CN106526590A - Method for monitoring and resolving three-dimensional ground surface deformation of industrial and mining area by means of multi-source SAR image - Google Patents

Abstract

The invention discloses a method for monitoring and calculating three-dimensional surface deformation of an industrial and mining area fused with multi-source SAR images. Based on the SAR image data of different orbits before and after deformation, the method obtains the deformation information of different radar satellite line-of-sight upwards by integrating D-InSAR technology and Offset-tracking technology, and obtains the deformation information of different radar satellites in azimuth direction by integrating MAI technology and Offset-tracking technology , on this basis, considering the difference in monitoring accuracy of deformation information in the line of sight and azimuth direction, the weight is determined by means of Helmert variance component estimation, and the three-dimensional surface deformation conversion model is established and solved to obtain the three-dimensional deformation field of the industrial and mining area. The invention can solve the problem that the InSAR technology is easily limited by factors such as loss of coherence and small deformation gradient when monitoring the surface deformation in industrial and mining areas; secondly, the real deformation of the ground surface can be more comprehensively monitored through multi-source SAR images.

Description

A 3D surface deformation monitoring and calculation method for industrial and mining areas fused with multi-source SAR images

technical field

The invention relates to a monitoring and solving method for three-dimensional surface deformation in an industrial and mining area fused with multi-source SAR images.

Background technique

Surface deformation in industrial and mining areas seriously endangers ground construction facilities and the natural environment, and affects human living environment, life and property safety, and local economic development. Comprehensive monitoring of surface deformation in mining areas and in-depth study of deformation formation mechanism and change law are of great significance for rational exploitation of underground mineral resources and control of sustainable development of industrial and mining areas.

The traditional geodetic leveling and GPS monitoring of the surface deformation of the mining area all obtain the deformation information of a point. With the development of surveying and mapping methods and the further expansion of the application range of land subsidence monitoring, its shortcomings are becoming more and more prominent.

Space-borne synthetic aperture radar interferometry (Interferometric Synthetic Aperture Radar, InSAR) technology is a new space-to-earth observation technology developed in recent years. Its principle is to obtain surface deformation information based on the differential interferometric phase of two SAR images before and after deformation, such as D-InSAR, MAI technology. In the application of surface deformation monitoring in mining areas, several successful cases have been obtained at home and abroad, and the monitoring accuracy has reached millimeter level. However, due to the influence of SAR image wavelength, incident angle, ground resolution and other parameters, InSAR technology can only monitor the deformation within a certain range of deformation gradient. Because the surface of industrial and mining areas undergoes different degrees of surface deformation as mining progresses, it may reach the meter level in a short period of time, which seriously exceeds the monitoring capability of conventional InSAR technology based on differential interferometric phase information, resulting in SAR impact loss of coherence. Secondly, InSAR technology can only obtain one-dimensional or two-dimensional surface deformation information, rather than a three-dimensional deformation field that directly and comprehensively reflects the characteristics of surface deformation.

Offset tracking technology (Offset-Tracking) is based on amplitude information, through precise registration of two SAR images, and obtains the offset between the registration points to calculate the deformation without phase unwrapping. The insensitivity of coherence can make up for the shortcomings of the above-mentioned conventional interferometric phase information technology based on factors such as decoherence and small deformation gradients that can be monitored. In addition, more and more in-orbit SAR satellites of different platforms can provide multi-directional deformation monitoring results for the same area, making it easier to implement 3D surface deformation monitoring based on multi-source SAR images. At the same time, monitoring the same area with different orbital SAR images can obtain more comprehensive surface deformation information.

Contents of the invention

The purpose of the present invention is to propose a method for monitoring and solving three-dimensional surface deformation in industrial and mining areas with fusion of multi-source SAR images, to overcome the limitations of traditional InSAR technology due to large gradient deformation, loss of coherence, and only providing surface deformation information in a single direction, which is beneficial to comprehensive It truly reflects the deformation characteristics of the deformation field in industrial and mining areas.

In order to achieve the above object, the present invention adopts the following technical solutions:

A method for monitoring and solving three-dimensional surface deformation in industrial and mining areas fused with multi-source SAR images, comprising the following steps:

a Obtain the coherence map of the multi-source SAR image interference pair;

b smoothing the coherence map;

c Combining the above coherence diagram and integrating D-InSAR technology and Offset-tracking technology to obtain radar line-of-sight deformation information;

d Combining the above coherence diagram and combining MAI technology and Offset-tracking technology to obtain radar azimuth deformation information;

e Utilize the radar line-of-sight deformation information and radar azimuth deformation information obtained in steps c and d, and use Helmert variance component estimation to establish a three-dimensional surface deformation model and solve it.

