CN117572378B - Mountain settlement analysis method and device based on InSAR and Beidou data - Google Patents
Mountain settlement analysis method and device based on InSAR and Beidou data Download PDFInfo
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Abstract
The application discloses mountain settlement analysis method and equipment based on InSAR and Beidou data, belongs to the technical field of geological monitoring, and is used for solving the technical problems that an existing space earth surface measurement technology is easily affected by an observation area, the precision of mountain settlement detection is low, complex mountain terrain is difficult to deal with, the spatial resolution is low and the data processing efficiency is slow. The method comprises the following steps: performing direction decomposition based on the sedimentation time sequence on Beidou data in the discrete observation points to obtain a three-dimensional Beidou sedimentation time sequence; error correction is carried out on InSAR data, and one-dimensional sedimentation time sequence of an observation area is calculated in a grouping mode; performing normalized mutual interference calculation on the main and auxiliary image data in the InSAR data to obtain a two-dimensional sedimentation time sequence; and performing correlation fusion on the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence to obtain a three-dimensional InSAR sedimentation time sequence, and finally obtaining a three-dimensional sedimentation rate field of the earth surface.
Description
Technical Field
The specification relates to the technical field of geological monitoring, in particular to a mountain settlement analysis method and device based on InSAR and Beidou data.
Background
Mountain settlement is a phenomenon in which land or a building is sunk, and is a result of downward movement of the ground surface, and is one of the causes of geological disasters such as landslide and collapse. The mountain settlement is usually caused by factors such as underground exploitation, underground construction, natural geological condition change and the like, and the real-time monitoring and early warning are carried out on the settlement condition, so that the mountain settlement is an effective means for preventing settlement disasters.
The traditional settlement measurement method comprises leveling measurement, triangular elevation measurement, GPS measurement and the like, wherein the leveling measurement, the triangular elevation measurement and the GPS measurement are all carried out on-site measurement by manually arranging measurement equipment, and the traditional measurement work is difficult to develop due to the complicated mountain area topography. Moreover, the leveling measurement and the GPS measurement are arranged in a point-line mode, so that the monitored results can not comprehensively reflect the deformation characteristics of sedimentation in the whole mountain range, long-term continuous monitoring can not be carried out, and real-time monitoring in all weather and large area is difficult to realize.
At present, two most widely applied spatial geodetic measuring means comprise InSAR (Interferometric Synthetic Aperture Radar, synthetic aperture radar interferometry) and GNSS (global navigation satellite system) technologies, which solve the problems existing in the traditional measurement and have the advantages of no weather influence, large area, low cost, high precision and the like. However, in the InSAR and time-sequential InSAR technology, global observation analysis is performed by using SAR (synthetic aperture radar) data of different dates, and the data interval is usually from several days to several months, so that the time resolution is low. GNSS technology, such as BDS (Beidou satellite navigation system), receives BDS data for analysis by arranging ground displacement monitoring equipment at detection points, and continuously detects fixed positions for a long time, but cannot acquire planar data, and has low spatial resolution.
In the existing similar technology, a time sequence InSAR technology, such as PS-InSAR (Persistent Scatterer InSAR, permanent scatterer synthetic aperture radar interferometry) or SBAS-InSAR (Small Baseline Subset InSAR, short baseline set synthetic aperture radar interferometry), and data of a Beidou surface deformation displacement detector are generally used, and the settlement value of an observation area is obtained by performing operations of interpolation, filtering, fitting and the like of space-time dimensions on the two data. However, the two techniques have the following disadvantages:
1. PS-InSAR technology relies on stable ground targets, namely PS points, which are generally unevenly distributed in an observation area, and generally are artificial targets, and in environments such as mountainous areas, fewer PS points can be found.
2. The SBAS-InSAR technology is easy to influence time-space incoherence and atmospheric delay on mountain settlement detection, has low precision and can not detect large-amplitude settlement.
3. When the three-dimensional deformation of the earth surface is obtained, only the Line of sight (LOS) deformation rate of the InSAR monitoring and the BDS interpolation horizontal deformation rate are fused, the influence of BDS interpolation deformation accuracy is large, and the method is difficult to be applied to complicated terrains such as mountain areas.
Disclosure of Invention
The embodiment of the specification provides a mountain settlement analysis method and equipment based on InSAR and Beidou data, which are used for solving the following technical problems: the existing space earth surface measurement technology is easily affected by an observation area, the precision of mountain settlement detection is low, complex mountain area terrains are difficult to deal with, the space resolution is low, and the data processing efficiency is low.
The embodiment of the specification adopts the following technical scheme:
on one hand, the embodiment of the specification provides a mountain settlement analysis method based on InSAR and Beidou data, which comprises the following steps: performing direction decomposition based on a sedimentation time sequence on Beidou data in the discrete observation points to obtain a three-dimensional Beidou sedimentation time sequence of the discrete observation points; the obtained image is opposite to the interference pattern; fusing the high-coherence target in the image pair interference map with the distributed target to obtain an observation area candidate point; performing error correction on InSAR data based on the observation region candidate points, and calculating one-dimensional sedimentation time sequence of the observation region in a grouping mode; carrying out normalized mutual interference calculation on main and auxiliary image data in the InSAR data through a preset pixel offset tracking technology, and carrying out offset time sequence decomposition on the normalized InSAR data to obtain a two-dimensional sedimentation time sequence of the observation area; performing correlation fusion on the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence to obtain a three-dimensional InSAR sedimentation time sequence; and carrying out inversion calculation on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence based on an ant colony algorithm under an energy function to obtain a ground surface three-dimensional settlement rate field of the observation area.
