Air-ground combined InSAR geological disaster monitoring method and device

Abstract

The invention belongs to the field of surveying and mapping engineering, and particularly relates to an air-ground combined InSAR geological disaster monitoring method and device, and the method comprises the steps: respectively obtaining a satellite digital orthoimage of a disaster area and a first digital orthoimage of a disaster point, and carrying out the matching of an elevation model for the satellite digital orthoimage and the first digital orthoimage; fusing the satellite digital orthoimage and the first digital orthoimage to obtain a first fused digital orthoimage; fusing the first digital elevation model and the second digital elevation model to obtain a first fused digital elevation model; establishing a mapping relation between the first fusion digital orthoimage and the first fusion digital elevation model to obtain an air-ground combined three-dimensional terrain model; and carrying out fusion processing on the ground-based SAR and the air-ground combined three-dimensional terrain model to obtain an InSAR three-dimensional detection model. According to the method, the problems of low acquisition efficiency, limited monitoring range, insufficient data precision, poor real-time performance and the like of a traditional monitoring method are solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of surveying and mapping engineering, and particularly relates to an air-ground combined InSAR geological disaster monitoring method and device. BACKGROUND

[0002] In recent years, various natural disasters occur frequently in China, showing the characteristics of wide disaster influence range, obvious disaster area characteristics, significant monsoon climate influence, and heavy geological disaster loss. In addition, engineering construction has changed the original geological environment, resulting in situations such as collapse, landslide, debris flow, ground subsidence, ground fissure and ground subsidence, directly causing social and economic losses.

[0003] For geological disaster monitoring, domestic experts have proposed many monitoring methods, such as space-based monitoring technology. Space-based monitoring mainly relies on optical satellite images, synthetic aperture radar (SAR) images, satellite positioning, satellite-borne precipitation radar, etc. These methods are suitable for large-scale geological disaster screening and monitoring, have a long data acquisition period, are not high in accuracy, and are high in cost. Air-based monitoring mainly uses unmanned aerial vehicle low-altitude remote sensing technology to monitor various geological disaster macroscopic deformation signs, development characteristics and disaster rules, and is suitable for large-scale geological monitoring. The data period is higher than that of space-based monitoring, and the accuracy is affected by the attitude of the aircraft. In bad weather such as rain, snow and fog, the unmanned aerial vehicle is difficult to obtain surface subsidence observation data, and there is a risk of crashing. Land-based monitoring mainly uses global navigation satellite system (GNSS) receivers and total station measurement methods to monitor various geological disaster changes. Although the data accuracy is high, it is a non-continuous monitoring method, and the observation data cannot directly represent the surface deformation value of the area far away from the observation pier. For mountain landslide, debris flow and ground subsidence, GNSS and total station monitoring methods are not suitable, and the operation risk is high.

[0004] In view of the limitations of the prior art, the present application aims to provide an air-ground combined InSAR (Interferometric Synthetic Aperture Radar, Interferometric Synthetic Aperture Radar) geological disaster monitoring method, which can quickly establish a three-dimensional terrain model of the disaster area, realize large-scale, high-precision and real-time geological disaster monitoring, and improve the accuracy and response speed of disaster warning. SUMMARY

[0005] The present application provides an air-ground combined InSAR geological disaster monitoring method and device to solve the problems of low data acquisition efficiency, limited monitoring range, insufficient data accuracy, poor real-time performance and complex data processing of traditional methods.

[0006] To solve the above problems, the present application adopts the following scheme: The application discloses an air-ground joint InSAR geological disaster monitoring method. In a first aspect, the application provides an air-ground joint InSAR geological disaster monitoring method, comprising the following steps: obtaining a satellite digital orthographic image of a disaster area, and matching a first digital elevation model to the satellite digital orthographic image; obtaining a first digital orthographic image of a disaster point in the disaster area, and matching a second digital elevation model to the first digital orthographic image; the first digital orthographic image is obtained by a UAV; fusing the satellite digital orthographic image and the first digital orthographic image to obtain a first fused digital orthographic image, fusing the first digital elevation model and the second digital elevation model to obtain a first fused digital elevation model, establishing a mapping relationship between the first fused digital orthographic image and the first fused digital elevation model, and obtaining an air-ground joint three-dimensional terrain model; fusing ground-based SAR and the air-ground joint three-dimensional terrain model to obtain an InSAR stereoscopic detection model, and performing real-time monitoring on geological disasters in a disaster area based on the InSAR stereoscopic detection model.

