which the elementary data units are images. Differential interferometry, unwrapping, filtering and parameter estimation are all performed on the image, and this processing method has the advantage of easy implementation and fast computation. The traditional D-InSAR, as well as most SBAS and Stacking algorithms, can be categorized into this class. However, it is also apparent that image-based processing relies on grid operation, failing to perform fine discrimination processing on high-quality and low-quality pixels, resulting in reduced spatial resolution and measurement accuracy of the result. The second stage is pixel-based processing, where only pixels with small variances are selected for further processing. By focusing on high quality pixels, multi-view filtering is not required and maximum resolution can be maintained. At the same time, contamination from adjacent low quality pixels is reduced. After the continued development of MT-InSAR technology, the information contained in SAR pixels has been fully explored. Therefore, the accuracy of the results given by the existing algorithm almost reaches the upper limit provided by the available data, but cannot meet the spatial fineness and deformation accuracy required by urban infrastructure deformation monitoring.
Accordingly, the prior art is still in need of improvement and development.
Disclosure of Invention
The invention mainly aims to provide an object-oriented MT-InSAR facility deformation monitoring method and device, and aims to solve the problem that the prior art cannot meet the space fineness and deformation accuracy required by urban infrastructure deformation monitoring.
In order to achieve the above object, the present invention provides a method for monitoring deformation of an object-oriented MT-InSAR, the method for monitoring deformation of an object-oriented MT-InSAR comprising:
acquiring a building 3D model and a synthetic aperture radar image of an urban facility, and primarily dividing the synthetic aperture radar image to obtain a plurality of facility objects;
constructing a two-dimensional resolving network based on a plurality of facility objects, and resolving deformation parameters and height parameters of the facility objects based on the two-dimensional resolving network;
screening scattering points of each facility object according to the time coherence, and carrying out registration correction on the scattering points and the building 3D model based on the deformation parameters and the height parameters to obtain a corrected resolving result of each facility object;
and constructing a global reference network, and connecting all the resolving results based on the global reference network to obtain the urban facility deformation monitoring result in the final area range.
Optionally, in the method for monitoring facility deformation of object-oriented MT-InSAR, the acquiring a building 3D model of an urban facility and a synthetic aperture radar image, and performing preliminary segmentation on the synthetic aperture radar image to obtain a plurality of facility objects specifically includes:
acquiring a synthetic aperture radar image based on a multi-time interference synthetic aperture radar, constructing a grid model of a city based on a three-dimensional point cloud of the city, and uniformly sampling a three-dimensional space of the network model to obtain a plurality of uniformly-spaced three-dimensional points;
according to the geometric relation of radar imaging, calculating to obtain pixel row and column numbers corresponding to each three-dimensional point on the synthetic aperture radar image, and storing the pixel row and column numbers to obtain a lookup table;
semantic segmentation is carried out on the three-dimensional points based on a machine learning method to obtain infrastructure three-dimensional points of cities, and clustering is carried out on the infrastructure three-dimensional points based on a spatial clustering algorithm to obtain a plurality of facility objects;
and independently numbering each facility object, and searching a radar pixel set corresponding to each numbered facility pair based on the lookup table.
Optionally, the method for monitoring facility deformation of object-oriented MT-InSAR, wherein the constructing a two-dimensional resolving network based on the facility object, and resolving deformation parameters and height parameters of the facility object based on the two-dimensional resolving network, specifically includes:
Traversing the radar pixel set to obtain pixel positions and object categories of each facility object, determining a parameter resolving model based on the object categories, and obtaining stable radar scattering points of the facility objects;
connecting the stable radar scattering points according to a space full-connection network to obtain a plurality of arc segments, and searching deformation parameters and height parameters of the facility object based on the parameter calculation model grid;
performing time dimension unwrapping on the arc segments based on the deformation parameters and the height parameters, and performing parameter estimation on the unwrapped arc segments according to least square to obtain arc segment parameters and time coherence of the arc segments;
setting a threshold value of the time coherence, and carrying out net adjustment on arc sections with the time coherence being larger than the threshold value to obtain the resolving values of the deformation parameters and the height parameters.
Optionally, in the method for monitoring facility deformation of object-oriented MT-InSAR, parameter estimation is performed on the unwound arc segment according to least square, so as to obtain an arc segment parameter and time coherence of the arc segment, and then the method further includes:
and calculating a time coherence average value of an arc section connected with each stable radar scattering point, and evaluating the phase stability of the stable radar scattering points based on the time coherence average value.
Optionally, the method for monitoring facility deformation of object-oriented MT-InSAR, wherein traversing the radar pixel set to obtain a pixel position and an object class of each facility object, determining a parameter solution model based on the object class, and then further includes:
and if the facility object is a temperature deformation sensitive facility object, adding a temperature element or a node deformation factor to the parameter calculation model, and performing distributed scatterer phase optimization.