According to the embodiment of the specification, through the fused InSAR three-dimensional sedimentation time sequence, sedimentation time sequence fusion is carried out with the continuous three-dimensional Beidou sedimentation time sequence of the discrete observation point, and the Gibbs (Gibbs free energy, gibbs) energy function is combined, the three-dimensional time sequence sedimentation quantity and the three-dimensional sedimentation rate of the continuous space-time of the observation area are finally obtained. Meanwhile, the resolution and the measurement accuracy are improved, the coverage range is wider, and the processing efficiency is high. More distributed target points can be obtained by adopting DS-InSAR (Distributed Scatterers, DS, distributed scatterer synthetic aperture radar interferometry) technical means, and an offset tracking method is combined to obtain the InSAR three-dimensional sedimentation time sequence with high spatial resolution. And then the Beidou three-dimensional sedimentation time sequence with high time resolution is fused, so that the high-precision ground surface three-dimensional sedimentation time sequence with high space-time resolution is obtained, and the processing efficiency of InSAR data is greatly improved.
In a possible implementation manner, the method for obtaining the three-dimensional Beidou settlement time sequence of the discrete observation point comprises the following steps of: acquiring original Beidou data of discrete observation points in the observation area based on a Beidou satellite navigation system; performing cycle slip restoration of a data signal on the original Beidou data through a preset TurboEdit algorithm, and performing multipath effect elimination processing on the restored original Beidou data through a preset HHT algorithm to obtain first Beidou data; performing sedimentation time sequence decomposition on the first Beidou data under a direction vector to obtain a three-dimensional Beidou sedimentation time sequence of the discrete observation point; wherein the direction vector includes: north-south direction vector, east-west direction vector, and vertical direction vector.
In one possible embodiment, the acquired image is an interferogram; fusing the high coherence target and the distributed target in the interference image by the image to obtain an observation area candidate point, wherein the method specifically comprises the following steps: acquiring various satellite data in an observation area; wherein the plurality of satellite data comprises: a multi-view SAR satellite image, a precise orbit parameter file and DEM data; performing differential interference processing on the plurality of satellite data to obtain a plurality of image pair interference patterns; carrying out index calculation on the amplitude dispersion of the image pair interference pattern, and extracting high-coherence target candidate points in the image pair interference pattern according to the calculated index characteristics; identifying a plurality of homogeneous pixels in the image pair interferogram; performing EMI phase optimization on the plurality of homogeneous pixels through a preset sample coherence matrix, and extracting distributed target candidate points in the optimized image pair interference image; and carrying out integrated processing on the high-coherence target candidate points and the distributed target candidate points according to a Kalman filtering fusion technology to obtain the observation area candidate points in the observation area.
In a possible implementation manner, based on the observation region candidate points, error correction is performed on the InSAR data, and one-dimensional sedimentation time sequences of the observation regions are calculated in a grouping manner, which specifically includes: constructing triangular grids related to spatial distribution in the InSAR data through the observation region candidate points; carrying out phase unwrapping processing on the pixel number of the interference image by the image in each triangular grid through a preset Markov random field and a continuous operation reference station system to obtain first InSAR data; based on ECMWF data and GNSS site data, performing correction processing of an atmospheric delay error on InSAR lifting rail data in the first InSAR data through a preset iteration troposphere model to obtain second InSAR data; the InSAR lifting rail data are lifting rail data and descending rail data in the InSAR data; the correction processing is general correction processing through correction product GACOSS; and carrying out grouping calculation on the settlement time sequence in the radar sight line direction on the track lifting data and the track descending data in the second InSAR data to obtain the one-dimensional settlement time sequence.
In a possible implementation manner, the normalized mutual interference calculation of the primary and secondary image data in the InSAR data by a preset pixel offset tracking technology specifically includes: according to various satellite data in the observation area, performing rough registration processing on the main and auxiliary image data in the InSAR data to obtain a first main and auxiliary registration image; performing identification processing of homonymous point pixels on the first main and auxiliary registration images through a preset search window, and performing least square fitting processing on the homonymous point pixels after identification to determine a second main and auxiliary registration image; performing correlation calculation on the main and auxiliary images of the second main and auxiliary registration images through the search window to obtain correlation coefficients of the main and auxiliary images, performing sub-pixel level registration on the second main and auxiliary registration images according to coefficient peaks in the correlation coefficients, and determining a third main and auxiliary registration image; based on a preset coherence tracking method, performing matrix transformation on the related image windows on the third main and auxiliary registration images to obtain a main and auxiliary window matrix; wherein, the primary and secondary window includes: a primary image window matrix and a secondary image window matrix; performing frequency domain calculation on the main and auxiliary window matrixes through a two-dimensional Fourier transform algorithm to obtain main image related power and auxiliary image related power; and performing phase difference calculation on the main image related power and the auxiliary image related power to obtain a local offset based on the main and auxiliary image data in a time domain.
In a possible implementation manner, performing time sequence decomposition of offset on normalized InSAR data to obtain a two-dimensional sedimentation time sequence of the observation area, which specifically includes: subtracting the local offset in the normalized InSAR data from the satellite orbit offset in the precise orbit parameter file to obtain the settling offset of the InSAR data; performing multi-vector time sequence decomposition on the sedimentation offset to determine a distance sedimentation field and a azimuth sedimentation field; and performing geocoding treatment on the distance sedimentation field and the azimuth sedimentation field to obtain a two-dimensional sedimentation time sequence of the observation area.