[0007] Preferably, the fusing of the ground-based SAR and the air-ground joint three-dimensional terrain model comprises the following steps: obtaining InSAR orbit coordinates; determining a radar deformation azimuth angle; based on the InSAR orbit coordinates and the radar deformation azimuth angle, registering InSAR radar data to the air-ground joint three-dimensional terrain model, and obtaining an initial InSAR stereoscopic detection model; establishing an InSAR data observation point on the initial InSAR stereoscopic detection model, and obtaining an InSAR stereoscopic detection model.

[0008] Preferably, after the InSAR stereoscopic detection model is obtained, InSAR observation data obtained from the InSAR data observation point is further optimized, and the optimization comprises: combining an average correlation coefficient and an amplitude deviation method to obtain a correlation coefficient of adjacent images in the InSAR observation data, setting the correlation coefficient under a preset proportion as a threshold parameter, correcting the InSAR data observation point, and determining a final InSAR data observation point.

[0009] Preferably, the step of obtaining the InSAR orbit coordinates comprises: obtaining coordinates of an InSAR device at a disaster point in the disaster area through UAV aerial mapping; Based on the coordinates of the InSAR device, the single-point coordinate measurement method is used to measure the coordinate values between the InSAR track end points.

[0010] Preferably, determining the radar deformation azimuth angle specifically includes: According to the InSAR data observation points, InSAR original data is obtained; By differential interference method, the cumulative displacement of each InSAR data observation point in the InSAR original data is obtained; the deformation data of each InSAR data observation point is obtained; The deformation data obtained at different time points is differentially processed to obtain the relative deformation data in the corresponding time period; According to the deviation between the deformation data and the InSAR original data, the radar deformation azimuth angle is calculated.

[0011] Preferably, according to the deviation between the deformation data and the InSAR original data, the radar deformation azimuth angle is calculated specifically: According to the deformation data, the deformation coordinates of the InSAR track end points are obtained; According to the InSAR original data, the original coordinates of the InSAR track end points are obtained; The coordinates of the InSAR device are connected with the deformation coordinates of the InSAR track end points and the original coordinates of the InSAR track end points respectively, and the radar deformation azimuth angle is obtained.

[0012] Preferably, registering the InSAR observation data to the air-ground combined three-dimensional terrain model specifically includes: registering according to the terrain point cloud data, track coordinates and InSAR observation data.

[0013] Preferably, it also includes: after the initial InSAR stereo detection model is established, the spatial relationship is verified, and whether the position of the coordinates of the InSAR radar observation data is consistent with the position on the air-ground combined three-dimensional terrain model is checked. If not, re-register.

[0014] Preferably, it also includes: through the radar solving tool, the radar interference file in the format of DiffImage is solved into a deformation map spot and registered to the InSAR stereo detection model.

[0015] Preferably, the first digital orthographic image of the disaster point in the disaster area is obtained by using a UAV, the relative height of the UAV to the ground is controlled to be 30-40m, the image overlap rate of the heading and the lateral is 75%-85%, and the UAV flight area is greater than 50m of the disaster point.

[0016] Preferably, the image resolution of the first digital orthographic image is less than 5cm / pix, and pix represents a pixel.

[0017] Preferably, after acquiring the first digital orthographic image of the disaster point, the method further comprises calculating the acquired laser radar data into three-dimensional point cloud data.

[0018] Preferably, the method for constructing the second digital elevation model comprises: acquiring laser radar data by the unmanned aerial vehicle carrying the laser radar, calculating three-dimensional point cloud according to the acquired laser radar data, and separating the point cloud of the disaster point by point cloud clipping.

[0019] Preferably, the separating the point cloud of the disaster point by point cloud clipping specifically comprises: By establishing a deep learning classification sample of point cloud data, the point cloud data is classified and displayed as ground point cloud data by the Patchwork++ algorithm, and the non-displayed part is non-ground point cloud data, the Patchwork++ algorithm is optimized by adjusting the deep learning iteration parameters and increasing the deep learning training sample, and accurate ground point cloud data is obtained.

[0020] Based on the same concept, an air-ground combined InSAR geological disaster monitoring device is also proposed, comprising at least one processor and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to implement any one of the above-mentioned air-ground combined InSAR geological disaster monitoring methods.