Optionally, in the method for monitoring facility deformation of object-oriented MT-InSAR, the screening scattering points of each facility object according to time coherence, and performing registration correction on the scattering points and the building 3D model based on the deformation parameter and the height parameter to obtain a corrected solution result of each facility object, which specifically includes:
screening scattering points of each facility object according to the time coherence, and establishing a conversion function from the scattering points of a radar coordinate system to a geographic coordinate system based on the parameter information of the synthetic aperture radar image;
converting the scattering points into three-dimensional point clouds of a geographic coordinate system based on the conversion function, and registering the three-dimensional point clouds with external point clouds of the building 3D model to obtain a starting point elevation value of the facility object;
Setting a distance threshold value between the three-dimensional point cloud and the external point cloud, and judging whether the distance between the three-dimensional point cloud and the external point cloud exceeds the distance threshold value;
and if the distance threshold is not exceeded, correcting the spatial position of the scattering point, and if the distance threshold is exceeded, removing the scattering point.
Optionally, in the method for monitoring facility deformation of object-oriented MT-InSAR, the constructing a global reference network, and connecting all the solutions based on the global reference network to obtain a city facility deformation monitoring result in a final area range specifically includes:
screening out different pixel point subsets of the facility equipment based on the time coherence average value, constructing a Delaunay triangulation based on the pixel point subsets, and overlapping triangular network arc segments of the Delaunay triangulation in a union mode to obtain a global reference network;
and carrying out deformation parameter calculation on the global reference network to obtain a target calculation result of the facility object, connecting the target calculation result to obtain a global 4D point cloud of the urban facility in a final area range, and obtaining a urban facility deformation monitoring result based on the global 4D point cloud.
Optionally, the object-oriented MT-InSAR facility deformation monitoring method, wherein the object-oriented MT-InSAR facility deformation monitoring system includes:
the object segmentation module is used for acquiring a building 3D model of urban facilities and a synthetic aperture radar image, and performing preliminary segmentation on the synthetic aperture radar image to obtain a plurality of facility objects;
the parameter resolving module is used for constructing a two-dimensional resolving network based on a plurality of facility objects and resolving deformation parameters and height parameters of the facility objects based on the two-dimensional resolving network;
the registration correction module is used for screening scattering points of each facility object according to the time coherence, carrying out registration correction on the scattering points and the building 3D model based on the deformation parameters and the height parameters, and obtaining a corrected resolving result of each facility object;
and the result acquisition module is used for constructing a global reference network, and connecting all the resolving results based on the global reference network to obtain the urban facility deformation monitoring result in the final area range.
In addition, to achieve the above object, the present invention also provides a terminal, wherein the terminal includes: the system comprises a memory, a processor and an object-oriented MT-InSAR facility deformation monitoring program which is stored in the memory and can run on the processor, wherein the object-oriented MT-InSAR facility deformation monitoring program realizes the steps of the object-oriented MT-InSAR facility deformation monitoring method when being executed by the processor.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium storing an object-oriented MT-InSAR facility deformation monitoring program, which when executed by a processor, implements the steps of the object-oriented MT-InSAR facility deformation monitoring method as described above.
The invention discloses a facility deformation monitoring method and equipment for object-oriented MT-InSAR, wherein the method comprises the following steps: acquiring a building 3D model and a synthetic aperture radar image of an urban facility, and primarily dividing the synthetic aperture radar image to obtain a plurality of facility objects; constructing a two-dimensional resolving network based on a plurality of facility objects, and resolving deformation parameters and height parameters of the facility objects based on the two-dimensional resolving network; screening scattering points of each facility object according to the time coherence, and carrying out registration correction on the scattering points and the building 3D model based on the deformation parameters and the height parameters to obtain a corrected resolving result of each facility object; and constructing a global reference network, and connecting all the resolving results based on the global reference network to obtain the urban facility deformation monitoring result in the final area range. The invention uses object-oriented MT-InSAR processing, and can realize finer processing on processing strategy selection and additional information constraint by utilizing the spatial integrity characteristics of objects. There are two significant advantages: on the one hand, the two-dimensional resolving network is built by taking the object as a unit, the network can be more dense and effective, the sparsity and the irrational property of the global pixel network are effectively avoided; on the other hand, object-oriented Gao Chengjie algorithm results may be subject to object-level fine correlation with external data, thereby eliminating coarse difference points while correcting Gao Chengjie algorithm bias. By introducing an object-oriented processing method, the existing MT-InSAR performance is improved, so that the space fineness and deformation accuracy required by urban infrastructure deformation monitoring are met.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the subject MT-InSAR-oriented facility deformation monitoring method of the present invention;