Preferably, the step a is specifically:

After performing imaging and multi-view processing on the collected multi-source SAR images covering the research area, the rough configuration processing is performed based on the precise orbit data to calculate the initial offset; and then the registration method based on the correlation coefficient is used to fit the offset Polynomial, the polynomial coefficients are calculated under the least squares criterion, and then the precise registration is completed through resampling processing; the respective registered SAR images are conjugated and multiplied to obtain the respective differential interferograms and calculate the SAR image. coherence diagram.

Preferably, the step c is specifically:

c.1 According to the maximum monitorable deformation gradient theory of InSAR technology in practical application, determine the coherence threshold that can be monitored by D-InSAR;

c.2 Using the coherence threshold obtained in step c.1 and the coherence map in step b, use D-InSAR technology to monitor surface deformation in the area of the study area where the coherence value is greater than the threshold in step c.1 to obtain radar line-of-sight deformation information;

c.3 Use Offset-tracking technology to process the area in the study area where the coherence value is less than the threshold in c.1, and obtain the deformation information in the range direction, and then convert it into the radar line-of-sight deformation information;

c.4 Fuse the radar line-of-sight deformation information obtained in step c.2 and step c.3 respectively according to the pixel position, so as to obtain the radar line-of-sight deformation information of the entire research area;

c.5 Repeat the above step c.1-step c.4, so as to process the SAR image data of different orbits separately, and realize the monitoring of deformation information in different radar line-of-sight directions.

Preferably, the determination process of the D-InSAR monitorable coherence threshold in step c.1 is as follows:

Theoretically, the calculation formula of the maximum deformation gradient model that can be monitored is as follows:

Among them, d _max is the maximum deformation gradient, λ is the wavelength, and u is the pixel size;

The relationship between the maximum deformation gradient and coherence that can be actually monitored: D _max ＝d _max +0.002(γ-1);

Among them, D _max is the actual maximum deformation gradient that can be monitored, and γ is the coherence value;

It can be seen from the formula that as the coherence value decreases, there is a small coherence value that makes D _max become 0, that is, no deformation information can be monitored; based on this, the critical coherence of the maximum deformation gradient that can be monitored by each sensor is calculated value as a threshold.

Preferably, the step d is specifically:

d.1 According to the relationship between the monitoring accuracy and coherence of MAI technology and Offset-tracking technology, select the critical coherence value when the monitoring accuracy of the two technologies changes as the coherence threshold;

d.2 According to the coherence threshold determined in step d.1 and the coherence map in step b, use MAI technology to monitor the azimuth deformation of the surface in the study area where the coherence value is greater than the threshold value in d.1 to obtain the radar azimuth deformation information;

d.3 Use Offset-tracking technology to process the area with coherence value less than the threshold in d.1 in the research area, and obtain the radar azimuth deformation information of the remaining area in the research area;

d.4 Fuse the radar azimuth deformation information obtained in step d.2 and step d.3 respectively, so as to obtain the radar azimuth direction deformation information of the entire research area;

d.5 Repeat the above step d.1-step d.4, so as to process the SAR image data of different orbits, and realize the monitoring of deformation information in different radar azimuths.

Preferably, the coherence threshold in step d.1 is 0.8.

Preferably, the step e is specifically:

After obtaining the multi-source SAR image radar line-of-sight deformation information and radar azimuth deformation information, the monitoring results of different resolutions are resampled to the same resolution; the method of Helmert variance component estimation is used for iterative processing pixel by pixel to calculate the The weight of the element is used to establish a three-dimensional surface deformation calculation model and perform calculation.