[0021] Compared with the prior art, the beneficial effects of the present application are: The present application provides an air-ground combined InSAR geological disaster monitoring method, specifically comprising space-air-ground three-dimensional terrain model fusion, ground-based InSAR and three-dimensional terrain fusion, PSInSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) time series data calculation and multi-time series ground-based InSAR stereo monitoring, which ensures the continuity of the ground monitoring results, realizes real-time monitoring and early warning of geological disasters, improves the three-dimensional visualization monitoring effect of geological disasters, and solves the problems of long data observation period, low acquisition efficiency, limited monitoring range, insufficient data precision, poor real-time performance and complex data processing of traditional monitoring methods. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The flow chart of the air-ground combined InSAR geological disaster monitoring method in Embodiment 1 of the present application is shown in the figure. Figure 2 The effect diagram of the unmanned aerial vehicle, satellite and InSAR collected data fusion in Embodiment 1 of the present application is shown in the figure. Figure 3InSAR registration map in Example 1 of the present application; Figure 4 ground surface overall subsidence map in Example 2 of the present application; Figure 5 ground surface overall subsidence map in Example 2 of the present application;

[0023] Reference signs: 1-satellite image, 2-drone DOM image, 3-ground surface collapse area, 4-InSAR registration area, 5-InSAR position, 6-drone DEM model. DETAILED DESCRIPTION

[0024] The present application will be further described in conjunction with test examples and specific embodiments. However, this should not be understood as limiting the scope of the above-mentioned subject matter of the present application to the following examples only, and any technology realized based on the content of the present application falls within the scope of protection of the present application.

[0025] In the description of specific embodiments of the present application, the orientation or position relationship terms such as "up", "down", "left", "right", "center", "inner", "outer", "side" and the like appear without special indication, are expressed based on the orientation or position relationship shown in the drawings, or are the orientation or position relationship when the product / device / apparatus is usually used. These orientation or position relationship terms are only for the convenience of describing the present application scheme or simplifying the description in specific embodiments, for the convenience of the technical personnel to quickly understand the scheme, and cannot be understood as indicating or implying that a specific device / component / element must have a specific orientation, or be constructed and operated in a specific position relationship, and therefore cannot be understood as limiting the present application.

[0026] In the description of specific embodiments of the present application, the technical terms "first", "second" and the like only distinguish one entity or operation from another entity or operation, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of specific embodiments of the present application, the meaning of "multiple" is two and more than two, unless otherwise explicitly and specifically limited.

[0027] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase that the phrase in the specification do not all necessarily refer to the same embodiment, nor are they necessarily mutually exclusive or alternative embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined.

[0028] Example 1 An air-ground combined InSAR geological disaster monitoring method, a flowchart as Figure 1 shown, comprising the following steps: Obtaining a satellite digital orthographic image of a disaster area, matching a first digital elevation model to the satellite digital orthographic image; Obtaining a first digital orthographic image of a disaster point in the disaster area, matching a second digital elevation model to the first digital orthographic image; the first digital orthographic image is obtained by a UAV; Fusing the satellite digital orthographic image and the first digital orthographic image to obtain a first fused digital orthographic image; fusing the first digital elevation model and the second digital elevation model to obtain a first fused digital elevation model; establishing a mapping relationship between the first fused digital orthographic image and the first fused digital elevation model to obtain an air-ground combined three-dimensional terrain model; Fusing ground-based SAR and the air-ground combined three-dimensional terrain model to obtain an InSAR stereo detection model; based on the InSAR stereo detection model, real-time monitoring of geological disasters in the disaster area is performed.

[0029] Taking a practical engineering project as an example, referring to Figures 2 to 4 , the embodiment improves the geological disaster monitoring method and proposes an air-ground combined InSAR geological disaster monitoring method, which specifically comprises: I. Air-space three-dimensional terrain model fusion processing (1) Satellite terrain rapid acquisition In order to quickly establish a three-dimensional terrain model of the disaster area, the satellite digital orthographic image (DOM) of the disaster area is downloaded through the BIG MAP software, and the pixel resolution is 2m; the digital elevation model (DEM) data is applied to the surveying and mapping geographic information bureau, and the highest terrain accuracy is 12.5m, and the data effect is as Figure 2 shown. The peripheral area in the figure is satellite image 1, the UAV DOM image 2 is closer to the disaster area relative to the peripheral area, and the InSAR registration area 4 is closer to the surface collapse area 3 relative to the UAV DOM image 2, forming the characteristics that the image from the periphery to the surface collapse area 3 is clearer.

[0030] (2) Establish high-resolution DOM image Here the high-resolution digital orthophoto map (DOM) is reconstructed by using unmanned aerial optical photo. Due to the large terrain undulation in the collapse area, the unmanned aerial vehicle adopts full-automatic flight to collect images, which has the risk of crashing. Therefore, the research group adopts manual flight control mode. In order to improve the resolution of DOM image, the relative height of the aircraft to the ground should be controlled at 30-40m, and the image overlap rate in the direction of flight and the direction of side should be 75%-85%. At the same time, in order to quickly realize the monitoring of the ground collapse area 3, the unmanned aerial vehicle flight area is greater than the collapse by about 50m, the purpose is to reduce the unmanned aerial photo, quickly establish the image data of the rescue site, and provide services for the rescue command department. For the processing of unmanned aerial DOM image 2, DJI ZhiTu software is used to import multi-view unmanned aerial photo, the coordinate reference is CGCS2000 (China Geodetic Coordinate System 2000), the local central meridian longitude is selected, and high-resolution digital orthophoto map (DOM) is established. The image resolution is recommended to be less than 5cm / pix (pix represents pixel), so as to view the surface cracking texture area.