In a possible implementation manner, the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence are subjected to correlation fusion to obtain a three-dimensional InSAR sedimentation time sequence, which specifically comprises: dividing the gradient of the sedimentation field area in the observation area by a preset correlation algorithm to obtain a large gradient sedimentation area and a small gradient sedimentation area; according to the area ratio between the large gradient sedimentation area and the small gradient sedimentation area, carrying out weight division on the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence to respectively obtain one-dimensional sedimentation weight and two-dimensional sedimentation weight; based on the one-dimensional sedimentation weight and the two-dimensional sedimentation weight, performing correlation fusion on the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence to obtain the three-dimensional InSAR sedimentation time sequence of the InSAR data; wherein, the three-dimensional InSAR sedimentation time sequence comprises: radar line-of-sight direction vector, distance vector, and azimuth vector.
In a possible implementation manner, before performing inversion calculation on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence based on an ant colony algorithm under an energy function to obtain a ground surface three-dimensional settlement rate field of the observation area, the method further comprisesThe method comprises the following steps: according toObtaining InSAR track lifting quantity based on the three-dimensional InSAR sedimentation time sequence>The method comprises the steps of carrying out a first treatment on the surface of the Wherein k is a mathematical constant, ">Is in the north-south direction>East-west->And (5) vertical direction>A sedimentation vector on the upper surface; />For InSAR in the line of sight direction of InSAR up-track quantity +.>In the north-south direction->East-west->And (5) vertical direction>Is a unit projection vector of (2); />An amount of lifting the InSAR rail>A settlement vector in the radar line-of-sight direction; />An amount of lifting the InSAR rail>Is the standard deviation of earth's surface subsidence; according toObtaining InSAR derailment track quantity based on the three-dimensional InSAR sedimentation time sequence>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The InSAR derailment amount in the InSAR sight line direction>In the north-south direction->East-west->And (5) vertical direction>Is a unit projection vector of (2); />For the InSAR derailment amount +.>A settlement vector in the radar line-of-sight direction; />For the InSAR derailment amount +.>Is the standard deviation of earth's surface subsidence; according to->Obtaining a Beidou observation value constraint quantity based on the three-dimensional Beidou settlement time sequence >The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The ground surface subsidence standard deviation in the direction of the Beidou radar sight line in the three-dimensional Beidou subsidence time sequence is +.>The ground surface subsidence standard deviation corresponding to the track lifting quantity in the three-dimensional Beidou subsidence time sequence is +.>The ground surface settlement standard deviation corresponding to the rail descending quantity in the three-dimensional Beidou settlement time sequence is obtained;、/>and +.>Respectively, the Beidou settling fields in the three-dimensional Beidou settling time sequence are in the north-south direction +.>East-west->And (5) vertical direction>Interpolated sedimentation vectors on; i is a mathematical constant; according to->Obtaining said energy function->The method comprises the steps of carrying out a first treatment on the surface of the Wherein N is the number of pixels in the image pair interferogram of the observation area.
In a possible implementation manner, inversion calculation is performed on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence based on an ant colony algorithm under an energy function to obtain a ground surface three-dimensional settlement rate field of the observation area, and the method specifically comprises the following steps: performing continuous space calculation on an energy function generated based on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence through a preset ant colony algorithm to obtain a minimum value of the energy function; performing inversion calculation on the energy function based on the minimum value of the energy function to obtain a ground surface three-dimensional sedimentation rate field of the observation area; wherein the surface three-dimensional sedimentation rate field comprises: three-dimensional subsidence amount of the earth surface and three-dimensional subsidence rate of the earth surface.
On the other hand, this specification embodiment still provides mountain settlement analysis equipment based on InSAR and big dipper data, equipment includes: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the mountain settlement analysis method based on the InSAR and beidou data according to any one of the above embodiments.
Compared with the prior art, the mountain settlement analysis method and the mountain settlement analysis equipment based on InSAR and Beidou data have the following beneficial technical effects:
according to the embodiment of the specification, through the fused InSAR three-dimensional sedimentation time sequence, sedimentation time sequence fusion is carried out with the continuous three-dimensional Beidou sedimentation time sequence of the discrete observation point, and the Gibbs (Gibbs free energy, gibbs) energy function is combined, the three-dimensional time sequence sedimentation quantity and the three-dimensional sedimentation rate of the continuous space-time of the observation area are finally obtained. Meanwhile, the resolution and the measurement accuracy are improved, the coverage range is wider, and the processing efficiency is high. More distributed target points can be obtained by adopting DS-InSAR (Distributed Scatterers, DS, distributed scatterer synthetic aperture radar interferometry) technical means, and an offset tracking method is combined to obtain the InSAR three-dimensional sedimentation time sequence with high spatial resolution. And then the Beidou three-dimensional sedimentation time sequence with high time resolution is fused, so that the high-precision ground surface three-dimensional sedimentation time sequence with high space-time resolution is obtained, and the processing efficiency of InSAR data is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present description, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a flow chart of a mountain settlement analysis method based on InSAR and beidou data provided by an embodiment of the present disclosure;
fig. 2 is a flowchart of an InSAR data processing method provided in an embodiment of the present disclosure;
fig. 3 is a flowchart of an InSAR data offset tracking processing method according to an embodiment of the present disclosure;
fig. 4 is a flowchart of a method for fusing sedimentation timing of beidou and InSAR according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a mountain settlement analysis device based on InSAR and beidou data according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present disclosure.
