CN116973917A - Slope hidden danger identification method based on multi-source information fusion - Google Patents
Slope hidden danger identification method based on multi-source information fusion Download PDFInfo
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Abstract
A slope hidden danger identification method based on multi-source information fusion is characterized in that images of lifting rail SAR images under the same research area are fused, a lifting rail deformation phase image library is constructed after registration, interference and unwrapping are carried out, a multi-source auxiliary information library is constructed by combining multi-source external data including a monitoring area DEM, gradient, slope direction, satellite flight direction angle, incidence angle and the like, and finally deformation resolving of landslide monitoring points is realized through singular value decomposition.
Description
Technical Field
The invention relates to a slope hidden danger identification method based on multisource information fusion, and belongs to the technical field of InSAR landslide monitoring.
Background
Landslide refers to a natural phenomenon that soil, stone piles and the like on a mountain slope slowly creep or suddenly change under the action of external force. Along with the continuous expansion of human activities, the ecological environment is greatly influenced, landslide phenomenon and secondary disasters possibly brought by the landslide phenomenon occur more and more frequently, such as landslide in Sichuan county, white grid landslide and the like, so that the production activities of human beings are greatly influenced, and great potential threat is caused to the production and life safety of human beings. Therefore, how to qualitatively and quantitatively monitor, identify and predict landslide hazard points becomes an important task for many geology researchers.
At present, common landslide deformation monitoring technologies are mainly divided into two main categories: contact deformation monitoring and non-contact deformation monitoring. However, the conventional contact and non-contact earth surface deformation monitoring technologies have a small monitoring range and severe monitoring conditions, and the earth surface deformation data is impractical to acquire on a large area scale by using the technologies. Compared with the conventional single-point or small-range landslide deformation monitoring technology, the synthetic aperture radar interferometry technology has the characteristics of wide monitoring range, high spatial resolution, good measurement accuracy, full-day time and all-weather monitoring and the like, is not influenced by weather conditions, provides effective means for regional ground surface deformation monitoring, landslide identification and evaluation, and adopts the principle that regional ground surface deformation information is extracted by utilizing the relationship between radar wave phase difference and spatial distance in two times of observation of the synthetic aperture radar so as to realize the identification of landslide hidden danger points.
However, since the synthetic aperture radar satellite is side view imaging, when imaging different observation areas, image distortion of different degrees is often caused by fluctuation of topography, and a simple track lifting image or a track lowering image cannot be fully covered in an area with large topography fluctuation, the InSAR technology has a monitoring blind area in landslide deformation monitoring of a complex area due to the problems, and large monitoring errors are easily generated to influence the identification and evaluation of hidden slope points.
Disclosure of Invention
The invention solves the technical problems that: aiming at the condition that monitoring errors are easy to generate in the complex area by the conventional InSAR technology in the prior art, the slope hidden danger identification method based on multi-source information fusion is provided.
The invention solves the technical problems by the following technical proposal:
a slope hidden danger identification method based on multi-source information fusion comprises the following steps:
determining a side slope to be identified, wherein hidden danger risk identification is to be carried out on the side slope to be identified;
determining an AOI research area according to the slope position to be identified, and acquiring an ascending SAR image and a descending SAR image corresponding to each time point in a preset time sequence of the AOI research area;
cutting all the ascending SAR images and all the descending SAR images according to the geographic range of the AOI research area, and inputting the cut images into a deformation phase image library;
acquiring relevant auxiliary information of an AOI research area, constructing a multisource auxiliary information base, and constructing a monitorable model by using the relevant auxiliary information;
performing differential interference on all cut track lifting SAR images and track descending SAR images, performing format conversion on the processed images, registering and inputting the processed images into a deformation phase image library;
calculating deformation image data of the slope to be identified according to the information in the deformation phase image library and the multisource auxiliary information library;
calculating the horizontal deformation rate v in each pixel according to the obtained deformation image data h Vertical deformation rate v v Obtaining a deformation grid chart, performing spatial superposition on the deformation grid chart and a monitorable index grid image output by a monitorable model, and performing threshold segmentation on the superposed pixel values to obtain the deformation rate and the accumulated sedimentation value of the slope to be identified; v according to deformed raster pattern h 、v v The deformation value is used for carrying out delineation and identification of hidden danger points, and determining the slope to be identifiedAll hidden trouble point positions.
The method for determining the AOI research area according to the slope position to be identified comprises the following steps:
the slope to be identified is adjacent to a road or a river channel, the steepness of the slope is within a preset range, and the surface soil and stone stacking body is loose, namely the density of the stone stacking body is smaller than the preset range.
The related auxiliary information comprises DEM image data, gradient parameter information, slope parameter information, satellite imaging angle, satellite platform flight angle, orbit ascending information parameter and orbit descending information parameter, wherein the satellite imaging angle, the satellite platform flight angle, the orbit ascending information parameter and the orbit descending information parameter are obtained by reading all orbit ascending SAR images and head files of the orbit descending SAR images, and the method comprises the steps of:
if the longitude and latitude coordinates of four vertex pixels of the ascending SAR image and the descending SAR image are located in the geographic range of the AOI research area, cutting the ascending SAR image and the descending SAR image, reading the vertex coordinates of the cut image of each pixel after finishing cutting, and selecting DEM image data of the corresponding AOI research area according to the vertex coordinates;
the gradient parameter information of each pixel is obtained according to the DEM image data;
and calculating and acquiring the slope parameter information of each pixel according to the slope parameter information of each pixel and the DEM image data.