[0031] (3) Establishing a high-precision DEM model Since the high-precision digital elevation model (DEM) cannot be directly obtained, it needs to be obtained by processing point cloud data. This embodiment uses DJI unmanned aerial vehicle equipped with lidar. In order to facilitate the viewing of the spatial position relationship between the tunnel and the collapse body, it is necessary to establish the lidar point cloud data from the collapse tunnel entrance to the ground surface in the collapse body area. In order to improve the accuracy of lidar point cloud data acquisition, the echo and true color mode are used, a total of 5 flights are completed, and the lidar point cloud data acquisition is completed. After the data acquisition is completed, the original lidar point cloud data is calculated into three-dimensional point cloud by using DJI ZhiTu, and the point cloud quantity established in the rescue site is 400497588.

[0032] To improve the efficiency of ground monitoring, the point cloud around the collapse body is segmented and retained by the point cloud clipping tool. By marking 30-50 feature points on the point cloud data that express the ground, a point cloud deep learning classification sample is established by the Patchwork++ algorithm. The displayed point cloud is classified as ground points by the algorithm, and the non-displayed part is non-ground points. PointNet++ is suitable for small-scale point cloud classification, directly processes unordered point sets, and extracts global and local features. In addition, DGCNN algorithm, Voxel-Based method and Transformer model can also be used. Dynamic Graph CNN (DGCNN, Dynamic Graph Convolutional Neural Network, a deep learning model based on graph convolutional neural network) captures the topology of point cloud by dynamic graph convolution, which is suitable for complex terrain. Voxel-Based method (such as VoxelNet) is used to voxelize point cloud and use 3D CNN for processing, which is suitable for high-density data. Transformer model (such as Point Transformer) can use self-attention mechanism to handle long-range dependencies, which is suitable for large-scale scenarios. The above deep learning algorithms can output the segmentation results in the classification task, such as distinguishing disaster areas (such as landslide boundary, subsidence area) from stable areas. Since single classification cannot accurately classify all ground points, the classification results can be optimized by adjusting the deep learning iteration parameters and increasing the number of deep learning training samples to improve the classification accuracy of ground points. During iterative training, weighted cross-entropy loss is used to solve the class imbalance problem (such as the scarcity of disaster points). Further, segmented learning rate (initial 0.001, decay every 10 rounds) is used, and AdamW optimizer is used to avoid overfitting. Due to the existence of cliffs around the collapse body, the classification accuracy of the point cloud in the cliff area cannot be directly solved by optimizing the iteration parameters and training samples, and the ground points in the cliff area need to be classified by point cloud segmentation and resampling method, and then combined with the ground point cloud data.

[0033] Further, based on the curvature feature (such as PCA local plane fitting, Principal Component Analysis-based Local Plane Fitting, a principal component analysis algorithm-based local plane fitting), the high-curvature area is segmented, and the cliff can be identified.

[0034] Delaunay triangulation (also called TIN triangulation) method, the ground point cloud data is converted into a triangular network, in order to reduce the number of triangular network, ensure smooth computer operation, need to filter out the triangular network with edge length less than 0.1m, edge length greater than 30m above, at the same time can also improve the rescue three-dimensional scene building speed. Then through the TIN triangulation DEM method, set DEM using interval parameters X=0.2m, Y=0.2m, to establish high-precision digital elevation model (DEM).

[0035] (4) Space three-dimensional terrain model First, satellite image 1 and unmanned aerial vehicle DOM; image fusion processing, using GIS software, through geometric correction to unify the coordinate reference of two images, and then using IHS parameter fusion method to realize the fusion processing of satellite image 1 and unmanned aerial vehicle DOM, wherein, intensity (intensity) is set to 4-8, hue (hue) is set to 4-7, and saturation (saturation) is set to 8-15.

[0036] Second, realize the fusion processing of DEM, adopt sparse linear expression method to realize the seamless fusion between satellite DEM and unmanned aerial vehicle DEM data, wherein, the sparse line value is 25-40.

[0037] Finally, the mapping superposition relationship between the above fused DEM and DOM is established, that is, the three-dimensional terrain model of space-ground joint is obtained.