Preferably, the process of establishing a three-dimensional surface deformation calculation model in the step e and performing the calculation is as follows:

The three-dimensional surface deformation calculation model is expressed as: R=Bd;

Among them, d=(d _u , de _e , d _n ) ^T represents the three-dimensional surface deformation information, d _u represents the deformation information in the vertical direction, d _e represents the deformation information in the east-west direction, and d _n represents the deformation information in the north-south direction; Indicates the deformation information in different directions in the radar coordinate system, Indicates the line-of-sight deformation information of track 1, Indicates the line-of-sight deformation information of track 2, Indicates the azimuth deformation information of track 1, Indicates the azimuth deformation information of track 2; B is the three-dimensional deformation model coefficient matrix;

According to the schematic diagram of SAR satellite earth observation principle, it is expressed as:

Among them, α ₁ represents the azimuth angle of the track 1 radar, α ₂ represents the azimuth angle of the track 2 radar, θ ₁ represents the incident angle of the track 1 radar, and θ ₂ represents the incident angle of the track 2 radar;

According to the principle of least squares, the three-dimensional deformation information of the surface is calculated: d=(B ^T PB) ^-1 B ^T PR;

Among them, P is the weight matrix corresponding to two different orbit line-of-sight and azimuth deformation variables; according to the Helmert variance component estimation formula, the relationship between the variance component and the residual error of the observation value is expressed as: Sθ=W _θ ;

In the formula, Indicates the unit weight error of two types of observations; and W _θ and S are respectively:

In the formula, N _i ＝B _i ^T P _i B _i , i=1, 2; P ₁ and P ₂ represent the weight matrix of the two types of observations respectively; tr represents the matrix trace operation; V ₁ and V ₂ represent the two types of observations respectively the correction number;

Given the initial weight matrix P, estimate θ according to the Helmert variance component estimation formula after the first adjustment, and then recalculate the weight according to the following formula:

in, and represent the valuations of the two types of observation weight matrix respectively; let Iterative calculation is performed again until Re-weighting, and then calculate the three-dimensional deformation information.

The present invention has the following advantages:

The method of the invention monitors the surface deformation of the industrial and mining area based on multi-source SAR image data, and can more comprehensively and truly obtain the temporal and spatial characteristics of the surface deformation of the industrial and mining area. According to the characteristics of surface deformation in industrial and mining areas, the limitations of factors such as decoherence and large deformation gradient can be effectively solved by integrating multiple InSAR technologies, which broadens the application prospects of InSAR technology in large-scale surface deformation monitoring in industrial and mining areas, and reduces the cost and cost of deformation monitoring. technical limitations. On this basis, by establishing a three-dimensional deformation calculation model, it is possible to more comprehensively and effectively obtain three-dimensional surface deformation information in industrial and mining areas, understand and grasp the surface deformation in the study area more directly, and effectively prevent geological disasters. At the same time, it also provides more direct basic data for the study of surface deformation mechanism and deformation law, which is of great significance to the sustainable mining of mining areas.

Description of drawings

Fig. 1 is a flow chart of a fusion multi-source SAR image industrial and mining area three-dimensional surface deformation monitoring and solution method in the present invention;

Fig. 2 is the schematic diagram of the line-of-sight deformation monitoring results of the ascending orbit ALOS PALSAR data processed by D-InSAR technology in the present invention;

Fig. 3 is the schematic diagram of the line-of-sight deformation monitoring results of the ascending orbit ALOSPALSAR data processed by the fusion of D-InSAR technology and Offseet-tracking technology in the present invention;

Fig. 4 is the schematic diagram of the line-of-sight deformation monitoring result of the descending orbit ENVISAT ASAR data processed by D-InSAR technology in the present invention;

5 is a schematic diagram of the line-of-sight deformation monitoring results of the descending orbit ENVISATASAR data processed by the fusion of D-InSAR technology and Offseet-tracking technology in the present invention;

Fig. 6 is a schematic diagram of the azimuth deformation monitoring results of the ascending orbit ALOS PALSAR data processed by MAI technology in the present invention;

Fig. 7 is a schematic diagram of the azimuth deformation monitoring results of the ascending orbit ALOS PALSAR data processed by the fusion of MAI technology and Offseet-tracking technology in the present invention;

Fig. 8 is a schematic diagram of the azimuth deformation monitoring results of the descending orbit ENVISAT ASAR data processed by MAI technology in the present invention;

Fig. 9 is a schematic diagram of the azimuth deformation monitoring results of the descending orbit ENVISATASAR data after the fusion of MAI technology and Offseet-tracking technology in the present invention;

Fig. 10 is a schematic diagram of the vertical direction result of three-dimensional surface deformation monitoring in the present invention;

Fig. 11 is a schematic diagram of the east-west direction results of three-dimensional surface deformation monitoring in the present invention;

Fig. 12 is a schematic diagram of the north-south direction results of three-dimensional surface deformation monitoring in the present invention;

Fig. 13 is a schematic diagram of the principle of SAR satellite earth observation in the present invention.

detailed description

Basic principle of the present invention is:

By using a variety of InSAR technologies to process multi-source SAR images covering the same research area, the multiple InSAR technologies are fused to obtain multi-directional surface deformation information in the area, and the multi-source monitoring results are further fused to solve the three-dimensional surface deformation.

Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

As shown in Figure 1, a method for monitoring and calculating 3D surface deformation in industrial and mining areas fused with multi-source SAR images includes the following steps:

a Obtain the coherence map of the interferometric pair of multi-source SAR images. The specific method of obtaining is:

After performing imaging and multi-view processing on the collected multi-source SAR images covering the research area, the rough configuration processing is performed based on the precise orbit data to calculate the initial offset; and then the registration method based on the correlation coefficient is used to fit the offset Polynomial, the polynomial coefficients are calculated under the least squares criterion, and then the precise registration is completed through resampling processing; the respective registered SAR images are conjugated and multiplied to obtain the respective differential interferograms and calculate the SAR image. coherence diagram.

Among them, the coherence map refers to the basis for evaluating the similarity of two SAR images, which is generated in the differential interference processing.

b Smoothing of the coherence map.

The coherence map is the basis of subsequent data processing, but in the actual processing process, due to the influence of various noises and other factors, the spatial continuity of the coherence value of each pixel in the obtained coherence map is poor, that is, the change of the coherence value between adjacent pixels is small. It is impossible to determine the continuous range that can be processed by different InSAR technologies in the subsequent processing process based on this. Therefore, the coherence map whose coherence values are spatially discrete is smoothed before subsequent processing.

In order to ensure the authenticity of the coherence map as much as possible during the processing, the Kriging interpolation method can be used.

c Integrate D-InSAR technology and Offset-tracking technology to obtain radar line-of-sight deformation information.

In order to obtain high-precision surface deformation monitoring results in industrial and mining areas, and in view of the phenomenon that the surface deformation in industrial and mining areas is prone to decoherence, the surface deformation monitoring of the whole basin is realized by integrating D-InSAR technology and Offset-tracking technology.

The step c is specifically:

c.1 According to the maximum deformation gradient theory that can be monitored by InSAR technology in practical application, determine the coherence threshold that can be monitored by D-InSAR.

Surface deformation in industrial and mining areas has the characteristics of small impact range and large deformation level, and is prone to large gradient deformation. In the differential interferogram, the fringes are relatively dense, which leads to unwrapping errors and cannot obtain reliable deformation information; and the deformation gradient is too large. It is easy to cause decoherence in the deformation zone and reduce the accuracy of monitoring results.

The theory of the maximum detectable deformation gradient of InSAR technology was first proposed by Massonnett and Feigl, and a theoretical model was given. On this basis, Baran et al., considering the influence of coherence and other factors, found that the maximum monitorable deformation gradient value of InSAR in practical applications is smaller than the theoretical value, and gave the maximum monitorable deformation gradient model of InSAR technology in practical applications.

The determination process of the D-InSAR monitorable coherence threshold in step c.1 of the present invention is as follows:

Massonnett and Feigl proposed in 1998 that the calculation formula of the theoretically monitorable maximum deformation gradient model is as follows:

Among them, d _max is the maximum deformation gradient, λ is the wavelength, and u is the pixel size.

However, due to the influence of decoherence and other factors in the actual application process, the theoretical model of the maximum deformation gradient that can be monitored is smaller than the theoretical one. Considering the influence of coherence and other factors, the actual maximum monitorable deformation gradient is smaller than the theoretical value. In 2005, Baran et al. gave the relationship between the actual monitorable maximum deformation gradient and coherence:

D _max =d _max +0.002(γ-1); wherein, D _max is the actual maximum monitorable deformation gradient, and γ is the coherence value.

It can be seen from the formula that as the coherence value decreases, there is a small coherence value that makes D _max become 0, that is, no deformation information can be monitored; based on this, the critical coherence of the maximum deformation gradient that can be monitored by each sensor is calculated value as a threshold.

Among them, D-InSAR technology can be used for surface deformation monitoring in areas where the coherence value is greater than the threshold value, and the Offset-tracking technology can be used for surface deformation monitoring in areas where the coherence value is less than the threshold value.

c.2 Using the coherence threshold obtained in step c.1 and the coherence map in step b, use D-InSAR technology to monitor surface deformation in the area of the study area where the coherence value is greater than the threshold in step c.1 to obtain radar line-of-sight deformation information.

c.3 Use Offset-tracking technology to process the areas in the study area whose coherence value is less than the threshold in c.1, and obtain the deformation information in the range direction and then convert it into deformation information in the radar line-of-sight direction.