The construction method of the monitorable model comprises the following steps:
constructing a distortion model D according to the DEM image data, the satellite imaging angle and the satellite platform flight angle;
constructing a sensitive model S according to the DEM image data, the gradient parameter information and the slope parameter information;
and carrying out DEM pixel space superposition of the distortion model D and the sensitive model S to obtain a monitorable model.
The monitorability model is:
V＝D*S
the distortion model D is determined by shadow distortion and overlay mask distortion of the ascending SAR image and the descending SAR image:
D＝Shadow*Layover
the sensitivity model S takes the difference or the ratio of the pixel size in the ascending SAR image and the descending SAR image to the corresponding pixel size under the real ground distance as input quantity, and the ground distance pixel compression degree as output quantity:
S lifting device ＝sin[θ-α*sin(σ+Az+90+180)]
S Lowering blood pressure ＝sin[θ-α*sin(σ-Az-90)]
Wherein S is Lifting device Is a sensitive model of the ascending track SAR image, S Lowering blood pressure For the sensitive model of the down-orbit SAR image, θ is the incident angle, α is the gradient image calculated according to the DEM image data, σ is the gradient image calculated according to the DEM image data, and Az is the flight direction of the satellite platform.
The specific steps of carrying out image differential interference and format conversion, inputting the deformation phase image library and then carrying out registration are as follows:
performing coherence computation on the ascending SAR image and the descending SAR image at the same time point, and selecting an optimal ascending SAR image and an optimal descending SAR image as reference images according to computation results;
determining geographic homonymy points for all the elevated SAR images according to the elevated reference images, and calculating a main image reference center point (r) according to the geodetic longitude and latitude coordinates of the reference image center point 1 ,c 1 ) And calculates the instantaneous position of the auxiliary image when imaging the center point of the reference image, and the pixel coordinate (r) of the center point of the reference image in the auxiliary image according to the oblique distance equation, the Doppler equation and the ellipsoid equation 2 ,c 2 )；
Based on the instantaneous position and pixel coordinates (r) of the secondary image when imaged with the reference image center point 2 ,c 2 ) Calculating the offset of the reference image, and calculating the coordinate offset of the geographic homonymy point in the row-column direction;
resampling is carried out on each auxiliary image by utilizing the coordinate offset, so as to finish coarse registration; after the rough registration of the images, the homonymous point selection is carried out again according to a rough registration method, and the homonymous point selection of global distribution is carried out in the main image and the auxiliary image, so that the fine registration of the main image and the auxiliary image is realized;
extracting post-alignment referencesComplex conjugate multiplication is carried out on the pixels with corresponding row numbers in the images and the auxiliary images, and phase difference components between the images are extracted
Removing the terrain phase and the land leveling phase in the extracted phase components and extracting the deformation phase components, namely specific components, in the extracted phase components;
and re-selecting any pixel with the coherence higher than a preset value from the specific components, taking the pixel as a reference point to perform unwrapping treatment of the specific phase components, and inputting the obtained deformation phase image into a deformation phase image library.
The method for determining the geographic homonymy point comprises the following steps:
selecting a reference image center point as a reference point, selecting geographic homonymous points of the reference image and the auxiliary image to be registered by utilizing satellite orbit parameters of the reference image and the image to be registered, and using the geographic homonymous points as rough registration control points for calculating pixel coordinates (r 2 ,c 2 ) The calculation method comprises the following steps:
R＝|P-S|
wherein R is the slant distance from the satellite to be registered to the ground point P, and P= (X) P ,Y P ,Z P ) Is the position vector of the ground point P, s= (X) S ,Y S ,Z S ) For the position vector when the phase center of the image satellite radar to be registered images the P point, V= (V) XS ,V YS ,V ZS ) Is a speed vector, f is Doppler frequency, a is an ellipsoidal long half shaft, b is an ellipsoidal short half shaft, and h is a geodetic height; calculating the auxiliary image to image at the center point of the reference image according to the oblique distance equation, the Doppler equation and the ellipsoid equationAnd calculates the pixel coordinates (r) of the center point in the acquired reference image in the auxiliary image 2 ,c 2 )。
The method for calculating the coordinate offset Off comprises the following steps:
the resampling is specifically as follows:
the coordinate offset is subtracted from each pixel of each secondary image.
The deformation image data calculation method of the side slope to be identified comprises the following steps:
according to the phase value of the deformation phase image as a deformation measurement value, calculating deformation image data of the slope to be identified, including track lifting image dataDerailment image data->Wherein:
wherein Δt is 1 、Δt 2 For the imaging time interval of the differential interference image of the lifting rail, deltat 3 、Δt 4 To derail the time interval of the differential interference image,for the horizontal deformation component of the corresponding time interval, +.>Is a vertical deformation component corresponding to the time interval.
v h 、v v The calculation method of (1) is as follows:
wherein v is h 、v v The horizontal deformation rate and the vertical deformation rate are respectively,in order to obtain the image data of the track lifting,for the derailment image data, λ is the super parameter set by the experiment, λ=0.1 to 1.