[0038] After the fusion of digital elevation model (DEM) and digital orthographic image (DOM), the two are combined to build a three-dimensional visual expression of geographic scene through spatial mapping superposition relationship, and the specific relationship is as follows: First step, determine the core relationship of mapping superposition First, align the spatial coordinates, set the plane coordinates as (X, Y): DEM (terrain elevation data) and DOM (ground image data) are strictly aligned through the same plane coordinate system (such as UTM or WGS84), to ensure that each pixel (DOM) and the corresponding elevation point (DEM) are in the same position. Secondly, map the RGB information of DOM to the Z value (elevation value) of DEM to form a 2.5D surface model (i.e. each XY position corresponds to only one Z value). The data coupling mode is shown in Table 1: Table 1 Data coupling mode

[0039] Second step, realization of superposition relationship The realization of superposition relationship specifically includes geometric correction and registration; texture mapping. The specific implementation of geometric correction and registration includes: DOM processing: eliminate image distortion (such as lens distortion of UAV images, terrain displacement) through orthorectification, or use resolution matching (such as 0.1m resolution of DOM needs to be aligned with 0.1m grid of DEM).

[0040] DEM processing: eliminate data holes (such as interpolate to complete the missing area of lidar point cloud data), or smoothing processing (such as Gaussian filter to retain terrain trend and remove noise).

[0041] The mapping of texture specifically includes: binding the RGB value of DOM as texture to the triangular network (TIN) or regular grid of DEM, realizing seamless fitting through UV coordinate mapping.

[0042] Python code as follows: # DEM + DOM overlay based on GIS library import rasterio from pyvista import dem_to_3d dem = rasterio.open("dem.tif").read(1)# read DEM dom = rasterio.open("dom.tif").read([1,2,3])# read DOM (RGB three bands) mesh = dem_to_3d(dem)# convert DEM to 3D grid mesh.textures["dom"] = dom.transpose(1,2,0)# bind DOM texture Further, the resolution needs to be set, DOM resolution ≥ DEM resolution (to avoid texture blur), for example: if DEM is 1m grid, the corresponding DOM needs to be set to a resolution greater than or equal to 0.5m resolution.

[0043] Further, the time should be consistent, the time difference between the two should be as small as possible (such as post-disaster DEM needs to be synchronized with post-disaster DOM). The coordinate system should be unified, ensuring that DEM and DOM use the same projection (such as CGCS2000) and vertical datum (such as EGM96 elevation).

[0044] Two, ground-based SAR and three-dimensional terrain fusion processing (1) Obtain InSAR orbit coordinates Since the foundation InSAR does not have satellite positioning function, the observed data has no coordinate value, so it needs to be converted to the coordinate system of the unmanned aerial vehicle lidar, that is, the CGCS2000 coordinate system, so when flying the terrain data, the InSAR equipment needs to be installed first, and then the flight surveying and mapping work is carried out. On the three-dimensional real scene model of the collapse area, find the position of the InSAR equipment, and use the single point coordinate measurement method to measure the geodetic coordinate values (B, L, H) and the projection coordinate values (X, Y, H) of the left and right two end points of the InSAR track respectively, and save them as txt text.

[0045] (2) Determine the radar deformation azimuth The InSAR original data can obtain the cumulative displacement of each target point at that moment through the differential interference technology. The deformation data at T1 and T2 is processed by difference to obtain the relative deformation data in this period. The radar deformation azimuth of this embodiment is 120°, and by adding the coordinate values of the two ends of the track on the three-dimensional terrain model, the radar deformation azimuth can be calculated.

[0046] Preferably, by adding the coordinate values of the two ends of the track on the three-dimensional terrain model, the geometric observation direction of the radar (i.e. azimuth direction, Azimuth Direction) is essentially related to the geographic coordinate system. The specific steps of calculating the radar deformation azimuth are as follows: First step, prepare the radar track endpoint coordinates. Obtain the two endpoint coordinates of the radar satellite track on the ground surface projection, usually from satellite ephemeris data or SAR image metadata. Use DEM data to construct the terrain surface, and ensure that the coordinate system is consistent with the radar coordinate.

[0047] Second step, calculate the radar azimuth vector.

[0048] Horizontal plane projection vector: ignore the elevation (only use XY coordinates), calculate the horizontal projection vector of the radar flight direction : ; Where, and The two endpoint coordinates of the radar satellite track on the ground surface projection.

[0049] Calculate the horizontal azimuth of the radar flight direction ( ): Calculate the horizontal projection vector and the angle between the north direction (clockwise direction), use the arctangent function to calculate, the formula is: ; Convert the result to angle system (°), the range is 0°~360°, the example is: If the two end point coordinates of the radar satellite orbit projected on the ground surface are M1(100, 200) and M2(150, 180), then: ; ; Third step, calculate the relationship between the deformation azimuth and the radar azimuth.