FIG. 2 is a schematic diagram of identifying three-dimensional points of an urban infrastructure based on existing machine learning methods;
FIG. 3 is a schematic diagram of a set of radar pixels corresponding to a facility object in an embodiment of the invention;
FIG. 4 is a schematic illustration of a fully connected network of the present invention;
FIG. 5 is a schematic diagram of an embodiment of the present invention prior to registration of a three-dimensional point cloud;
FIG. 6 is a schematic diagram of a three-dimensional point cloud registration according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a global reference network constructed based on overlay Delaunay in accordance with the present invention;
FIG. 8 is a schematic diagram of an urban infrastructure global 4D point cloud according to an embodiment of the invention;
FIG. 9 is a schematic diagram of a global 4D point cloud of an urban installation including temperature deformation coefficients according to an embodiment of the invention;
FIG. 10 is a schematic diagram of temperature deformation coefficients of an embodiment of the present invention;
FIG. 11 is a schematic diagram of a deformation curve of an embodiment of the present invention;
FIG. 12 is a schematic diagram of a preferred embodiment of the subject MT-InSAR-oriented facility deformation monitoring system of the present subject matter;
FIG. 13 is a schematic view of the operating environment of a preferred embodiment of the terminal of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear and clear, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It should be noted that, if directional indications (such as up, down, left, right, front, and rear … …) are included in the embodiments of the present invention, the directional indications are merely used to explain the relative positional relationship, movement conditions, etc. between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
In the method for monitoring the deformation of the object-oriented MT-InSAR facility according to the preferred embodiment of the present invention, as shown in FIG. 1, the method for monitoring the deformation of the object-oriented MT-InSAR facility comprises the following steps:
and S10, acquiring a building 3D model of the urban facility and a synthetic aperture radar image, and primarily dividing the synthetic aperture radar image to obtain a plurality of facility objects.
The step S10 includes:
step S11, acquiring a synthetic aperture radar image based on a multi-time interference synthetic aperture radar, constructing a grid model of a city based on a three-dimensional point cloud of the city, and uniformly sampling a three-dimensional space of the grid model to obtain a plurality of uniformly-spaced three-dimensional points;
step S12, according to the geometric relation of radar imaging, calculating to obtain pixel row and column numbers corresponding to each three-dimensional point on the synthetic aperture radar image, and storing the pixel row and column numbers to obtain a lookup table;
s13, carrying out semantic segmentation on the three-dimensional points based on a machine learning method to obtain infrastructure three-dimensional points of a city, and clustering the infrastructure three-dimensional points based on a spatial clustering algorithm to obtain a plurality of facility objects;
and S14, independently numbering each facility object, and searching a radar pixel set corresponding to each numbered facility object based on the lookup table.
Specifically, acquiring a synthetic aperture radar image based on a multi-time-coherence synthetic aperture radar requires preliminary segmentation of a facility object on a SAR image (Synthetic Aperture Radar, synthetic aperture radar image). Due to the characteristic of SAR time imaging, the adjacency relation of pixel points on a radar coordinate system is inconsistent with the real space relation, and the phenomena of overlapping and masking, shrinkage, shadow and the like exist, so that a method for dividing an object directly based on SAR images has great challenges. However, the object extraction by using the external space three-dimensional point cloud under the geographic coordinate system is more visual and accurate, and if the SAR object can be effectively associated with the space point of the geographic coordinate system, the problem of object extraction on the SAR image can be solved. Therefore, the invention designs a novel SAR object extraction method based on a 3D lookup table, specifically, a grid model of a city is built based on three-dimensional point cloud of the city, and three-dimensional space uniform sampling is carried out on the network model, so that all three-dimensional points with uniform intervals in a plurality of display scenes are obtained; according to the geometric relationship of radar imaging, calculating to obtain the corresponding pixel row and column numbers of each three-dimensional point on the synthetic aperture radar image, and storing the pixel row and column numbers in a lookup table form; semantic segmentation of three-dimensional points using existing machine learning methods (e.g., pointnet++ deep neural network algorithms), identifying urban infrastructure three-dimensional points, as shown in FIG. 2; based on a spatial clustering algorithm, clustering three-dimensional points of an infrastructure into independent objects, and independently numbering each independent object; searching a radar pixel set corresponding to each independent object according to the lookup table, as shown in fig. 3; the main idea of the SAR object extraction method is to design a 3D lookup table method, and effectively correlate space points with radar pixels, so that object segmentation results of a 3D model in geographic coordinates can be correlated to SAR images; it should be noted here that, due to the aliasing effect, each pixel of the radar may correspond to a plurality of ground objects (for example, building facades, floors, etc.), so that the SAR object extraction stores pixels in units of objects, and the same pixel can be recorded in a plurality of object sets, so that ground object aliasing information can be fully preserved.
And step S20, constructing a two-dimensional resolving network based on a plurality of facility objects, and resolving deformation parameters and height parameters of the facility objects based on the two-dimensional resolving network.