All pixels with pixel values larger than 0 in the distortion index image output by the distortion model are assigned to be 1, threshold segmentation of the monitorable model is carried out after the combination of the sensitive model, the threshold is sin theta, theta is the radar satellite incidence angle, geographic superposition is carried out on the monitorable model and the solved deformation grid, and pixels smaller than sin theta in the deformation grid are removed, so that the deformation rate and the accumulated sedimentation value of the slope S to be identified are finally obtained.
And (3) carrying out the delineation and identification of hidden danger points on the accumulated sedimentation value, setting the deformation rate threshold value to be +/-30 mm/y, delineating pixels exceeding the threshold value, regarding deformed pixels with a relatively close distance as a deformed cluster, traversing all pixels of the side slope S to be identified, and determining the positions of all hidden danger points to finish hidden danger identification.
Compared with the prior art, the invention has the advantages that:
according to the slope hidden danger identification method based on multi-source information fusion, the images of the lifting orbit SAR images in the same research area can be fused, the lifting orbit deformation phase image library is constructed after registration, interference and unwrapping processing, the multi-source auxiliary information library is constructed by combining multi-source external data including a monitoring area DEM, gradient, slope direction, satellite flight direction angle, incidence angle and the like, and finally, the deformation resolving of landslide monitoring points is realized through singular value decomposition, so that the slope hidden danger identification method based on the time sequence InSAR technology is suitable for slope hidden danger identification and monitoring in complex areas, the influence of different monitoring precision of the topography and satellite lifting orbit sight is removed to the greatest extent, the problems that the influence of the radar sight is difficult to invert and the precision is low in the to-be-landslide monitoring are solved, and the hidden danger identification of slope monitoring in complex mountain areas can be realized more accurately.
Drawings
FIG. 1 is a schematic flow chart of an InSAR landslide monitoring method based on multi-source information assistance;
FIG. 2 is a flow chart of differential interferometric image calculation provided by the invention;
Detailed Description
A slope hidden danger identification method based on multi-source information fusion is characterized in that images of lifting rail SAR images in the same research area are fused, a lifting rail deformation phase image library is constructed after registration, interference and unwrapping are carried out, a multi-source auxiliary information library is constructed by combining multi-source external data including a monitoring area DEM, gradient, slope direction, satellite flight direction angle, incidence angle and the like, and finally deformation resolving of landslide monitoring points is realized through singular value decomposition.
The slope hidden danger identification method specifically comprises the following steps:
determining a side slope to be identified, wherein hidden danger risk identification is to be carried out on the side slope to be identified;
determining an AOI research area according to the slope position to be identified, and acquiring an ascending SAR image and a descending SAR image corresponding to each time point in a preset time sequence of the AOI research area;
cutting all the ascending SAR images and all the descending SAR images according to the geographic range of the AOI research area, and inputting the cut images into a deformation phase image library;
acquiring relevant auxiliary information of an AOI research area, constructing a multisource auxiliary information base, and constructing a monitorable model by using the relevant auxiliary information; the monitorable model comprises a monitoring distortion model and a sensitive model, wherein the input quantity of the monitoring distortion model is an AOI research area DEM, a satellite flight azimuth angle and an incident angle, and the input quantity of the sensitive model is an AOI research area DEM, a gradient, a slope direction, a satellite flight azimuth angle and an incident angle;
performing differential interference on all the ascending SAR images and the descending SAR images, performing format conversion on the processed images, registering and inputting the images into a deformation phase image library;
calculating deformation image data of the slope to be identified according to the information in the deformation phase image library and the multisource auxiliary information library;
calculating the horizontal deformation rate v in each pixel according to the obtained deformation influence data h And vertical deformation rate v v Obtaining a deformation grid chart by utilizing a singular value decomposition method (Singular Value Decomposition, SVD), superposing the deformation grid chart with a monitorable index grid image output by a monitorable model, and carrying out threshold segmentation on the superposed pixel values to obtain the deformation rate and the accumulated sedimentation value of the slope to be identified; for v h 、v v And carrying out delineation and identification on hidden danger points by the deformation values, and determining the positions of all hidden danger points of the side slope to be identified.
Specifically, the method for determining the AOI research area according to the slope position to be identified comprises the following steps: the slope is adjacent to a road or river channel, the slope body is steeper, and the surface soil and stone accumulation body is loose. And the slope range is moderately expanded by 100 m-500 m as an AOI research area according to research requirements.
The relevant auxiliary information comprises DEM image data, gradient parameter information, slope parameter information, satellite imaging angle, satellite platform flight angle, orbit ascending information parameter and orbit descending information parameter, wherein the satellite imaging angle, the satellite platform flight angle, the orbit ascending information parameter and the orbit descending information parameter are obtained by reading all orbit ascending SAR images and head files of the orbit descending SAR images;
if the longitude and latitude coordinates of four vertex pixels of the ascending SAR image and the descending SAR image are located in the geographic range of the AOI research area, cutting the ascending SAR image and the descending SAR image, reading the vertex coordinates of the cut image of each pixel after finishing cutting, and selecting DEM image data of the corresponding AOI research area according to the vertex coordinates;
the gradient parameter information of each pixel is obtained according to the DEM image data;
and calculating and acquiring the slope parameter information of each pixel according to the slope parameter information of each pixel and the DEM image data.
The construction method of the monitorable model comprises the following steps:
constructing a distortion model D according to the DEM image data, the satellite imaging angle and the satellite platform flight angle;
constructing a sensitive model S according to the DEM image data, the gradient parameter information and the slope parameter information;
and carrying out DEM pixel space superposition of the distortion model D and the sensitive model S to obtain a monitorable model.