The Offset_tracking technology calculates the registration offset based on the amplitude information of the two scenes, and further converts the registration offset into deformation information, and calculates the azimuth deformation on this basis.

The biggest feature of this technology is that it is not sensitive to coherence, and deformation information can be obtained without phase unwrapping. It can effectively overcome the application limitations of D-InSAR technology, and can provide deformation details for areas with severe loss of correlation caused by large deformation gradients.

Figure 2 and Figure 4 respectively show the line-of-sight deformation monitoring results obtained by using D-InSAR technology for two sets of different SAR images of ALOS and ASAR, and the blank area in the figure is the decoherence area. For this area, the Offset-tracking technology that does not require coherence is used for processing. Figure 3 and Figure 5 are the final monitoring results after the fusion of the two technologies. By comparing Figure 2 and Figure 3 and Figure 4 and Figure 5, we can find the effect comparison Obviously, the monitoring of surface deformation in the low coherence area is realized. In addition, from Figures 2 to 5, it can be found that different orbital SAR images observe the surface from different angles of view, and obtain different monitoring results including deformation range and deformation distribution, so that more comprehensive surface deformation information can be obtained.

c.4 Fuse the radar line-of-sight deformation information obtained in step c.2 and step c.3 respectively according to the pixel position, so as to obtain the radar line-of-sight deformation information of the entire research area;

c.5 In order to obtain the three-dimensional surface deformation information in the study area, deformation information in at least three different directions is required. Repeat the above step c.1-step c.4, so as to process the SAR image data of different orbits separately, realize the monitoring of deformation information in different radar line-of-sight directions, and obtain more comprehensive and real surface deformation information.

d Combining MAI technology and Offset-tracking technology to obtain radar azimuth deformation information.

MAI technology is an earth observation technology based on interferometric phase information. Different from InSAR technology, this technology uses split beam InSAR processing algorithm to obtain higher precision azimuth deformation:

Firstly, two pairs of front and rear master-slave images are generated by splitting two single-view complex images (SLC). Conjugate multiplication of the backsight interferogram and the backsight interferogram produces a multi-aperture differential interferogram containing azimuth deformation information. After filtering, phase unwrapping and other steps, the azimuth deformation is obtained. Similar to the mechanism of conventional InSAR monitoring deformation, the coherence of SAR images is the main factor restricting the application of this technology, and the quality of coherence directly affects the accuracy of MAI measurement of azimuth deformation. Compared with the offset-tracking technology based on amplitude information, the monitoring accuracy of the high-coherence area is high, while the monitoring accuracy of the low-coherence area is low.

Image coherence has a great influence on the accuracy of MAI technology, and the surface of industrial and mining areas is generally covered with crops, and the coherence is poor. In addition, large-scale surface deformation in a short period of time deepens image decoherence. In order to obtain the azimuth deformation information, the MAI technology and the Offset-tracking technology which is not sensitive to coherence are used to monitor the surface deformation.

Offset-tracking technology calculates the offset with an accuracy of 1/50 pixel, which is equivalent to 7.5cm in azimuth and 14cm in line of sight for SAR data.

MAI phase standard deviation can be expressed as

As the coherence increases, the calculated phase standard deviation becomes smaller and smaller. Related literature shows that when the coherence value is greater than 0.8, the standard deviation of MAI is better than that of Offset-tracking technology. Therefore, the method of the present invention uses MAI technology to process the area with a coherence value greater than 0.8 in the study area, and uses Offset-tracking technology to process the area with a coherence value less than 0.8 in the study area. Finally, the two monitoring results are fused to achieve full tracking. Basin surface deformation monitoring.

The step d is specifically:

d.1 According to the relationship between the monitoring accuracy and coherence of MAI technology and Offset-tracking technology, the critical coherence value when the monitoring accuracy of the two technologies changes is selected as the coherence threshold, and its value is 0.8.

d.2 According to the coherence threshold determined in step d.1 and the coherence map in step b, use MAI technology to monitor the azimuth deformation of the surface in the study area where the coherence value is greater than the threshold value in d.1 to obtain the radar azimuth deformation information.

d.3 Use Offset-tracking technology to process the areas where the coherence value is less than the threshold in d.1 in the study area, and obtain the radar azimuth deformation information of the remaining areas in the study area.

d.4 Fuse the radar azimuth deformation information obtained in step d.2 and step d.3 respectively, so as to obtain the radar azimuth direction deformation information of the entire research area.