[0050] Deformation azimuth definition: Deformation azimuth (such as 120°) refers to the angle between the ground deformation direction and north (clockwise direction), which needs to be compared with the radar azimuth (west-north direction) to analyze the deformation sensitivity.

[0051] Radar geometric constraints: Radar is not sensitive to deformation along the azimuth direction (west-north direction) (blind area), and is most sensitive to deformation perpendicular to the azimuth direction (north-south direction).

[0052] Verification example: If =111.8°, then: Radar sensitive direction: 111.8°+90°=201.8° (i.e. southwest-northeast direction). The angle between the user-reported deformation azimuth 120° and the radar sensitive direction (201.8°) is: |120°-201.8°|=81.8°, which is close to 90°, indicating that the deformation direction is close to the radar sensitive direction, and the data is reliable.

[0053] The code for calculating the radar deformation azimuth with python is as follows: import numpy as np # Input orbit endpoint coordinates (x, y, z) P1 = np.array([100, 200, 50])# Start point P2 = np.array([150, 180, 55])# End point # Calculate horizontal azimuth (degrees) dx, dy = P2[0] - P1[0], P2[1] - P1[1] azimuth_deg = np.degrees(np.arctan2(dx, dy)) % 360# Convert to 0~360° print(f"Radar azimuth: {azimuth_deg:.1f}°") (3) InSAR orbit coordinate registration ​​​InSAR radar data is registered to the three-dimensional terrain model using a radar coordinate registration tool to improve the three-dimensional stereo monitoring effect. In the registration tool, 5 files need to be prepared, of which 3 are terrain point cloud files, 1 is an orbit coordinate file, and 1 is an InSAR radar observation file; In the InSAR registration interface, the first item is the point cloud file in the independent coordinate system after cropping, and the point cloud file in the projected coordinate system if there is no independent coordinate system; the second item is the radar orbit coordinate, which must be in the same coordinate system as the first item; the third item is the diffimage graph of the radar; the fourth item is the point cloud file in the CGCS2000 geodetic coordinate system after cropping; the fifth item is the point cloud file in the CGCS2000 projected coordinate system after cropping, which is the same as the first item if there is no independent coordinate system. The InSAR registration graph is shown in Figure 3 , where the red line area circled is the surface collapse area 3, the yellow area larger than the surface collapse area 3 is the InSAR registration area 4, and the red dot in the figure is the InSAR position 5. The area larger than the InSAR registration area 4 is the UAV DEM model 6.

[0054] (4) InSAR and three-dimensional terrain fusion Radar position registration. After radar coordinate registration, select the verification space relationship to check whether the position of the radar coordinate is consistent with the position on the UAV tilt model. If not, re-registration is needed until the radar deformation graph is fused to the three-dimensional terrain model. The second is to fuse the radar deformation graph. Through the radar solving tool, the radar interference file in the DiffImage format is calculated into a deformation graph spot, and it is registered to the DEM model and DOM image.

[0055] (5) Establishing InSAR data observation Using multi-source data fusion, create a monitoring task on the monitoring and early warning platform, add the radar ID to the device management, input the username and password on the login page of the slope radar monitoring and early warning system, click the login button to log in, click the drop-down triangle on the right side of the application field to expand all groups under the application field, click the drop-down triangle in front of the group to expand all companies under the group, click the drop-down triangle in front of the company to expand all projects under the company, click the project name, and the webpage jumps to the three-dimensional scene to display the terrain of the first project under the project. The three-dimensional earth displays the icons of all radars and sensors under the project.

[0056] III. PSInSAR (PS) time series data solving In order to eliminate the influence of the correlation coefficient, amplitude deviation, phase deviation and other factors on the InSAR registration area 4 data, combined with the average correlation coefficient and amplitude deviation method, the correlation coefficient of the adjacent image is calculated based on the average correlation coefficient algorithm, and the correlation coefficient under the set proportion is calculated as the threshold parameter for the final target point selection, which ensures the real-time performance of the ground-based SAR data processing. The main and auxiliary images are formed by transforming the adjacent two images, the number and quality of the PS (Persistent Scatterer) points are optimized, the adaptive threshold of the average correlation coefficient is used, k ground-based SAR complex images are selected to form k interference pairs, and the correlation coefficient of the interference pair is calculated. Finally, the amplitude value of the K ground-based InSAR image is used to calculate the amplitude mean and amplitude standard deviation of each pixel. In this example, a total of 17745 effective PS points are obtained, and the registration rate is 93.89%. In the rescue site, considering the time is relatively tense, when the PS effective point reaches more than 60%, it can be used for on-site observation.