The monitorability model is:
V＝D*S
the distortion model D is determined by shadow distortion and overlay mask distortion of the ascending SAR image and the descending SAR image:
D＝Shadow*Layover
the sensitivity model S takes the difference or the ratio of the pixel size in the ascending SAR image and the descending SAR image to the corresponding pixel size under the real ground distance as input quantity, and the ground distance pixel compression degree as output quantity:
S lifting device ＝sin[θ-α*sin(σ+Az+90+180)]
S Lowering blood pressure ＝sin[θ-α*sin(σ-Az-90)]
Wherein S is Lifting device Is a sensitive model of the ascending track SAR image, S Lowering blood pressure For the sensitive model of the down-orbit SAR image, θ is the incident angle, α is the gradient image calculated according to the DEM image data, σ is the gradient image calculated according to the DEM image data, and Az is the flight direction of the satellite platform.
The specific steps of carrying out image differential interference and format conversion, inputting the deformation phase image library and then carrying out registration are as follows:
performing coherence computation on the ascending SAR image and the descending SAR image at the same time point, and selecting an optimal ascending SAR image and an optimal descending SAR image as reference images according to computation results;
determining geographic homonymy points for all the elevated SAR images according to the elevated reference images, and calculating a main image according to the geodetic longitude and latitude coordinates of the central point of the reference imageReference centre point (r) 1 ,c 1 ) And calculates the instantaneous position of the auxiliary image when imaging the center point of the reference image, and the pixel coordinate (r) of the center point of the reference image in the auxiliary image according to the oblique distance equation, the Doppler equation and the ellipsoid equation 2 ,c 2 )；
Based on the instantaneous position and pixel coordinates (r) of the secondary image when imaged with the reference image center point 2 ,c 2 ) Calculating the offset of the reference image, and calculating the coordinate offset of the geographic homonymy point in the row-column direction;
resampling is carried out on each auxiliary image by utilizing the coordinate offset, so as to finish coarse registration; and after the rough registration of the images, carrying out homonymous point selection again according to a rough registration method, and carrying out globally distributed homonymous point selection in the main image and the auxiliary image, thereby finishing the fine registration of the main image and the auxiliary image.
Extracting the phase difference component between the precisely aligned reference image and the corresponding row and column number pixels in the auxiliary image to perform complex conjugate multiplication
Removing the terrain phase and the land phase in the extracted phase components and extracting deformation phase components (specific components) in the extracted phase components;
and re-selecting any pixel with the coherence higher than a preset value from the specific components, taking the pixel as a reference point to perform unwrapping treatment of the specific phase components, and inputting the obtained deformation phase image into a deformation phase image library.
The method for determining the geographic homonymy point comprises the following steps:
selecting a reference image center point as a reference point, selecting geographic homonymous points of the reference image and the auxiliary image to be registered by utilizing satellite orbit parameters of the reference image and the image to be registered, and using the geographic homonymous points as rough registration control points for calculating pixel coordinates (r 2 ,c 2 ) The calculation method comprises the following steps:
R＝|P-S|
wherein R is the slant distance from the satellite to be registered to the ground point P, and P= (X) P ,Y P ,Z P ) Is the position vector of the ground point P, s= (X) S ,Y S ,Z S ) For the position vector when the phase center of the image satellite radar to be registered images the P point, V= (V) XS ,V YS ,V ZS ) Is a speed vector, f is Doppler frequency, a is an ellipsoidal long half shaft, b is an ellipsoidal short half shaft, and h is a geodetic height; calculating the instantaneous position of the auxiliary image in the imaging of the center point of the reference image according to the oblique distance equation, the Doppler equation and the ellipsoidal equation, and calculating and obtaining the pixel coordinate (r) of the center point in the reference image in the auxiliary image 2 ,c 2 )。
The method for calculating the coordinate offset Off is as follows:
the resampling is specifically as follows:
the coordinate offset is subtracted from each pixel of each secondary image to complete the coarse alignment.
The deformation image data calculation method of the side slope to be identified comprises the following steps:
according to the phase value of the deformation phase image as a deformation measurement value, calculating deformation image data of the slope to be identified, including track lifting image dataDerailment image data->Wherein:
wherein Δt is 1 、Δt 2 For the imaging time interval of the differential interference image of the lifting rail, deltat 3 、Δt 4 A time interval for the derailment differential interferometric image;for the horizontal deformation component of the corresponding time interval, +.>Is a vertical deformation component corresponding to the time interval.
v h 、v v The calculation method of (1) is as follows:
wherein v is h 、v v The horizontal deformation rate and the vertical deformation rate are respectively,in order to obtain the image data of the track lifting,for the derailment image data, λ is the super parameter set for the experiment, typically λ=0.1 to 1.
All pixels with pixel values larger than 0 in the distortion index grid image output by the distortion model are assigned to be 1, threshold segmentation of the monitorable model is carried out after the pixels are combined through the sensitive model, the threshold is sin theta, theta is the radar satellite incidence angle, geographic superposition is carried out on the monitorable model and the solved deformation grid, pixels smaller than sin theta in the deformation grid are removed, and finally the deformation rate and the accumulated sedimentation value of the slope S to be identified are obtained.