The step S20 includes:
step S21, traversing the radar pixel set to obtain pixel positions and object categories of each facility object, determining a parameter resolving model based on the object categories, and obtaining stable radar scattering points of the facility objects;
s22, connecting the stable radar scattering points according to a space full-connection network to obtain a plurality of arc segments, and searching deformation parameters and height parameters of the facility object based on the parameter calculation model grid;
step S23, carrying out time dimension unwrapping on the arc segments based on the deformation parameters and the height parameters, and carrying out parameter estimation on the unwrapped arc segments according to least square to obtain arc segment parameters and time coherence of the arc segments;
and step S24, setting a threshold value of the time coherence, and carrying out net adjustment on the arc segments with the time coherence being greater than the threshold value to obtain the resolving values of the deformation parameters and the height parameters.
Specifically, after the facility object is initially segmented from the SAR image, parameter calculation of the facility object may be performed next. In order to eliminate the uncorrelated signal image such as the atmosphere, a spatial network configuration calculation is required. The parameters to be solved in MT-InSAR generally include a deformation parameter and a height parameter (i.e. the deformation parameter is a deformation value of the facility object and the height parameter is an elevation value of the facility object), and if the deformation of the facility object is sensitive to temperature, a temperature deformation coefficient may be further added. Specifically, traversing the radar pixel set generated in step S14 to obtain a pixel position and an object type (e.g., a building, a road, a bridge, etc.) of each facility object, selecting a parameter resolving model according to the object type, wherein the parameter resolving model is different in whether to add a temperature or seasonal deformation factor (for a temperature deformation sensitive facility object) and whether to add a distributed scatterer phase optimization step (for a road object), and acquiring a stable radar scattering point of the facility object; connecting the stable radar scattering points by using a space full-connection network to obtain a plurality of arc segments, wherein a schematic diagram of the connection completion is shown in fig. 4; according to the selected parameter calculation model, calculating deformation parameters and height parameters by using a periodic graph method from arc segment to arc segment, namely, discretizing the deformation parameters and the height parameters within the value range of the two parameters, then carrying out periodic graph calculation from value to value, and leaving the parameters corresponding to the maximum value as a solution result; grid searching is carried out on the deformation parameters and the height parameters; performing time dimension unwrapping on the arc segments according to the searched parameters, and performing parameter estimation on the unwrapped arc segment phases by least square to obtain arc segment parameters and the time coherence of the arc segments; setting a threshold value of the time coherence, and carrying out net adjustment on arc sections with the time coherence being larger than the threshold value to obtain the resolving values of the deformation parameters and the height parameters. Because the parameter calculation is carried out based on the object in the invention, the method has two remarkable advantages, firstly, a proper calculation model can be selected according to the characteristics of the object, and the limitation that only one parameter calculation model can be used in one area in the prior method is overcome; secondly, a fully-connected dense two-dimensional network can be constructed on the object, and compared with a common triangular network, the fully-connected network has more processing and observation, so that the result is more robust, and in the prior art, if the fully-connected network is used, massive arc segments can be generated, and the processing cannot be performed.
Further, calculating a time coherence average value of an arc section connected with each stable radar scattering point, evaluating phase stability of the stable radar scattering points, namely quality of the scattering points, based on the time coherence average value, and calculating a later connection object by selecting a point with high coherence according to the quality of the scattering points.
And S30, screening scattering points of each facility object according to the time coherence, and carrying out registration correction on the scattering points and the building 3D model based on the deformation parameters and the height parameters to obtain a corrected resolving result of each facility object.
The step S30 includes:
s31, screening scattering points of each facility object according to time coherence, and establishing a conversion function from the scattering points of a radar coordinate system to a geographic coordinate system based on parameter information of the synthetic aperture radar image;
step S32, converting the scattering points into three-dimensional point clouds of a geographic coordinate system based on the conversion function, and registering the three-dimensional point clouds with external point clouds of the building 3D model to obtain a starting point elevation value of the facility object;
step S33, setting a distance threshold between the three-dimensional point cloud and the external point cloud, and judging whether the distance between the three-dimensional point cloud and the external point cloud exceeds the distance threshold;
And step S34, correcting the spatial position of the scattering point if the distance threshold is not exceeded, and removing the scattering point if the distance threshold is exceeded.