The embodiment of the specification provides a mountain settlement analysis method based on InSAR and Beidou data, as shown in FIG. 1, the mountain settlement analysis method based on the InSAR and the Beidou data specifically comprises steps S101-S105:
s101, carrying out direction decomposition based on a sedimentation time sequence on Beidou data in the discrete observation points to obtain a three-dimensional Beidou sedimentation time sequence of the discrete observation points.
Specifically, based on a Beidou satellite navigation system, original Beidou data of discrete observation points in an observation area are acquired.
Further, cycle slip restoration of data signals is carried out on original Beidou data through a preset TurboEdit algorithm, and multipath effect elimination processing is carried out on the restored original Beidou data through a preset HHT algorithm, so that first Beidou data is obtained.
Further, performing sedimentation time sequence decomposition on the first Beidou data under the direction vector to obtain a three-dimensional Beidou sedimentation time sequence of the discrete observation points. Wherein the direction vector includes: north-south direction vector, east-west direction vector, and vertical direction vector.
In one embodiment, firstly, based on a Beidou satellite navigation system, discrete observation point BDS data are acquired, a turbo edit algorithm is adopted to solve the cycle slip problem in original Beidou data, a DIA method is adopted to eliminate the coarse difference problem of the original Beidou data, and then a HHT algorithm is utilized to eliminate the multipath effect. And decomposing Beidou sedimentation time sequences in the three directions of north and south, east and west e and vertical u to obtain the continuous three-dimensional Beidou (n, e and u) sedimentation time sequences of the discrete observation points.
S102, the acquired image is opposite to the interference pattern. And fusing the high-coherence target in the image pair interference map with the distributed target to obtain an observation area candidate point. And performing error correction on the InSAR data based on the observation area candidate points, and grouping to calculate a one-dimensional sedimentation time sequence of the observation area.
Specifically, a plurality of satellite data in an observation area is acquired. Among the various satellite data include: a multi-view SAR satellite image, a precise orbit parameter file and DEM data.
Further, differential interference processing is carried out on various satellite data, and a plurality of image pair interference patterns are obtained.
Further, the index calculation of the amplitude dispersion is carried out on the image pair interferogram, and the high-coherence target candidate point in the image pair interferogram is extracted according to the calculated index features.
Further, a number of homogeneous pixels in the image pair interferogram are identified. And (3) performing EMI phase optimization on a plurality of homogeneous pixels through a preset sample coherence matrix, and extracting distributed target candidate points in the optimized image pair interference diagram.
Further, according to the Kalman filtering fusion technology, the high-coherence target candidate points and the distributed target candidate points are subjected to integrated processing, so that the observation area candidate points in the observation area are obtained.
Further, triangular grids related to spatial distribution in the InSAR data are constructed through observation of the region candidate points.
Further, the image in each triangular grid is subjected to phase unwrapping processing on the pixel number of the interference image through a preset Markov random field and a continuous operation reference station system, so that first InSAR data are obtained.
Further, based on ECMWF data and GNSS site data, and through a preset iteration troposphere model, correction processing of an atmospheric delay error is carried out on InSAR lifting rail data in the first InSAR data, and second InSAR data is obtained. The InSAR lifting rail data are lifting rail data and descending rail data in the InSAR data. The correction process is a general correction process by correcting the product GACOSS.
Further, grouping calculation of the settlement time sequence in the radar sight line direction is carried out on the track lifting data and the track descending data in the second InSAR data, and a one-dimensional settlement time sequence is obtained.
As a feasible implementation mode, a multi-view SAR satellite image covering an observation area, a precise orbit parameter file and DEM data are acquired, and an image pair interference image is obtained through the processes of image registration, differential interference and the like. And then selecting a high coherence target (PS point) and a distributed target (DS point), and fusing target points to obtain the observation area candidate points. And then, performing phase unwrapping, atmospheric delay error correction and time sequence deformation calculation by using the Beidou data, and calculating the settlement time sequence of the radar sight line direction of the InSAR ascending and descending orbit data in a grouping way to obtain the discrete one-dimensional (LOS) settlement time sequence of the observation area.
In one embodiment, fig. 2 is a flowchart of an InSAR data processing method provided in the embodiment of the present disclosure, as shown in fig. 2, a one-dimensional settling time sequence of an observation area is calculated by grouping and calculating by using a thread-level parallel technique, and the main steps are (herein, it should be noted that B1, B2, B3 and B4 are not reference numerals):
b1: differential interference processing: and obtaining a differential interference image of each image pair through DEM forward geocoding, main and auxiliary image registration, pre-filtering and differential interference.
B2: extracting and fusing a distributed target and a high-coherence target: firstly, calculating an amplitude dispersion index, extracting a high coherence candidate point (PS point), then, extracting a distributed target candidate point (DS point) through homogeneous pixel identification, sample coherence matrix and EMI phase optimization, and carrying out integrated processing on the DS point and the PS point by adopting Kalman filtering fusion to obtain an observation area candidate point.
B3: error correction and settlement information extraction: and (3) carrying out phase unwrapping and correcting an atmospheric phase error by combining the constructed triangular meshes with a Beidou auxiliary InSAR phase unwrapping algorithm, and solving time sequence sedimentation and sedimentation rate.
For phase unwrapping: the essence of the algorithm is to guide unwrapping by using an initial unwrapping phase value acquired by Beidou, and an objective function f is expressed as follows:
Wherein,
in the method, in the process of the invention,and->The number of pixels in the row and column directions of the image-to-interferogram, respectively. />Unwrapped phase values for row i and column j. />And->Is a constant; />To calculate the mask, when->The value is 1 in the Beidou fixed domain, otherwise, the value is 0.