Figure 6 and Figure 8 respectively show the azimuthal deformation monitoring results obtained by MAI technology for two different SAR images of ALOS and ASAR, and the blank area in the figure is the decoherence area. In the decoherent area, Offset-tracking technology is used to obtain deformation information. Figure 7 and Figure 9 are the final monitoring results after combining the two technologies. By comparing Figures 6 and 7 and Figures 8 and 9, it can be found that the effect is more obvious. By combining the two techniques, the deformation information including the azimuth direction of the low coherence area in the center of the subsidence basin is obtained, and the monitoring of surface deformation in the low coherence area is realized.

d.5 Repeat the above step d.1-step d.4, so as to process the SAR image data of different orbits, realize the monitoring of deformation information in different radar azimuth directions, and obtain more comprehensive and real surface deformation information.

So far, through the fusion of multiple InSAR technologies to process multi-source SAR images, deformation information in four different directions including two different line-of-sight directions and two different azimuth directions in industrial and mining areas has been obtained.

e After obtaining the line-of-sight and azimuth deformation information of multi-source SAR images (at least two different orbits), resample the monitoring results with different resolutions to the same resolution.

Considering the difference in the accuracy of monitoring results obtained by different technologies, the monitoring results are divided into two categories according to the different acquisition methods, and the method of Helmert variance component estimation is used to iteratively process pixel by pixel, and the weight of each pixel is calculated to establish the three-dimensional surface deformation Solve the model and perform the evaluation.

Fig. 10, Fig. 11 and Fig. 12 are the vertical, east-west and north-south direction results of the research on three-dimensional surface deformation monitoring respectively.

Among them, the Helmert variance component estimation is to use the weighted square sum of various correction numbers after each adjustment to estimate the unit weight variance of various observations, and then realize the determination of the weight of various observations according to the accuracy.

This step e is specifically: the three-dimensional surface deformation calculation model is expressed as: R=Bd;

Among them, d=(d _u , de _e , d _n ) ^T represents the three-dimensional surface deformation information, d _u represents the deformation information in the vertical direction, d _e represents the deformation information in the east-west direction, and d _n represents the deformation information in the north-south direction; Indicates the deformation information in different directions in the radar coordinate system, Indicates the line-of-sight deformation information of track 1, Indicates the line-of-sight deformation information of track 2, Indicates the azimuth deformation information of track 1, Indicates the azimuth deformation information of track 2; B is the three-dimensional deformation model coefficient matrix;

As shown in Figure 13, the schematic diagram of the SAR satellite’s earth observation principle can be expressed as:

Among them, α ₁ represents the azimuth angle of the track 1 radar, α ₂ represents the azimuth angle of the track 2 radar, θ ₁ represents the incident angle of the track 1 radar, and θ ₂ represents the incident angle of the track 2 radar;

According to the principle of least squares, the three-dimensional deformation information of the surface is calculated: d=(B ^T PB) ^-1 B ^T PR;

Among them, P is the weight matrix corresponding to two different orbit line-of-sight and azimuth deformation variables; according to the Helmert variance component estimation formula, the relationship between the variance component and the residual error of the observation value is expressed as: Sθ=W _θ ;

In the formula, Indicates the unit weight error of two types of observations; and W _θ and S are respectively:

In the formula, N _i ＝B _i ^T P _i B _i , i=1, 2; P ₁ and P ₂ represent the weight matrix of the two types of observations respectively; tr represents the matrix trace operation; V ₁ and V ₂ represent the two types of observations respectively the correction number;

Given the initial weight matrix P, estimate θ according to the Helmert variance component estimation formula after the first adjustment, and then recalculate the weight according to the following formula:

in, and represent the valuations of the two types of observation weight matrix respectively; let Iterative calculation is performed again until Re-weighting, and then calculate the three-dimensional deformation information.

Of course, the above descriptions are only preferred embodiments of the present invention, and the present invention is not limited to the above-mentioned embodiments. It should be noted that all equivalent substitutions made by any person skilled in the art under the teaching of this specification , obvious deformation forms, all fall within the essential scope of this specification, and should be protected by the present invention.