Specifically, after the scattering point elevation of the facility object is solved, the facility object can be converted into a geographic coordinate system to be registered with an external three-dimensional scene, and the resolving result is corrected. Specifically, scattering points of each facility object are screened out according to time coherence, and a conversion function from the scattering points of a radar coordinate system to a geographic coordinate system is established based on parameter information of the synthetic aperture radar image; converting the scattering points into three-dimensional point clouds of a geographic coordinate system based on the conversion function, registering the three-dimensional point clouds with external point clouds of the building 3D model (schematic diagram before registration is shown in fig. 5), and searching to obtain a starting point elevation value which is matched with the actual point clouds best; setting a distance threshold value, and removing radar pixel points with the distance exceeding the threshold value from the external point cloud; setting a distance threshold value between the three-dimensional point cloud and the external point cloud, and judging whether the distance between the three-dimensional point cloud and the external point cloud exceeds the distance threshold value; if the distance threshold is not exceeded, correcting the spatial position of the pixel point in the threshold to be the three-dimensional coordinate of the nearest point of the external point cloud, and if the distance threshold is exceeded, removing the scattering point; the three-dimensional spatial map of the registered radar scattering points is shown in fig. 6. The conversion process from radar coordinates to geospatial coordinates is affected by various error factors, such as satellite orbit, elevation accuracy, coordinate references, starting point elevation, etc.; the method has the advantages that the elevation error of the starting point is large, the horizontal position and the vertical position of the object are influenced at the same time, the starting point Gao Chengjin is reliably fixed in an external point cloud constraint mode, the elevation precision of SAR calculation is a certain difference from the elevation precision of space point cloud obtained by laser or photogrammetry, and invalid points can be effectively removed and calculated through fine registration. Meanwhile, the external three-dimensional position is directly assigned to the SAR pixel point, so that the spatial positioning precision of the radar scattering point can be remarkably improved, and the interpretability of the data is improved.
And S40, constructing a global reference network, and connecting all the calculation results based on the global reference network to obtain a city facility deformation monitoring result in a final area range.
The step S40 includes:
step S41, screening out different pixel point subsets of the facility equipment based on the time coherence average value, constructing a Delaunay triangulation based on the pixel point subsets, and overlapping and superposing triangulation arc sections of the Delaunay triangulation to obtain a global reference network;
and step S42, performing deformation parameter calculation on the global reference network to obtain a target calculation result of the facility object, connecting the target calculation result to obtain a global 4D point cloud of the urban facility in a final area range, and obtaining a urban facility deformation monitoring result based on the global 4D point cloud.
Specifically, a certain time coherence threshold is set, and pixel points for constructing a global network are screened out by comparing the time coherence average value with the time coherence threshold; in order to avoid isolated subnetworks, the invention provides a construction mode of superposing Delaunay triangulation (Delaunay triangulation: a set of continuous but non-overlapping triangles, and the circumscribed circles of the triangles do not contain any other point of the area), which comprises the following specific paths: screening out different pixel point subsets by using different thresholds, constructing a Delaunay triangular network by each subset, and then overlapping all triangular network arc segments to obtain a global reference network, as shown in figure 7; performing deformation parameter calculation on the global reference network or referencing the parameter calculation result of each point on the global reference network; connecting the result of the facility object to a global reference network to obtain a city facility global 4D point cloud (namely, a space three-dimensional and a deformation dimension) in a final area range, as shown in FIG. 8; in addition, the global 4D point cloud of the urban facility for the temperature deformation sensitive facility object is shown in fig. 9, the temperature deformation coefficient is shown in fig. 10, and the deformation curve is shown in fig. 11.
Further, as shown in fig. 12, based on the above-mentioned object-oriented MT-InSAR facility deformation monitoring method, the present invention further provides an object-oriented MT-InSAR facility deformation monitoring system, where the object-oriented MT-InSAR facility deformation monitoring system includes:
the object segmentation module 51 is configured to acquire a building 3D model of an urban facility and a synthetic aperture radar image, and perform preliminary segmentation on the synthetic aperture radar image to obtain a plurality of facility objects;
a parameter resolving module 52, configured to construct a two-dimensional resolving network based on the facility object, and resolve a deformation parameter and a height parameter of the facility object based on the two-dimensional resolving network;
the registration correction module 53 is configured to screen out a scattering point of each facility object according to time coherence, and perform registration correction on the scattering point and the building 3D model based on the deformation parameter and the height parameter, so as to obtain a corrected solution result of each facility object;
and the result acquisition module 54 is configured to construct a global reference network, and connect all the solution results based on the global reference network to obtain the urban facility deformation monitoring result in the final area range.
Further, as shown in fig. 13, the present invention further provides a terminal based on the above-mentioned object-oriented MT-InSAR facility deformation monitoring method, where the terminal includes a processor 10, a memory 20 and a display 30; fig. 13 shows only some of the components of the terminal, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may alternatively be implemented.
The memory 20 may in some embodiments be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The memory 20 may in other embodiments also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal. Further, the memory 20 may also include both an internal storage unit and an external storage device of the terminal. The memory 20 is used for storing application software installed in the terminal and various data, such as program codes of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores an object MT-InSAR-oriented facility deformation monitoring program 40, and the object MT-InSAR-oriented facility deformation monitoring program 40 is executable by the processor 10 to implement the object MT-InSAR-oriented facility deformation monitoring method in the present application.