And (3) carrying out the delineation and identification of hidden danger points on the accumulated sedimentation value, setting the deformation rate threshold value to be +/-30 mm/y, delineating pixels exceeding the threshold value, regarding deformed pixels with a relatively close distance as a deformed cluster, traversing all pixels of the side slope S to be identified, and determining the positions of all hidden danger points to finish hidden danger identification.
The following further description of the preferred embodiments is provided in connection with the accompanying drawings of the specification:
in the current embodiment, the multisource information-assisted InSAR landslide monitoring method, as shown in fig. 1, comprises the following steps:
step 1, determining a slope S needing hidden danger risk identification;
step 2, acquiring a lifting rail SAR image of the region;
specifically, the ESA can download a time sequence SAR image covering the research area, such as 20 scene Sentinel1 data;
step 3, AOI cutting is carried out on the obtained radar image according to the geographical range of the research area, the cut SAR image is input into an image library, and step 4-7 is executed to judge whether the landslide monitoring area S has risk hidden danger points or not, specifically comprising the following steps:
step 4, acquiring relevant auxiliary information data of an AOI region, including DEM, satellite imaging angle, satellite platform flight angle, lifting orbit information parameters and the like, and constructing a multisource auxiliary information base;
specifically, to build the multisource auxiliary information base, the image header file needs to be read and relevant parameters need to be extracted first to determine the relevant range of the research area AOI, the relevant range of the DEM and the like.
The step 4 is specifically as follows:
step 4.1, preparation of the AOI investigation region DEM
Reading a header file and extracting related parameter information of the downloaded lifting rail image, judging whether an AOI (automated optical inspection) of a research area is in an image coverage area or not by reading longitude and latitude coordinates of four vertex pixels of the image, and if the AOI is in the image coverage area, performing cutting work; then, reading the AOI vertex coordinates, and downloading the DEM covering the AOI area, such as SRTM30m, according to the vertex coordinates;
and 4.2, calculating the gradient and slope parameter information of the AOI of the research area according to the acquired SRTM30m DEM, and obtaining gradient parameters of each pixel in the image:
wherein θ is the gradient;
and 4.3, calculating the gradient and slope parameter information of the research area AOI according to the acquired SRTM30m DEM, and obtaining the slope parameter of each pixel in the image:
wherein, A is the slope direction,for the rate of change of a picture element in the x-direction +.>The change rate of a certain pixel in the y direction is given;
step 4.4, obtaining parameter information;
after determining the AOI research area, further reading the header file, and extracting parameters such as a head Angle, an incoden Angle, orbit Configuration and the like in the header file;
step 4.5, constructing a monitoring performance model Visibility (V) of the radar sight line;
in particular, in SAR two-dimensional imaging, due to the side-view imaging defect of the sensor, multiple scatterers often image in the same pixel, resulting in image distortion such as shading, overlay masking, and perspective shrinkage. In the subsequent deformation inversion process, the value of the distorted pixel often affects the accuracy and the overall robustness of deformation, so that a monitorable model of a radar view line is required to be constructed to reject the distorted pixel;
the method mainly comprises the steps of integrating a DEM, a gradient, a slope direction, an incident angle and a satellite platform flight angle to construct a radar vision line monitoring model; the method comprises the steps of constructing a monitoring Distortion model (D) of a radar sight based on DEM information, an incident angle and a satellite platform flight angle, and eliminating image Distortion pixels in subsequent radar images; constructing a sensitivity model (S) of radar vision based on DEM information, gradient, slope direction, etc.; and performing spatial superposition under DEM pixels on the distortion model and the sensitive model to finish a monitoring model Visibility (V) of a radar view line:
V＝D*S
the method comprises the following specific steps: distortion model (display, D)
Assuming the satellite position is the sun position, calculating SAR image shadow distortion according to the satellite azimuth angle and the line of sight incidence angle:
Shadow＝255
*[(cos(Az)*cos(90-θ)+sin(Az)*sin(90-θ)
*cos(Az-(90-θ))]
az is the satellite flying azimuth angle, and theta is the satellite sight line incident angle;
performing binarization classification on the Shadow:
and (5) calculating the overlay mask distortion of the SAR image in the same way:
Layover＝255
*[(cos(Az+180)*cos(θ)+sin(Az+180)*sin(θ)
*cos(Az+180-θ)]
multiplying the obtained overlay Distortion and shadow Distortion to calculate a Distortion model (D) in the SAR image:
D＝Shadow*Layover
the method comprises the following specific steps: sensitive model (sensitivity S)
The sensitivity model refers to the difference or ratio of the pixel size in the SAR image to the corresponding pixel size under the real ground distance, and represents the compression degree of the ground distance pixels in the SAR image.
The specific calculation mode is as follows:
S lifting device ＝sin[θ-α*sin(σ+Az+90+180)]
S Lowering blood pressure ＝sin[θ-α*sin(σ-Az-90)]
Wherein S is Lifting device For sensitivity model of track lifting image S Lowering blood pressure A sensitivity model for the derailment image; θ is the incident angle of the image, α is the gradient image calculated from the DEM image, σ is the slope image calculated from the DEM image, az is the flight direction of the satellite platform (positive in north clockwise direction).
The distortion model information and the sensitivity model information of the lifting rail SAR image can be respectively obtained through the calculation, the spatial superposition of the two models finally forms a monitorable model visual (V) of the radar image visual line, and the distortion model image, the sensitivity model image and the finally calculated monitorable model image are finally added into a multi-source auxiliary information base.