Claims (8)

1. A method for monitoring and calculating the deformation of a three-dimensional earth surface of a mining area fused with multi-source SAR images is characterized by comprising the following steps:

a, acquiring a coherence map of a multi-source SAR image interference pair;

b, smoothing the coherent graph;

c, combining the coherent graph and fusing a D-InSAR technology and an Offset-tracking technology to acquire radar sight distortion information;

d, combining the coherent graph and fusing an MAI technology and an Offset-tracking technology to acquire radar azimuth deformation information;

and e, using the radar sight direction deformation information and the radar direction deformation information obtained in the step c and the step d, adopting Helmert variance component estimation to establish a three-dimensional earth surface deformation model and calculating.

2. The method for monitoring and calculating the deformation of the three-dimensional earth surface of the mining area fused with the multi-source SAR images according to claim 1, wherein the step a specifically comprises the following steps:

imaging and multi-view processing are carried out on the collected multi-source SAR images covering the research area, coarse configuration processing is carried out on the basis of precise orbit data, and initial offset is calculated; fitting an offset polynomial by adopting a registration method based on a correlation coefficient, calculating a polynomial coefficient under a least square criterion, and then completing precision registration through resampling treatment; and respectively carrying out conjugate multiplication on the SAR images after respective registration to obtain respective differential interference phase diagrams and simultaneously calculate a coherence map of the SAR images.

3. The method for monitoring and calculating the deformation of the three-dimensional earth surface of the mining area fused with the multi-source SAR images according to claim 1, wherein the step c specifically comprises the following steps:

c.1, determining a D-InSAR (interferometric synthetic aperture radar) monitorable coherence threshold according to the maximum monitorable deformation gradient theory of the InSAR technology in practical application;

c.2, utilizing the coherence threshold obtained in the step c.1 and the coherence map in the step b, carrying out surface deformation monitoring on the region of which the coherence value in the research region is greater than the threshold in the step c.1 by adopting a D-InSAR technology to obtain radar sight line deformation information;

c.3, processing the area with the coherence value smaller than the threshold value in the c.1 in the research area by using an Offset-tracking technology, acquiring distance deformation information and then further converting the distance deformation information into radar sight deformation information;

c.4, fusing the radar sight line deformation information obtained in the step c.2 and the radar sight line deformation information obtained in the step c.3 according to the positions of the pixels, so as to obtain the radar sight line deformation information towards the ground surface of the whole research area;

and c.5, repeating the steps c.1-c.4, so as to respectively process the SAR image data of different tracks and realize the monitoring of deformation information of different radar sight lines.

4. The method for monitoring and calculating the three-dimensional surface deformation of the mining area fused with the multi-source SAR images according to claim 3, wherein the determination process of the D-InSAR monitorable coherence threshold in the step c.1 is as follows:

the theoretical formula for monitoring the maximum deformation gradient model is as follows:

wherein d is_maxMaximum deformation gradient, wherein lambda is wavelength and u is pixel size;

the maximum deformation gradient versus coherence can be monitored in practice: d_max＝d_max+0.002(γ-1)；

Wherein D is_maxThe maximum deformation gradient can be monitored actually, and gamma is a coherent value;

as can be seen from the formula, as the coherence value decreases, there is a smaller coherence value such that D_maxChanging to 0, namely monitoring deformation information; and calculating the critical coherence value of the maximum deformation gradient which can be actually monitored by each sensor as a threshold value on the basis of the above calculation.

5. The method for monitoring and calculating the deformation of the three-dimensional earth surface of the mining area fused with the multi-source SAR images according to claim 1, wherein the step d specifically comprises the following steps:

d.1, selecting a critical coherence value as a coherence threshold value when the monitoring precision of the two technologies changes according to the relationship between the monitoring precision and the coherence of the MAI technology and the Offset-tracking technology;

d.2, according to the coherence threshold determined in the step d.1 and the coherence map in the step b, carrying out surface orientation deformation monitoring on the region of which the coherence value in the research region is greater than the threshold in the step d.1 by adopting an MAI (moving average information) technology to obtain radar orientation deformation information;

d.3, processing the region with the coherence value smaller than the threshold value in d.1 in the research region by using an Offset-tracking technology to obtain radar azimuth deformation information of the rest region in the research region;

d.4, fusing the radar azimuth deformation information respectively obtained in the step d.2 and the step d.3, so as to obtain radar azimuth earth surface deformation information of the whole research area;

d.5, repeating the steps d.1-d.4, thereby processing the SAR image data of different tracks and realizing the monitoring of deformation information of different radar azimuth directions.

6. The method for monitoring and calculating the three-dimensional surface deformation of the multi-source SAR image fused mining area according to claim 5, wherein the coherence threshold in the step d.1 is 0.8.