The processor 10 may in some embodiments be a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip for executing program code or processing data stored in the memory 20, such as performing the object-oriented MT-InSAR facility deformation monitoring method, etc.
The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like in some embodiments. The display 30 is used for displaying information at the terminal and for displaying a visual user interface. The components 10-30 of the terminal communicate with each other via a system bus.
In an embodiment, the following steps are implemented when the processor 10 executes the object MT-InSAR-oriented facility deformation monitoring program 40 in the memory 20:
acquiring a building 3D model and a synthetic aperture radar image of an urban facility, and primarily dividing the synthetic aperture radar image to obtain a plurality of facility objects;
constructing a two-dimensional resolving network based on the facility object, and resolving deformation parameters and height parameters of the facility object based on the two-dimensional resolving network;
Screening scattering points of each facility object according to the time coherence, and carrying out registration correction on the scattering points and the building 3D model based on the deformation parameters and the height parameters to obtain a corrected resolving result of each facility object;
and constructing a global reference network, and connecting all the resolving results based on the global reference network to obtain the urban facility deformation monitoring result in the final area range.
The method comprises the steps of obtaining a building 3D model of urban facilities and a synthetic aperture radar image, and primarily dividing the synthetic aperture radar image to obtain a plurality of facility objects, wherein the method specifically comprises the following steps:
acquiring a synthetic aperture radar image based on a multi-time interference synthetic aperture radar, constructing a grid model of a city based on a three-dimensional point cloud of the city, and uniformly sampling a three-dimensional space of the network model to obtain a plurality of uniformly-spaced three-dimensional points;
according to the geometric relation of radar imaging, calculating to obtain pixel row and column numbers corresponding to each three-dimensional point on the synthetic aperture radar image, and storing the pixel row and column numbers to obtain a lookup table;
semantic segmentation is carried out on the three-dimensional points based on a machine learning method to obtain infrastructure three-dimensional points of cities, and clustering is carried out on the infrastructure three-dimensional points based on a spatial clustering algorithm to obtain a plurality of facility objects;
And independently numbering each facility object, and searching a radar pixel set corresponding to each numbered facility pair based on the lookup table.
The method for constructing the two-dimensional resolving network based on the facility object, and resolving deformation parameters and height parameters of the facility object based on the two-dimensional resolving network specifically comprises the following steps:
traversing the radar pixel set to obtain pixel positions and object categories of each facility object, determining a parameter resolving model based on the object categories, and obtaining stable radar scattering points of the facility objects;
connecting the stable radar scattering points according to a space full-connection network to obtain a plurality of arc segments, and searching deformation parameters and height parameters of the facility object based on the parameter calculation model grid;
performing time dimension unwrapping on the arc segments based on the deformation parameters and the height parameters, and performing parameter estimation on the unwrapped arc segments according to least square to obtain arc segment parameters and time coherence of the arc segments;
setting a threshold value of the time coherence, and carrying out net adjustment on arc sections with the time coherence being larger than the threshold value to obtain the resolving values of the deformation parameters and the height parameters.
The method comprises the steps of carrying out parameter estimation on the unwound arc section according to least square to obtain arc section parameters and time coherence of the arc section, and then further comprising:
and calculating a time coherence average value of an arc section connected with each stable radar scattering point, and evaluating the phase stability of the stable radar scattering points based on the time coherence average value.
The step of traversing the radar pixel set to obtain the pixel position and the object category of each facility object, and determining a parameter resolving model based on the object category, and then further comprises the steps of:
and if the facility object is a temperature deformation sensitive facility object, adding a temperature element or a node deformation factor to the parameter calculation model, and performing distributed scatterer phase optimization.
The method specifically includes the steps of screening scattering points of each facility object according to time coherence, carrying out registration correction on the scattering points and the building 3D model based on the deformation parameters and the height parameters, and obtaining corrected resolving results of each facility object, wherein the resolving results specifically include:
screening scattering points of each facility object according to the time coherence, and establishing a conversion function from the scattering points of a radar coordinate system to a geographic coordinate system based on the parameter information of the synthetic aperture radar image;
Converting the scattering points into three-dimensional point clouds of a geographic coordinate system based on the conversion function, and registering the three-dimensional point clouds with external point clouds of the building 3D model to obtain a starting point elevation value of the facility object;
setting a distance threshold value between the three-dimensional point cloud and the external point cloud, and judging whether the distance between the three-dimensional point cloud and the external point cloud exceeds the distance threshold value;
if the distance threshold is not exceeded, correcting the spatial position of the scattering point, and if the distance threshold is exceeded, removing the scattering point;
the construction of the global reference network, and connecting all the resolving results based on the global reference network to obtain the urban facility deformation monitoring result in the final area range, specifically comprising the following steps:
screening out different pixel point subsets of the facility equipment based on the time coherence average value, constructing a Delaunay triangulation based on the pixel point subsets, and overlapping triangular network arc segments of the Delaunay triangulation in a union mode to obtain a global reference network;
and carrying out deformation parameter calculation on the global reference network to obtain a target calculation result of the facility object, connecting the target calculation result to obtain a global 4D point cloud of the urban facility in a final area range, and obtaining a urban facility deformation monitoring result based on the global 4D point cloud.