[0057] The PS (Persistent Scatterer) technology can keep the stable scattering characteristics of the ground targets, such as natural stable rock surfaces, in a long time sequence of radar images. The stable backscattering and phase characteristics of the rock in the time sequence make them can be used for high-precision ground deformation monitoring, and achieve millimeter-level precision ground deformation measurement.

[0058] Example 2 The verification process of the air-ground combined InSAR geological disaster monitoring method is as follows: (1) Upload observation period data In this rescue example, the observation station of the ground-based SAR was built at 10:00 on May 5, and the observation data was started at 10:34. The first time sequence radar image was obtained at 10:42. According to the requirements of the rescue command department, the observation station was ended at 23:31 on May 7, and a total of 446 time sequence radar images were obtained. It takes 8 minutes to observe one time sequence of radar data.

[0059] (2) Upload multi-time sequence InSAR data Based on the geological disaster monitoring and early warning platform, the radar ID name is added, the computer is connected with the radar through wifi, the multi-time sequence radar data acquisition is uploaded immediately, the long-yu village geological monitoring project is found in the left directory tree, the radar icon is clicked by using the mouse, the interval time of the radar upload data is set to 8 minutes, which is consistent with the radar observation time.

[0060] (3) InSAR stereo monitoring and early warning From the radar real-time data, 446 observation time series radar data are superimposed at the same time, the whole surface subsidence of the collapse area is calculated by radar multi-track interference phase difference method, in order to intuitively view the subsidence in the whole collapse area, the self-defined color spectrum is used, and the color is usually set to red when the subsidence value is larger. Based on the slope radar real-time monitoring platform, according to the first received ground SAR data, the polygon drawing is selected to monitor the warning area, the subsidence of the surface collapse area 3 is monitored in real time, the warning threshold is set to 5cm / h, that is, when the subsidence value is more than 5cm per 1 hour, the system will be included in the warning, and the warning report is pushed to the rescue command department at the same time. Through the InSAR stereo detection model in the application, the real-time monitoring of geological disasters in the disaster area can obtain a plurality of subsidence values, so as to carry out early warning, and the surface overall subsidence graph and the subsidence amplitude change graph obtained after analysis are as shown in Figure 4 and Figure 5 .

[0061] Figure 4 In the figure, the abscissa represents the subsidence type, the ordinate represents the subsidence value (unit: cm), the black square line represents the maximum subsidence outside the surface collapse, the red square line represents the average subsidence of the surface, the blue square line represents the maximum subsidence inside the collapse body, and the green square line represents the cumulative deformation value of the average subsidence.

[0062] Figure 5 In the figure, the abscissa represents time, and the ordinate represents the subsidence value (unit: cm), Figure 5 The cumulative deformation values of the maximum subsidence outside the surface collapse, the average subsidence of the surface, the maximum subsidence in the collapse pit, and the average subsidence in the collapse pit obtained with the observation time are shown in

[0063] Embodiment 3 An air-ground combined InSAR geological disaster monitoring device, comprising at least one processor, and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the air-ground combined InSAR geological disaster monitoring method of embodiments 1 or 2.

[0064] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A combined air-ground InSAR geological disaster monitoring method, characterized in that: The following steps are involved: Acquire a satellite digital orthophoto of the disaster-stricken area, and match the satellite digital orthophoto with a first digital elevation model; Acquire a first digital orthophoto of a disaster-stricken point in the disaster-stricken area, and match the first digital orthophoto with a second digital elevation model; the first digital orthophoto is acquired by a drone; fusing the satellite digital orthoimage and the first digital orthoimage to obtain a first fused digital orthoimage; fusing the first digital elevation model and the second digital elevation model to obtain a first fused digital elevation model; establishing a mapping relationship between the first fused digital orthoimage and the first fused digital elevation model to obtain an air-ground combined three-dimensional terrain model; The ground-based SAR and the air-ground combined three-dimensional terrain model are fused to obtain an InSAR stereo detection model; and real-time monitoring of geological disasters in disaster areas is performed based on the InSAR stereo detection model.

2. The air-ground combined InSAR geological disaster monitoring method according to claim 1, characterized in that: The fusing of the ground-based SAR and the air-ground combined three-dimensional terrain model specifically comprises the following steps: Get InSAR orbit coordinates; Determine radar deformation azimuth; Based on the InSAR orbit coordinates and the radar deformation azimuth, the InSAR radar data is registered to the air-ground combined three-dimensional terrain model; and an initial InSAR stereo detection model is obtained; InSAR data observation points are established on the initial InSAR stereo detection model to obtain the InSAR stereo detection model.