And step 5, preparing a differential interference pattern according to the acquired track lifting image. Performing binary file format conversion on all cut track lifting radar images, inputting the converted images into a track lifting image information base, and performing coarse registration and fine registration;
the method is mainly used for registering SAR images and preparing data of differential interference images.
Specifically, firstly, carrying out coherence computation between every two of the track lifting/track descending images, taking the optimal image as a reference image, and carrying out image registration, interference processing, extraction of interested phase components and phase unwrapping with other auxiliary images.
Step 5.1, performing rough registration on all the format-converted track-lifting images, wherein the registration method adopts a method of automatically selecting control points, firstly selecting a center point of a reference image as a reference point, and then solving geographic homonymous points in the reference image and a corresponding auxiliary image by utilizing satellite orbit parameters of the reference image and the image to be registered, inSAR geometry and SAR positioning equation, wherein the found homonymous points are used as the control points for rough registration of the images;
the following formula is adopted:
R＝|P-S|
wherein R is the slant distance from the satellite to be registered to the ground point P, and P= (X) P ,Y P ,Z P ) Is the position vector of the ground point P, s= (X) S ,Y S ,Z S ) For the position vector when the phase center of the image satellite radar to be registered images the P point, V= (V) XS ,V YS ,V ZS ) The speed vector f is Doppler frequency, a is an ellipsoid long half shaft, b is an ellipsoid short half shaft, and h is geodetic height. Calculating the instantaneous position of the auxiliary image in the imaging of the center point of the main image according to the oblique distance equation, the Doppler equation and the ellipsoidal equation, and calculating and obtaining the pixel coordinate (r) of the center point in the reference main image in the auxiliary image 2 ,c 2 )；
Step 5.2, calculating the reference center point (r) of the main image according to the longitude and latitude coordinates of the center point of the reference main image 1 ,c 1 ) Space rectangular coordinates (X, Y, Z);
step 5.3, calculating the instantaneous position of the auxiliary image when the main image center point is imaged according to the oblique distance equation, the Doppler equation and the ellipsoidal equation;
step 5.4, according to the calculated instantaneous position and the pixel coordinates (r 2 ,c 2 ) Calculating the offset of the image;
and 5.5, calculating the coordinate offset of the pixels with the same name point in the row and column directions according to the coordinates of the two pixels, wherein the calculation process comprises the following steps:
step 5.6, resampling operation of the auxiliary image is carried out on the obtained offset, namely the obtained offset is subtracted from each pixel of the auxiliary image;
step 5.7, after the rough registration of the images, the homonymy point selection is carried out again according to the method, and the homonymy point selection of global distribution is carried out in the main image and the auxiliary image;
step 5.8, after coarse registration and fine registration, the pixel row and column numbers of the main and auxiliary images are consistent, and complex conjugate multiplication is performed on each corresponding pixel in the images to extract phase difference components between the two images
Step 5.9, extracting interesting components from the extracted phase components, namely removing other contribution phases such as land leveling phases, topography phases, possible atmosphere delay phases and the like;
step 5.10, after the interested deformation phase component is extracted, selecting a certain pixel with higher coherence in the phase image, adopting an SNAPHU method to carry out relative recovery of a phase period by taking the pixel as a reference point, realizing solving of deformation phase, and finally adding the obtained deformation phase image into a deformation phase image library; as shown in fig. 2;
step 6, establishing a landslide deformation extraction method based on multi-time constraint according to images in the deformation phase image library, incidence angles in the multi-source auxiliary information library, satellite platform flight angles and the like;
specifically, a plurality of differential interference images obtained by solving are used as deformation measurement values, and deformation solving of the monitored landslide is carried out.
According to the provided differential interference image, the imaging time of the track lifting image and the track lowering image is uniformly distributed, and the track lifting image isThe derailment image is->(1 represents the earliest imaging time and 4 represents the latest imaging time), then the deformation solution equationThe method comprises the following steps:
it should be noted that the imaging time interval of the differential interference image of the lifting rail is Δt 1 、Δt 2 The time interval of the derailment differential interference image is deltat 3 、Δt 4 ；
Step 7, adopting a singular value decomposition method to carry out pixel-by-pixel v on the images in the deformation image library h 、v v Solving and optimizing landslide deformation precision in continuous imaging time; and performing geographic superposition on the solved deformation grid graph and a monitorable model, removing deformation pixels smaller than a threshold value in the monitorable model, performing time-series sedimentation analysis on points with larger deformation rate in the deformation graph after removing, and marking the points as landslide hidden danger points if the change rule of linear deformation is met and the change rule exceeds a set deformation danger threshold value.
Specifically, solving according to the formula in the step 6, adding regularization parameters of the continuous imaging time as follows, and optimizing the result of the continuous imaging time for the first solving according to the regularization parameters;
the step and the monitorable model solved in the step 4 are subjected to geographic superposition; reclassifying the distortion model obtained in the step 4, assigning all pixels with pixel values larger than 0 (non-overlap mask and non-shadow) as 1, calculating a sensitivity model, and performing threshold segmentation on the solved monitorable model (the monitorable model value range is [ -1,1 ]), wherein the threshold is often set as sin theta (theta is the radar satellite incidence angle); finally, carrying out geographic superposition on the monitorable model and the solved deformed grid, removing pixels smaller than sin theta in the deformed grid, and finally obtaining the deformation rate and the accumulated sedimentation value of the landslide S;
and (3) carrying out delineation and identification of hidden danger points on the obtained deformation values, setting a deformation rate threshold value to be +/-30 mm/y, delineating pixels exceeding the threshold value, and regarding deformed pixels with a relatively close distance as a deformation cluster (the distance threshold value can be set to be 100 m), so as to finally realize risk point location monitoring and identification of landslide S.