The present invention also provides a computer-readable storage medium storing an object-oriented MT-InSAR facility deformation monitoring program which, when executed by a processor, implements the steps of the object-oriented MT-InSAR facility deformation monitoring method described above.
In summary, the present invention provides a method and an apparatus for monitoring facility deformation of object-oriented MT-InSAR, where the method includes: acquiring a building 3D model and a synthetic aperture radar image of an urban facility, and primarily dividing the synthetic aperture radar image to obtain a plurality of facility objects; constructing a two-dimensional resolving network based on the facility object, and resolving deformation parameters and height parameters of the facility object based on the two-dimensional resolving network; screening scattering points of each facility object according to the time coherence, and carrying out registration correction on the scattering points and the building 3D model based on the deformation parameters and the height parameters to obtain a corrected resolving result of each facility object; and constructing a global reference network, and connecting all the resolving results based on the global reference network to obtain the urban facility deformation monitoring result in the final area range. The invention uses object-oriented MT-InSAR processing, and can realize finer processing on processing strategy selection and additional information constraint by utilizing the spatial integrity characteristics of objects. There are two significant advantages: on one hand, the two-dimensional resolving network is built by taking the object as a unit, so that the network can be more dense and effective, and the sparsity and irrational property of the global pixel network are effectively avoided; on the other hand, object-oriented Gao Chengjie algorithm results may be subject to object-level fine correlation with external data, thereby eliminating coarse difference points while correcting Gao Chengjie algorithm bias. By introducing an object-oriented processing method, the existing MT-InSAR performance is improved, so that the space fineness and deformation accuracy required by urban infrastructure deformation monitoring are met.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal comprising the element.
Of course, those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by a computer program for instructing relevant hardware (e.g., processor, controller, etc.), the program may be stored on a computer readable storage medium, and the program may include the above described methods when executed. The computer readable storage medium may be a memory, a magnetic disk, an optical disk, etc.
It is to be understood that the invention is not limited in its application to the examples described above, but is capable of modification and variation in light of the above teachings by those skilled in the art, and that all such modifications and variations are intended to be included within the scope of the appended claims.
Claims (9)
1. The facility deformation monitoring method based on the object-oriented MT-InSAR is characterized by comprising the following steps of:
acquiring a building 3D model and a synthetic aperture radar image of an urban facility, and primarily dividing the synthetic aperture radar image to obtain a plurality of facility objects;
constructing a two-dimensional resolving network based on a plurality of facility objects, and resolving deformation parameters and height parameters of the facility objects based on the two-dimensional resolving network;
the construction of a two-dimensional resolving network based on a plurality of facility objects, and resolving deformation parameters and height parameters of the facility objects based on the two-dimensional resolving network, specifically includes:
traversing the radar pixel set to obtain pixel positions and object categories of each facility object, determining a parameter resolving model based on the object categories, and obtaining stable radar scattering points of the facility objects;
connecting the stable radar scattering points according to a space full-connection network to obtain a plurality of arc segments, and performing grid search based on the parameter calculation model to obtain deformation parameters and height parameters of the facility object;
performing time dimension unwrapping on the arc segments based on the deformation parameters and the height parameters, and performing parameter estimation on the unwrapped arc segments according to least square to obtain arc segment parameters and time coherence of the arc segments;
Setting a threshold value of the time coherence, and carrying out net adjustment on arc sections with the time coherence being greater than the threshold value to obtain a solution value of the deformation parameter and the height parameter;
screening scattering points of each facility object according to the time coherence, and carrying out registration correction on the scattering points and the building 3D model based on the deformation parameters and the height parameters to obtain a corrected resolving result of each facility object;
and constructing a global reference network, and connecting all the resolving results based on the global reference network to obtain the urban facility deformation monitoring result in the final area range.
2. The method for monitoring facility deformation based on object-oriented MT-InSAR according to claim 1, wherein the steps of obtaining a building 3D model of an urban facility and a synthetic aperture radar image, and performing preliminary segmentation on the synthetic aperture radar image to obtain a plurality of facility objects comprise:
acquiring a synthetic aperture radar image based on a multi-time interference synthetic aperture radar, constructing a network model of a city based on a three-dimensional point cloud of the city, and uniformly sampling a three-dimensional space of the network model to obtain a plurality of uniformly-spaced three-dimensional points;
According to the geometric relation of radar imaging, calculating to obtain pixel row and column numbers corresponding to each three-dimensional point on the synthetic aperture radar image, and storing the pixel row and column numbers to obtain a lookup table;
semantic segmentation is carried out on the three-dimensional points based on a machine learning method to obtain infrastructure three-dimensional points of cities, and clustering is carried out on the infrastructure three-dimensional points based on a spatial clustering algorithm to obtain a plurality of facility objects;
and independently numbering each facility object, and searching a radar pixel set corresponding to each numbered facility pair based on the lookup table.