For atmospheric delay error correction: the InSAR atmospheric delay error correction is performed by iterating the troposphere model using ECMWF (weather forecast data) and GNSS site data, and generating a universal atmospheric delay correction product GACOSS (Generic Atmospheric Correction Online Service).
B4: one-dimensional LOS sedimentation time sequence calculation: and aiming at the ascending and descending track data grouping calculation in the InSAR data, resolving an LOS (radar sight line) direction sedimentation time sequence to obtain a discrete one-dimensional (LOS) sedimentation time sequence of an observation area, namely, grouping calculation of the sedimentation time sequence in the radar sight line direction is carried out on the ascending track data and the descending track data in the second InSAR data to obtain a one-dimensional sedimentation time sequence.
S103, carrying out normalized mutual interference calculation on main and auxiliary image data in InSAR data through a preset pixel offset tracking technology (pixel-based offset tracking, POT), and carrying out time sequence decomposition of offset on the normalized InSAR data to obtain a two-dimensional sedimentation time sequence of an observation area.
Specifically, according to various satellite data in the observation area, coarse registration processing of the main and auxiliary images is performed on the main and auxiliary image data in the InSAR data, and a first main and auxiliary registration image is obtained.
Further, through a preset search window, the identification processing of the homonymous point pixels is carried out on the first main and auxiliary registration images, and the least square fitting processing is carried out on the homonymous point pixels after identification, so that the second main and auxiliary registration images are determined.
Further, through a search window, performing correlation calculation on the main and auxiliary images of the second main and auxiliary registration images to obtain correlation coefficients of the main and auxiliary images, performing sub-pixel level registration on the second main and auxiliary registration images according to coefficient peaks in the correlation coefficients, and determining a third main and auxiliary registration image.
Further, based on a preset coherence tracking method, the matrix transformation of the related image window is carried out on the third main and auxiliary registration images, and a main and auxiliary window matrix is obtained. Wherein, the primary and secondary window includes: a primary image window matrix and a secondary image window matrix.
Further, the main and auxiliary window matrixes are subjected to frequency domain calculation through a two-dimensional Fourier transform algorithm, and the main image related power and the auxiliary image related power are obtained.
Further, phase difference calculation is carried out on the main image related power and the auxiliary image related power, and the local offset based on the main image data and the auxiliary image data in the time domain is obtained.
Further, the local offset in the normalized InSAR data and the satellite orbit offset in the precise orbit parameter file are subtracted to obtain the settling offset of the InSAR data. And carrying out multi-vector time sequence decomposition on the sedimentation offset to determine a distance sedimentation field and an azimuth sedimentation field.
Further, the distance sedimentation field and the azimuth sedimentation field are subjected to geocoding treatment, and a two-dimensional sedimentation time sequence of the observation area is obtained.
In one embodiment, fig. 3 is a flowchart of an InSAR data offset tracking processing method provided in the embodiment of the present disclosure, and as shown in fig. 3, the normalized InSAR data is subjected to time sequence decomposition of an offset, and the main steps are (herein, it should be noted that C1, C2, C3, C4, C5 and C6 are not reference numerals):
c1, coarse registration of images: and finishing rough registration of the main and auxiliary images according to the precise satellite orbit parameters to obtain a first main and auxiliary registration image.
C2, image pixel level registration: searching pixels of the same-name pixels of the main and auxiliary images based on the search window, and establishing a pixel point offset model of the same-name pixels by using a least square fitting polynomial to finish pixel-level registration of the main and auxiliary images so as to obtain a second main and auxiliary registration image.
C3, sub-pixel level registration: and (3) oversampling the main and auxiliary images, calculating the correlation coefficient of the main and auxiliary images based on a search window, searching a peak value of the correlation coefficient as a registration result, and completing sub-pixel level registration of the main and auxiliary images to obtain a third main and auxiliary registration image.
And C4, tracking offset: and converting the image window matrix into a frequency domain through two-dimensional Fourier transform by adopting an intensity tracking method or a coherence tracking method to obtain main image related power and auxiliary image related power, and then calculating the phase difference of the related power spectrums in the frequency domain to obtain the local offset of the main image and the auxiliary image in the time domain, namely the local offset of the main image data and the auxiliary image data.
And C5, calculating and decomposing the system offset: and subtracting the satellite orbit offset according to the estimated local offset to obtain the settling offset of the InSAR data.
And C6, resolving a two-dimensional sedimentation time sequence: and decomposing a distance-oriented sedimentation field and a azimuth-oriented sedimentation field from the sedimentation offset of InSAR data, and then performing geocoding to obtain a two-dimensional sedimentation time sequence of the observation area.
S104, performing correlation fusion on the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence to obtain the three-dimensional InSAR sedimentation time sequence.
Specifically, the gradient size of the sedimentation field area is divided by a preset correlation algorithm, so that a large gradient sedimentation area and a small gradient sedimentation area are obtained.
Further, according to the area ratio between the large gradient sedimentation area and the small gradient sedimentation area, the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence are subjected to weight division, and the one-dimensional sedimentation weight and the two-dimensional sedimentation weight are respectively obtained.
Further, based on the one-dimensional sedimentation weight and the two-dimensional sedimentation weight, performing correlation fusion on the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence to obtain the three-dimensional InSAR sedimentation time sequence of the InSAR data. Wherein, three-dimensional InSAR subsides time sequence includes: radar line-of-sight direction vector, distance vector, and azimuth vector.