7. The method for monitoring and calculating the deformation of the three-dimensional earth surface of the mining area fused with the multi-source SAR images according to claim 1, wherein the step e specifically comprises the following steps:

after multi-source SAR image radar sight direction deformation information and radar azimuth direction deformation information are obtained, monitoring results with different resolutions are resampled to the same resolution; iterative processing is carried out pixel by adopting a Helmert variance component estimation method, the weight of each pixel is calculated, and a three-dimensional earth surface deformation calculation model is established and calculated.

8. The method for monitoring and calculating the three-dimensional surface deformation of the multi-source SAR image mining area according to claim 7, wherein the process of establishing the three-dimensional surface deformation calculation model and calculating in the step e is as follows:

the three-dimensional surface deformation resolving model is expressed as: r ═ Bd;

wherein d ═ d (d)_u,d_e,d_n)^TRepresenting three-dimensional surface deformation information, d_uIndicating deformation information in the vertical direction, d_eIndicating east-west direction deformation information, d_nRepresenting deformation information in the north-south direction;representing deformation information in different directions in a radar coordinate system,indicating the track 1 line-of-sight distortion information,representing the track 2 line-of-sight distortion information,indicating the azimuthal deformation information of the track 1,indicating track 2 azimuth deformation information; b is a three-dimensional deformation model coefficient matrix;

according to the SAR satellite earth observation principle, the schematic diagram is shown as follows:

$B=\left[\begin{array}{ccc}{\mathrm{cos\θ}}_1& -\cos ({\mathrm{\α}}_1-\frac{3\mathrm{\π}}{2}){\mathrm{sin\θ}}_1& -\sin ({\mathrm{\α}}_1-\frac{3\mathrm{\π}}{2}){\mathrm{sin\θ}}_1\\ {\mathrm{cos\θ}}_2& -\cos ({\mathrm{\α}}_2-\frac{3\mathrm{\π}}{2}){\mathrm{sin\θ}}_2& -\sin ({\mathrm{\α}}_2-\frac{3\mathrm{\π}}{2}){\mathrm{sin\θ}}_2\\ 0& \sin ({\mathrm{\α}}_1-\frac{3\mathrm{\π}}{2})& -\cos ({\mathrm{\α}}_1-\frac{3\mathrm{\π}}{2})\\ 0& \sin ({\mathrm{\α}}_2-\frac{3\mathrm{\π}}{2})& -\cos ({\mathrm{\α}}_2-\frac{3\mathrm{\π}}{2})\end{array}\right];$

wherein, α₁Indicating track 1 radar azimuth, α₂Representing track 2 radar azimuth, θ₁Representing the radar incident angle, theta, of track 1₂Represents the track 2 radar incident angle;

and (3) solving the three-dimensional deformation information of the earth surface according to the least square principle: d ═ B^TPB)^-1B^TPR；

Wherein, P is a weight matrix corresponding to two different track sight lines and azimuth deformation; according to the Helmert variance component estimation formula, expressing the relation between the variance component and the observation value residual as follows: s theta ═ W_θ；

In the formula,error in unit weight representing two types of observations; and W_θAnd S are respectively:

$W_{\mathrm{\θ}}=\left[\begin{array}{cc}{V_1}^T{P}_1{V}_1& {V_2}^T{P}_2{V}_2\end{array}\right];S=\left[\begin{array}{cc}2-2tr\left(N_1{N}^{-1}\right)+tr{\left(N_1{N}^{-1}\right)}^2& tr\left(N_1{N}^{-1}{N}_2{N}^{-1}\right)\\ tr\left(N_2{N}^{-1}{N}_1{N}^{-1}\right)& 2-2tr\left(N_2{N}^{-1}\right)+tr{\left(N_2{N}^{-1}\right)}^2\end{array}\right];$

in the formula,N_i＝B_i ^TP_iB_i,i＝1,2；P₁and P₂Weight arrays respectively representing the two types of observed quantities; tr represents a matrix trace-solving operation; v₁And V₂Respectively representing the correction numbers of the two types of observed quantities;

giving an initial weight matrix P, estimating theta according to a Helmert variance component estimation formula after the first averaging, and then recalculating the weight according to the following formula:

wherein,andrespectively representing the evaluation values of the two types of observation quantity weight arrays; order toThe iterative calculation is carried out again untilRe-weight, and thenAnd calculating three-dimensional deformation information.