3. The method for monitoring deformation of an object-oriented MT-InSAR based on claim 1, wherein said estimating parameters of the unwound arc segment according to least squares results in arc segment parameters and time coherence of the arc segment, and further comprising:
and calculating a time coherence average value of an arc section connected with each stable radar scattering point, and evaluating the phase stability of the stable radar scattering points based on the time coherence average value.
4. The object-oriented MT-InSAR based facility deformation monitoring method of claim 1, wherein traversing the set of radar pixels results in pixel locations and object classes for each facility object, determining a parameter solution model based on the object classes, and then further comprising:
And if the facility object is a temperature deformation sensitive facility object, adding a temperature element or a node deformation factor to the parameter calculation model, and performing distributed scatterer phase optimization.
5. The method for monitoring deformation of an object-oriented MT-InSAR based facility according to claim 1, wherein the screening scattering points of each facility object according to time coherence, performing registration correction on the scattering points and the building 3D model based on the deformation parameters and the height parameters, and obtaining corrected solution results of each facility object specifically includes:
screening scattering points of each facility object according to the time coherence, and establishing a conversion function from the scattering points of a radar coordinate system to a geographic coordinate system based on the parameter information of the synthetic aperture radar image;
converting the scattering points into three-dimensional point clouds of a geographic coordinate system based on the conversion function, and registering the three-dimensional point clouds with external point clouds of the building 3D model to obtain a starting point elevation value of the facility object;
setting a distance threshold value between the three-dimensional point cloud and the external point cloud, and judging whether the distance between the three-dimensional point cloud and the external point cloud exceeds the distance threshold value;
And if the distance threshold is not exceeded, correcting the spatial position of the scattering point, and if the distance threshold is exceeded, removing the scattering point.
6. The facility deformation monitoring method based on object-oriented MT-InSAR according to claim 3, wherein the constructing a global reference network, and connecting all the solutions based on the global reference network, to obtain a city facility deformation monitoring result in a final area range, specifically comprises:
screening out different pixel point subsets of the facility equipment based on the time coherence average value, constructing a Delaunay triangulation based on the pixel point subsets, and overlapping triangular network arc segments of the Delaunay triangulation in a union mode to obtain a global reference network;
and carrying out deformation parameter calculation on the global reference network to obtain a target calculation result of the facility object, connecting the target calculation result to obtain a global 4D point cloud of the urban facility in a final area range, and obtaining a urban facility deformation monitoring result based on the global 4D point cloud.
7. An object-oriented MT-InSAR based facility deformation monitoring system, comprising:
The object segmentation module is used for acquiring a building 3D model of urban facilities and a synthetic aperture radar image, and performing preliminary segmentation on the synthetic aperture radar image to obtain a plurality of facility objects;
the parameter resolving module is used for constructing a two-dimensional resolving network based on a plurality of facility objects and resolving deformation parameters and height parameters of the facility objects based on the two-dimensional resolving network;
the parameter resolving module is specifically configured to:
traversing the radar pixel set to obtain pixel positions and object categories of each facility object, determining a parameter resolving model based on the object categories, and obtaining stable radar scattering points of the facility objects;
connecting the stable radar scattering points according to a space full-connection network to obtain a plurality of arc segments, and performing grid search based on the parameter calculation model to obtain deformation parameters and height parameters of the facility object;
performing time dimension unwrapping on the arc segments based on the deformation parameters and the height parameters, and performing parameter estimation on the unwrapped arc segments according to least square to obtain arc segment parameters and time coherence of the arc segments;
setting a threshold value of the time coherence, and carrying out net adjustment on arc sections with the time coherence being greater than the threshold value to obtain a solution value of the deformation parameter and the height parameter;
The registration correction module is used for screening scattering points of each facility object according to the time coherence, carrying out registration correction on the scattering points and the building 3D model based on the deformation parameters and the height parameters, and obtaining a corrected resolving result of each facility object;
and the result acquisition module is used for constructing a global reference network, and connecting all the resolving results based on the global reference network to obtain the urban facility deformation monitoring result in the final area range.
8. A terminal, the terminal comprising: memory, a processor and a program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the object oriented MT-InSAR based facility deformation monitoring method according to any one of claims 1-6.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a program which, when executed by a processor, implements the steps of the object-oriented MT-InSAR based facility deformation monitoring method according to any one of claims 1-6.
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