As a feasible implementation mode, a large gradient sedimentation area and a small gradient sedimentation area are distinguished by utilizing correlation, then a one-dimensional sedimentation weight and a two-dimensional sedimentation weight are obtained, different weights are set for a one-dimensional sedimentation time sequence and a two-dimensional sedimentation time sequence respectively, then two sedimentation time sequence results of the InSAR are fused by using a least square method, namely, the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence are subjected to correlation fusion, and finally the fused three-dimensional InSAR sedimentation time sequence is obtained.
S105, performing inversion calculation on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence based on an ant colony algorithm under an energy function to obtain a ground surface three-dimensional settlement rate field of the observation area.
In particular according toInSAR track lifting quantity based on three-dimensional InSAR sedimentation time sequence is obtained>. Wherein k is a mathematical constant, ">Is in the north-south direction>East-west->And (5) vertical direction>A sedimentation vector on the upper surface. />InSAR track lifting amount in InSAR sight line direction>In the north-south direction->East-west->And (5) vertical direction>Is a unit projection vector of (a). />Track-lifting amount for InSARA settlement vector in the radar line-of-sight direction. />Track-lifting amount for InSAR>Is the standard deviation of the surface subsidence.
Further according toObtaining InSAR derailment track quantity based on three-dimensional InSAR sedimentation time sequence>. Wherein (1)>InSAR derailment amount +.>In the north-south direction->East-west->And (5) vertical direction>Is a unit projection vector of (a).For InSAR derailment amount +.>A settlement vector in the radar line-of-sight direction. />For InSAR derailment track quantityIs the standard deviation of the surface subsidence.
Further according toObtaining Beidou observation value constraint quantity ++based on three-dimensional Beidou settlement time sequence>. Wherein (1)>Is the ground surface subsidence standard deviation in the direction of the Beidou radar sight line in the three-dimensional Beidou subsidence time sequence >Is the ground surface subsidence standard deviation corresponding to the rail lifting quantity in the three-dimensional Beidou subsidence time sequence>The ground surface sedimentation standard deviation corresponding to the rail descending quantity in the three-dimensional Beidou sedimentation time sequence. />、/>And +.>Respectively, the north and south directions of the Beidou sedimentation fields in the three-dimensional Beidou sedimentation time sequence are +>East-west->And (5) vertical direction>And interpolated sedimentation vectors. i is a mathematical constant.
Further according toObtaining an energy function->The method comprises the steps of carrying out a first treatment on the surface of the Wherein N is the number of pixels in the image pair interferogram of the observation area.
Further, continuous space calculation is carried out on the energy function generated based on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence through a preset ant colony algorithm, so that the minimum value of the energy function is obtained.
Further, based on the minimum value of the energy function, inversion calculation is carried out on the energy function, and the earth surface three-dimensional sedimentation rate field of the observation area is obtained. Wherein, the three-dimensional subsidence rate field of earth's surface includes: three-dimensional subsidence amount of the earth surface and three-dimensional subsidence rate of the earth surface.
In one embodiment, fig. 4 is a flow chart of a method for fusing sedimentation timing of beidou and InSAR provided in the embodiment of the present disclosure, as shown in fig. 4, mainly including (here, it should be noted that, E1, E2, and E3 below are not reference numerals): e1, firstly acquiring a three-dimensional Beidou settlement time sequence and a three-dimensional InSAR settlement time sequence; e2, establishing a Gibbs energy equation based on a Markov random field; e3, solving an energy function by adopting an ant colony algorithm : the ant position is expressed as a three-dimensional subsidence value of the earth surface, an energy function is used as an adaptability function of ants, K is used as the number of the ant groups, geMax is the maximum iteration algebra of the ant colony, G (K) is the current ant algebra of the pixel K, and N is the number of the pixels. Then in a continuous space through the ant colonyInternally solving an energy function>The minimum value of the system can be inverted to obtain the three-dimensional subsidence quantity and the three-dimensional subsidence rate of the earth surface, so that real-time monitoring and early warning of the earth surface or mountain settlement condition can be completed, and earth surface settlement disasters can be prevented better.
In addition, the embodiment of the present disclosure further provides a mountain settlement analysis device based on the InSAR and the beidou data, as shown in fig. 5, a mountain settlement analysis device 500 based on the InSAR and the beidou data specifically includes:
at least one processor 501. And a memory 502 communicatively coupled to the at least one processor 501. Wherein the memory 502 stores instructions executable by the at least one processor 501 to enable the at least one processor 501 to perform:
performing direction decomposition based on the sedimentation time sequence on the Beidou data in the discrete observation points to obtain a three-dimensional Beidou sedimentation time sequence of the discrete observation points;
The obtained image is opposite to the interference pattern; fusing the high coherence target in the interference image with the distributed target to obtain an observation area candidate point; error correction is carried out on InSAR data based on the observation area candidate points, and one-dimensional sedimentation time sequence of the observation area is calculated in a grouping mode;
carrying out normalized mutual interference calculation on main and auxiliary image data in InSAR data through a preset pixel offset tracking technology, and carrying out time sequence decomposition of offset on the normalized InSAR data to obtain a two-dimensional sedimentation time sequence of an observation area;
performing correlation fusion on the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence to obtain a three-dimensional InSAR sedimentation time sequence;
and carrying out inversion calculation on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence based on an ant colony algorithm under an energy function to obtain a ground surface three-dimensional settlement rate field of the observation area.