3. The air-ground combined InSAR geological disaster monitoring method according to claim 2, characterized in that: After obtaining the InSAR stereo detection model, the InSAR observation data obtained from the InSAR data observation points are optimized. The optimization process includes: The correlation coefficient of adjacent images in the InSAR observation data is obtained by combining the average correlation coefficient and the amplitude deviation method. The correlation coefficient under a preset ratio is set as a threshold parameter, and the InSAR data observation point is corrected to determine the final InSAR data observation point.

4. The air-ground combined InSAR geological disaster monitoring method according to claim 2, characterized in that: The steps to obtain InSAR orbit coordinates include: The coordinates of the InSAR equipment at the disaster-stricken points in the disaster area were obtained through drone aerial mapping; Based on the coordinates of the InSAR device, the single-point coordinate measurement method is used to measure the coordinate values ​​between the InSAR track endpoints.

5. The air-ground combined InSAR geological disaster monitoring method according to claim 2, characterized in that: Determining the radar deformation azimuth specifically includes: Obtain InSAR raw data according to InSAR data observation points; The cumulative displacement of each InSAR data observation point in the InSAR raw data is obtained by differential interferometry; the deformation data of each InSAR data observation point is obtained; Perform differential processing on the deformation data obtained at different time points to obtain the relative deformation data within the corresponding time period; The radar deformation azimuth is calculated based on the deviation between the deformation data and the InSAR raw data.

6. The air-ground combined InSAR geological disaster monitoring method according to claim 5, characterized in that: According to the deviation between the deformation data and the InSAR raw data, the radar deformation azimuth is calculated by: Obtain the deformation coordinates of the InSAR track endpoints based on the deformation data; Obtain the original coordinates of the InSAR track endpoints based on the InSAR original data; The coordinates of the InSAR device are connected with the deformed coordinates of the InSAR track endpoint and the original coordinates of the InSAR track endpoint respectively to obtain the radar deformation azimuth.

7. The air-ground combined InSAR geological disaster monitoring method according to claim 2, characterized in that: The registration of InSAR observation data to the air-ground combined three-dimensional terrain model specifically includes: performing registration based on terrain point cloud data, orbital coordinates and InSAR observation data.

8. The air-ground combined InSAR geological disaster monitoring method according to claim 2, characterized in that: Also includes: After the initial InSAR stereo detection model is established, the spatial relationship is verified to check whether the coordinate positions of the InSAR radar observation data are consistent with the positions on the air-ground combined three-dimensional terrain model. If not, re-registration is performed.

9. The air-ground combined InSAR geological disaster monitoring method according to claim 8, characterized in that: Also includes: The radar interferometer file in the DiffImage format is solved into a deformed pattern using the radar solving tool and then registered to the InSAR stereo detection model.

10. The air-ground combined InSAR geological disaster monitoring method according to claim 1, characterized in that: A drone is used to obtain the first digital orthophoto of the disaster-stricken point in the disaster-stricken area. The relative height of the drone and the ground is controlled at 30-40m, the image overlap rate of the heading and lateral directions is 75%-85%, and the drone's flight area is 50m larger than the disaster-stricken point.

11. The air-ground combined InSAR geological disaster monitoring method according to claim 1, characterized in that: The image resolution of the first digital orthophoto is less than 5 cm / pix, where pix represents pixels.

12. The air-ground combined InSAR geological disaster monitoring method according to claim 1, characterized in that: After obtaining the first digital orthophoto of the disaster site, the method further includes converting the obtained laser radar data into three-dimensional point cloud data.

13. The air-ground combined InSAR geological disaster monitoring method according to claim 1, characterized in that: The method for constructing the second digital elevation model includes: obtaining lidar data through a lidar mounted on an unmanned aerial vehicle, calculating a three-dimensional point cloud based on the obtained lidar data, and separating the point clouds of the disaster-stricken points through point cloud clipping.

14. The air-ground combined InSAR geological disaster monitoring method according to claim 13, characterized in that: Through point cloud clipping, the point cloud of the disaster point is separated into the following parts: By establishing deep learning classification samples of point cloud data, the point cloud data is classified and displayed as ground point cloud data through the Patchwork++ algorithm, and the undisplayed part is non-ground point cloud data. By adjusting the deep learning iteration parameters and increasing the deep learning training samples, the Patchwork++ algorithm is optimized to obtain accurate ground point cloud data.

15. An air-ground combined InSAR geological disaster monitoring device, characterized in that: It includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement an air-ground combined InSAR geological disaster monitoring method according to any one of claims 1 to 14.

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