Although the present invention has been described in terms of the preferred embodiments, it is not intended to be limited to the embodiments, and any person skilled in the art can make any possible variations and modifications to the technical solution of the present invention by using the methods and technical matters disclosed above without departing from the spirit and scope of the present invention, so any simple modifications, equivalent variations and modifications to the embodiments described above according to the technical matters of the present invention are within the scope of the technical matters of the present invention.
What is not described in detail in the present specification belongs to the known technology of those skilled in the art.
Claims (13)
1. A slope hidden danger identification method based on multi-source information fusion is characterized by comprising the following steps:
determining a side slope to be identified, wherein hidden danger risk identification is to be carried out on the side slope to be identified;
determining an AOI research area according to the slope position to be identified, and acquiring an ascending SAR image and a descending SAR image corresponding to each time point in a preset time sequence of the AOI research area;
cutting all the ascending SAR images and all the descending SAR images according to the geographic range of the AOI research area, and inputting the cut images into a deformation phase image library;
acquiring relevant auxiliary information of an AOI research area, constructing a multisource auxiliary information base, and constructing a monitorable model by using the relevant auxiliary information;
performing differential interference on all cut track lifting SAR images and track descending SAR images, performing format conversion on the processed images, registering and inputting the processed images into a deformation phase image library;
calculating deformation image data of the slope to be identified according to the information in the deformation phase image library and the multisource auxiliary information library;
calculating the horizontal deformation rate v in each pixel according to the obtained deformation image data h Vertical deformation rate v v Obtaining a deformation grid chart, performing spatial superposition on the deformation grid chart and a monitorable index grid image output by a monitorable model, and performing threshold segmentation on the superposed pixel values to obtain the deformation rate and the accumulated sedimentation value of the slope to be identified; v according to deformed raster pattern h 、v v And carrying out delineation and identification on hidden danger points by the deformation values, and determining the positions of all hidden danger points of the side slope to be identified.
2. The slope hidden danger identification method based on multi-source information fusion according to claim 1, wherein the method is characterized in that:
the method for determining the AOI research area according to the slope position to be identified comprises the following steps:
the slope to be identified is adjacent to a road or a river channel, the steepness of the slope is within a preset range, and the surface soil and stone stacking body is loose, namely the density of the stone stacking body is smaller than the preset range.
3. The slope hidden danger identification method based on multi-source information fusion according to claim 1, wherein the method is characterized in that:
the related auxiliary information comprises DEM image data, gradient parameter information, slope parameter information, satellite imaging angle, satellite platform flight angle, orbit ascending information parameter and orbit descending information parameter, wherein the satellite imaging angle, the satellite platform flight angle, the orbit ascending information parameter and the orbit descending information parameter are obtained by reading all orbit ascending SAR images and head files of the orbit descending SAR images, and the method comprises the steps of:
if the longitude and latitude coordinates of four vertex pixels of the ascending SAR image and the descending SAR image are located in the geographic range of the AOI research area, cutting the ascending SAR image and the descending SAR image, reading the vertex coordinates of the cut image of each pixel after finishing cutting, and selecting DEM image data of the corresponding AOI research area according to the vertex coordinates;
the gradient parameter information of each pixel is obtained according to the DEM image data;
and calculating and acquiring the slope parameter information of each pixel according to the slope parameter information of each pixel and the DEM image data.
4. The slope hidden danger identification method based on multi-source information fusion according to claim 1, wherein the method is characterized in that:
the construction method of the monitorable model comprises the following steps:
constructing a distortion model D according to the DEM image data, the satellite imaging angle and the satellite platform flight angle;
constructing a sensitive model S according to the DEM image data, the gradient parameter information and the slope parameter information;
and carrying out DEM pixel space superposition of the distortion model D and the sensitive model S to obtain a monitorable model.
5. The slope hidden danger identification method based on multi-source information fusion according to claim 4, wherein the method is characterized in that:
the monitorability model is:
V＝D*S
the distortion model D is determined by shadow distortion and overlay mask distortion of the ascending SAR image and the descending SAR image:
D＝Shadow*Layover
the sensitivity model S takes the difference or the ratio of the pixel size in the ascending SAR image and the descending SAR image to the corresponding pixel size under the real ground distance as input quantity, and the ground distance pixel compression degree as output quantity:
S lifting device ＝sin[θ-α*sin(σ+Az+90+180)]
S Lowering blood pressure ＝sin[θ-α*sin(σ-Az-90)]
Wherein S is Lifting device Is a sensitive model of the ascending track SAR image, S Lowering blood pressure For the sensitive model of the down-orbit SAR image, θ is the incident angle, α is the gradient image calculated according to the DEM image data, ψ is the gradient image calculated according to the DEM image data, and Az is the flight direction of the satellite platform.