According to the embodiment of the specification, through the fused InSAR three-dimensional sedimentation time sequence, sedimentation time sequence fusion is carried out with the continuous three-dimensional Beidou sedimentation time sequence of the discrete observation point, and the Gibbs (Gibbs free energy, gibbs) energy function is combined, the three-dimensional time sequence sedimentation quantity and the three-dimensional sedimentation rate of the continuous space-time of the observation area are finally obtained. Meanwhile, the resolution and the measurement accuracy are improved, the coverage range is wider, and the processing efficiency is high. More distributed target points can be obtained by adopting DS-InSAR (Distributed Scatterers, DS, distributed scatterer synthetic aperture radar interferometry) technical means, and an offset tracking method is combined to obtain the InSAR three-dimensional sedimentation time sequence with high spatial resolution. And then the Beidou three-dimensional sedimentation time sequence with high time resolution is fused, so that the high-precision ground surface three-dimensional sedimentation time sequence with high space-time resolution is obtained, and the processing efficiency of InSAR data is greatly improved.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely exemplary of the present disclosure and is not intended to limit the disclosure. Various modifications and variations of the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of the embodiments of the present specification, should be included in the scope of the claims of the present specification.
Claims (10)
1. The mountain settlement analysis method based on InSAR and Beidou data is characterized by comprising the following steps of:
performing direction decomposition based on a sedimentation time sequence on Beidou data in the discrete observation points to obtain a three-dimensional Beidou sedimentation time sequence of the discrete observation points;
acquiring an image pair interference pattern; fusing the high-coherence target in the image pair interference map with the distributed target to obtain an observation area candidate point; performing error correction on InSAR data based on the observation region candidate points, and calculating one-dimensional sedimentation time sequence of the observation region in a grouping mode;
carrying out normalized mutual interference calculation on main and auxiliary image data in the InSAR data through a preset pixel offset tracking technology, and carrying out offset time sequence decomposition on the normalized InSAR data to obtain a two-dimensional sedimentation time sequence of the observation area;
performing correlation fusion on the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence to obtain a three-dimensional InSAR sedimentation time sequence;
and carrying out inversion calculation on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence based on an ant colony algorithm under an energy function to obtain a ground surface three-dimensional settlement rate field of the observation area.
2. The mountain settlement analysis method based on InSAR and Beidou data according to claim 1, wherein the method is characterized in that the Beidou data in the discrete observation points are subjected to direction decomposition based on a settlement time sequence to obtain a three-dimensional Beidou settlement time sequence of the discrete observation points, and specifically comprises the following steps:
acquiring original Beidou data of discrete observation points in the observation area based on a Beidou satellite navigation system;
performing cycle slip restoration of a data signal on the original Beidou data through a preset TurboEdit algorithm, and performing multipath effect elimination processing on the restored original Beidou data through a preset HHT algorithm to obtain first Beidou data;
performing sedimentation time sequence decomposition on the first Beidou data under a direction vector to obtain a three-dimensional Beidou sedimentation time sequence of the discrete observation point; wherein the direction vector includes: north-south direction vector, east-west direction vector, and vertical direction vector.
3. The mountain settlement analysis method based on InSAR and Beidou data according to claim 1, wherein an image pair interference pattern is obtained; fusing the high coherence target and the distributed target in the interference image by the image to obtain an observation area candidate point, wherein the method specifically comprises the following steps:
Acquiring various satellite data in an observation area; wherein the plurality of satellite data comprises: a multi-view SAR satellite image, a precise orbit parameter file and DEM data;
performing differential interference processing on the plurality of satellite data to obtain a plurality of image pair interference patterns;
carrying out index calculation on the amplitude dispersion of the image pair interference pattern, and extracting high-coherence target candidate points in the image pair interference pattern according to the calculated index characteristics;
identifying a plurality of homogeneous pixels in the image pair interferogram; performing EMI phase optimization on the plurality of homogeneous pixels through a preset sample coherence matrix, and extracting distributed target candidate points in the optimized image pair interference image;
and carrying out integrated processing on the high-coherence target candidate points and the distributed target candidate points according to a Kalman filtering fusion technology to obtain the observation area candidate points in the observation area.
4. The method for analyzing mountain settlement based on InSAR and Beidou data according to claim 1, wherein error correction is carried out on InSAR data based on the observation area candidate points, and one-dimensional settlement time sequences of the observation areas are calculated in a grouping mode, and the method specifically comprises the following steps:
Constructing triangular grids related to spatial distribution in the InSAR data through the observation region candidate points;
carrying out phase unwrapping processing on the pixel number of the interference image by the image in each triangular grid through a preset Markov random field and a continuous operation reference station system to obtain first InSAR data;
based on ECMWF data and GNSS site data, performing correction processing of an atmospheric delay error on InSAR lifting rail data in the first InSAR data through a preset iteration troposphere model to obtain second InSAR data; the InSAR lifting rail data are lifting rail data and descending rail data in the InSAR data; the correction processing is general correction processing through correction product GACOSS;
and carrying out grouping calculation on the settlement time sequence in the radar sight line direction on the track lifting data and the track descending data in the second InSAR data to obtain the one-dimensional settlement time sequence.
5. The mountain settlement analysis method based on InSAR and Beidou data according to claim 1, wherein the normalized mutual interference calculation of the main and auxiliary image data in the InSAR data is performed by a preset pixel offset tracking technology, and specifically comprises the following steps:
According to various satellite data in the observation area, performing rough registration processing on the main and auxiliary image data in the InSAR data to obtain a first main and auxiliary registration image;
performing identification processing of homonymous point pixels on the first main and auxiliary registration images through a preset search window, and performing least square fitting processing on the homonymous point pixels after identification to determine a second main and auxiliary registration image;
performing correlation calculation on the main and auxiliary images of the second main and auxiliary registration images through the search window to obtain correlation coefficients of the main and auxiliary images, performing sub-pixel level registration on the second main and auxiliary registration images according to coefficient peaks in the correlation coefficients, and determining a third main and auxiliary registration image;
based on a preset coherence tracking method, performing matrix transformation on the related image windows on the third main and auxiliary registration images to obtain a main and auxiliary window matrix; wherein, the primary and secondary window includes: a primary image window matrix and a secondary image window matrix;
performing frequency domain calculation on the main and auxiliary window matrixes through a two-dimensional Fourier transform algorithm to obtain main image related power and auxiliary image related power;
and performing phase difference calculation on the main image related power and the auxiliary image related power to obtain a local offset based on the main and auxiliary image data in a time domain.
6. The mountain settlement analysis method based on InSAR and Beidou data according to claim 5, wherein the method is characterized in that the normalized InSAR data is subjected to time sequence decomposition of offset to obtain a two-dimensional settlement time sequence of the observation area, and specifically comprises the following steps:
subtracting the local offset in the normalized InSAR data from the satellite orbit offset in the precise orbit parameter file to obtain the settling offset of the InSAR data;
performing multi-vector time sequence decomposition on the sedimentation offset to determine a distance sedimentation field and a azimuth sedimentation field;
and performing geocoding treatment on the distance sedimentation field and the azimuth sedimentation field to obtain a two-dimensional sedimentation time sequence of the observation area.
7. The mountain settlement analysis method based on InSAR and Beidou data according to claim 1, wherein the correlation fusion is carried out on the one-dimensional settlement time sequence and the two-dimensional settlement time sequence to obtain a three-dimensional InSAR settlement time sequence, and the method specifically comprises the following steps:
dividing the gradient of the sedimentation field area in the observation area by a preset correlation algorithm to obtain a large gradient sedimentation area and a small gradient sedimentation area;
According to the area ratio between the large gradient sedimentation area and the small gradient sedimentation area, carrying out weight division on the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence to respectively obtain one-dimensional sedimentation weight and two-dimensional sedimentation weight;
based on the one-dimensional sedimentation weight and the two-dimensional sedimentation weight, performing correlation fusion on the one-dimensional sedimentation time sequence and the two-dimensional sedimentation time sequence to obtain the three-dimensional InSAR sedimentation time sequence of the InSAR data; wherein, the three-dimensional InSAR sedimentation time sequence comprises: radar line-of-sight direction vector, distance vector, and azimuth vector.
8. The method for analyzing mountain settlement based on InSAR and Beidou data according to claim 1, wherein before performing inversion calculation on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence based on an ant colony algorithm under an energy function to obtain a ground surface three-dimensional settlement rate field of the observation area, the method further comprises:
according toObtaining InSAR track lifting quantity based on the three-dimensional InSAR sedimentation time sequence>The method comprises the steps of carrying out a first treatment on the surface of the Wherein k is a mathematical constant,is in the north-south direction>East-west->And (5) vertical direction>A sedimentation vector on the upper surface;for InSAR in the line of sight direction of InSAR up-track quantity +. >In the north-south direction->In east-west directionAnd (5) vertical direction>Is a unit projection vector of (2); />An amount of lifting the InSAR rail>A settlement vector in the radar line-of-sight direction; />An amount of lifting the InSAR rail>Is the standard deviation of earth's surface subsidence;
according toObtaining InSAR derailment track quantity based on the three-dimensional InSAR sedimentation time sequence>The method comprises the steps of carrying out a first treatment on the surface of the Wherein,the InSAR derailment amount in the InSAR sight line direction>In the north-south direction->East-west->And (5) vertical direction>Is a unit projection vector of (2); />For the InSAR derailment amount +.>A settlement vector in the radar line-of-sight direction; />For the InSAR derailment amount +.>Is the standard deviation of earth's surface subsidence;
according toObtaining a Beidou observation value constraint quantity based on the three-dimensional Beidou settlement time sequence>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The ground surface subsidence standard deviation in the direction of the Beidou radar sight line in the three-dimensional Beidou subsidence time sequence is +.>The ground surface subsidence standard deviation corresponding to the track lifting quantity in the three-dimensional Beidou subsidence time sequence is +.>The ground surface settlement standard deviation corresponding to the rail descending quantity in the three-dimensional Beidou settlement time sequence is obtained; />、/>And +.>Respectively, the Beidou settling fields in the three-dimensional Beidou settling time sequence are in the north-south direction +.>East-west->And (5) vertical direction>Interpolated sedimentation vectors on; i is a mathematical constant;
according to Obtaining said energy function->The method comprises the steps of carrying out a first treatment on the surface of the Wherein N is the number of pixels in the image pair interferogram of the observation area.
9. The mountain settlement analysis method based on InSAR and Beidou data according to claim 1, wherein inversion calculation is performed on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence based on an ant colony algorithm under an energy function to obtain a ground surface three-dimensional settlement rate field of the observation area, and the method specifically comprises the following steps:
performing continuous space calculation on an energy function generated based on the three-dimensional Beidou settlement time sequence and the three-dimensional InSAR settlement time sequence through a preset ant colony algorithm to obtain a minimum value of the energy function;
performing inversion calculation on the energy function based on the minimum value of the energy function to obtain a ground surface three-dimensional sedimentation rate field of the observation area; wherein the surface three-dimensional sedimentation rate field comprises: three-dimensional subsidence amount of the earth surface and three-dimensional subsidence rate of the earth surface.
10. Mountain settlement analysis equipment based on InSAR and big dipper data, its characterized in that, equipment includes:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the InSAR and beidou data based mountain settlement analysis method according to any one of claims 1-9.
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