6. The slope hidden danger identification method based on multi-source information fusion according to claim 1, wherein the method is characterized in that:
the specific steps of carrying out image differential interference and format conversion, inputting the deformation phase image library and then carrying out registration are as follows:
performing coherence computation on the ascending SAR image and the descending SAR image at the same time point, and selecting an optimal ascending SAR image and an optimal descending SAR image as reference images according to computation results;
determining geographic homonymy points for all the elevated SAR images according to the elevated reference images, and calculating a main image reference center point (r) according to the geodetic longitude and latitude coordinates of the reference image center point 1 ,c 1 ) And calculates the instantaneous position of the auxiliary image when imaging the center point of the reference image, and the pixel coordinate (r) of the center point of the reference image in the auxiliary image according to the oblique distance equation, the Doppler equation and the ellipsoid equation 2 ,c 2 )；
Based on the instantaneous position and pixel coordinates (r) of the secondary image when imaged with the reference image center point 2 ,c 2 ) Calculating the offset of the reference image, and calculating the coordinate offset of the geographic homonymy point in the row-column direction;
resampling is carried out on each auxiliary image by utilizing the coordinate offset, so as to finish coarse registration; after the rough registration of the images, the homonymous point selection is carried out again according to a rough registration method, and the homonymous point selection of global distribution is carried out in the main image and the auxiliary image, so that the fine registration of the main image and the auxiliary image is realized;
extracting the phase difference component between the precisely aligned reference image and the corresponding row and column number pixels in the auxiliary image to perform complex conjugate multiplication
Removing the terrain phase and the land leveling phase in the extracted phase components and extracting the deformation phase components, namely specific components, in the extracted phase components;
and re-selecting any pixel with the coherence higher than a preset value from the specific components, taking the pixel as a reference point to perform unwrapping treatment of the specific phase components, and inputting the obtained deformation phase image into a deformation phase image library.
7. The slope hidden danger identification method based on multi-source information fusion according to claim 6, wherein the method is characterized in that:
the method for determining the geographic homonymy point comprises the following steps:
selecting a reference image center point as a reference point, selecting geographic homonymous points of the reference image and the auxiliary image to be registered by utilizing satellite orbit parameters of the reference image and the image to be registered, and using the geographic homonymous points as rough registration control points for calculating pixel coordinates (r 2 ,c 2 ) The calculation method comprises the following steps:
R＝|P-S|
wherein R is the slant distance from the satellite to be registered to the ground point P, and P= (X) P ,Y P ,Z P ) Is the position vector of the ground point P, s= (X) S ,Y S ,Z S ) For the position vector when the phase center of the image satellite radar to be registered images the P point, V= (V) XS ,V YS ,V ZS ) Is a speed vector, f is Doppler frequency, a is an ellipsoidal long half shaft, b is an ellipsoidal short half shaft, and h is a geodetic height; calculating the instantaneous position of the auxiliary image in the imaging of the center point of the reference image according to the oblique distance equation, the Doppler equation and the ellipsoidal equation, and calculating and obtaining the pixel coordinate (r) of the center point in the reference image in the auxiliary image 2 ,c 2 )。
8. The slope hidden danger identification method based on multi-source information fusion according to claim 7, wherein the method is characterized in that:
the method for calculating the coordinate offset Off comprises the following steps:
9. the slope hidden danger identification method based on multi-source information fusion according to claim 7, wherein the method is characterized in that:
the resampling is specifically as follows:
the coordinate offset is subtracted from each pixel of each secondary image.
10. The slope hidden danger identification method based on multi-source information fusion according to claim 7, wherein the method is characterized in that:
the deformation image data calculation method of the side slope to be identified comprises the following steps:
according to the phase value of the deformation phase image as a deformation measurement value, calculating deformation image data of the slope to be identified, including track lifting image dataDerailment image data->Wherein:
wherein Δt is 1 、Δt 2 For the imaging time interval of the differential interference image of the lifting rail, deltat 3 、Δt 4 To derail the time interval of the differential interference image,for the horizontal deformation component of the corresponding time interval, +.>For a corresponding time intervalVertical deformation component of the separator.
11. The slope hidden danger identification method based on multi-source information fusion according to claim 10, wherein the method is characterized in that:
v h 、v v the calculation method of (1) is as follows:
wherein v is h 、v v The horizontal deformation rate and the vertical deformation rate are respectively,for the track-lifting image data, < >>For the derailment image data, λ is the super parameter set by the experiment, λ=0.1 to 1.
12. The slope hidden danger identification method based on multi-source information fusion according to claim 11, wherein the method is characterized in that:
all pixels with pixel values larger than 0 in the distortion index image output by the distortion model are assigned to be 1, threshold segmentation of the monitorable model is carried out after the combination of the sensitive model, the threshold is sin theta, theta is the radar satellite incidence angle, geographic superposition is carried out on the monitorable model and the solved deformation grid, and pixels smaller than sin theta in the deformation grid are removed, so that the deformation rate and the accumulated sedimentation value of the slope S to be identified are finally obtained.
13. The slope hidden danger identification method based on multi-source information fusion according to claim 12, wherein the method is characterized in that:
and (3) carrying out the delineation and identification of hidden danger points on the accumulated sedimentation value, setting the deformation rate threshold value to be +/-30 mm/y, delineating pixels exceeding the threshold value, regarding deformed pixels with a relatively close distance as a deformed cluster, traversing all pixels of the side slope S to be identified, and determining the positions of all hidden danger points to finish hidden danger identification.
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