CN117214898B - Wide-scale earth surface deformation refined remote sensing method based on multidimensional electromagnetic information depth fusion - Google Patents
Wide-scale earth surface deformation refined remote sensing method based on multidimensional electromagnetic information depth fusion Download PDFInfo
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Abstract
A wide-scale earth surface deformation refined remote sensing method based on multidimensional electromagnetic information depth fusion obtains high-frequency and low-frequency-band full-polarization differential interference phases and coherence coefficients, identifies a non-coherent area, judges a large-scale deformation area and a small-scale deformation area, calculates earth surface deformation of the large-scale deformation area by using a pixel tracking method based on an amplitude maximum cross-correlation coefficient, divides the small-scale deformation area into a vegetation area and a non-vegetation area based on a high-frequency-band coherence joint constraint criterion, obtains deformation of the deepest penetration position of the vegetation area based on a complex-coherent-coefficient phase maximization separation principle, uses a SquesAR algorithm under a polarization optimal coherence principle as the small-scale earth surface deformation of the vegetation area, calculates deformation differences of adjacent pixels three times, obtains small-scale earth surface deformation results of the non-vegetation area, fuses the large-scale deformation, the small-scale deformation in the vegetation area and the small-scale deformation in the non-vegetation coverage area, and serves as the multi-area wide-scale earth surface deformation of a complex scene.
Description
Technical Field
The invention belongs to the technical field of earth surface deformation measurement, and particularly relates to a frequency division interference image processing technology.
Background
The synthetic aperture radar differential interferometry DINSAR technology is a novel remote sensing earth observation means, has the advantages of all-day, all-weather and high-resolution observation, and is applied to the fields of natural disaster monitoring and earth surface subsidence monitoring, such as landslide, earthquake and volcanic eruption research.
Along with the continuous improvement of application requirements, the limitations of the traditional DINSAR technology are gradually highlighted, and based on the traditional DINSAR technology, the technology of PS-DINSAR, SBAS-InSAR and DS-InSAR of distributed target interferometry is provided.
Based on the PS-InSAR technology, 24-scene Sentinel-1A image data in 2019-2020 are used for researching the earth surface subsidence condition of a part of a city, and verifying results and analyzing causes. The 23-scene Sentinel-1A image data of 7 months 2017 to 12 months 2018 are selected in the university of Tibetan 2022, and the deformation characteristics and the trend of landslide disasters in the river basin of a certain county are analyzed by using an SBAS-InSAR technology, so that a ground disaster monitoring and prevention reference is provided. Taking a certain coal mine in the west as an example in the mining university of 2022, based on 52-scene Sentinel-1A image data covering a research area, acquiring surface deformation space-time distribution information caused by mining in the area 2016-2018 by using a DS-InSAR time sequence surface deformation monitoring method, and comparing and analyzing level measured data to verify the reliability of the method.
The deformation characteristics of various ground surfaces are large in difference, the mechanism is complex, such as landslide and debris flow are shown as local large deformation, and the deformation of the urban ground surface subsidence and high-speed rail surface is shown as wide-area micro deformation. With the increase of human activities, landslide and settlement frequently occur, and requirements of wide area, wide scale, high precision, high authenticity and high credibility are provided for deformation monitoring.
The spaceborne or airborne differential interferometry DINSAR technology is based on multiple navigation through acquired data and extracting reference information based on a permanent scattering point network, and is a preferred means for wide area deformation monitoring. Aiming at the surface deformation measurement of complex scenes, the prior differential interferometry DINSAR technology faces the following difficulties:
the measurable deformation scale range is small, the scale and the precision are poor, the complex scene has the surface deformation of multiple scales, the prior art is based on single frequency band measurement, the high frequency band cannot record large deformation information, and the low frequency band sensing tiny deformation precision is limited;
the scattering mechanism of the dense vegetation area is complex, single-frequency single-pole electromagnetic scattering comes from the upper layer in the branches, so that the ground deformation information of the vegetation bottom is difficult to truly reflect, and the ground deformation measurement is unreal;
the stable scattering points are unevenly distributed, so that the monitoring reliability is low, the long-time deformation monitoring has strong dependence on the stable scattering points, the stable scattering points are distributed in a sparse area in space, and the monitoring reliability is obviously reduced.
In summary, a method for finely monitoring the deformation of the surface of the wide scale surface suitable for complex scenes is urgently needed, for example, electromagnetic information utilized by SAR microwave remote sensing has multidimensional information characteristics of frequency, amplitude, phase and polarization.
Disclosure of Invention
In order to solve the technical problems of poor adaptability and single scale of the traditional differential interferometry deformation scene, the invention starts from the mechanism of surface deformation extraction, adopts the technical scheme of wide-scale surface deformation refined remote sensing by multi-dimensional electromagnetic information depth fusion, is based on the high-low frequency full-polarization interferometry synthetic aperture radar technology, flexibly applies various technologies such as pixel tracking, optimal polarization coherence, distributed scatterer differential interferometry, frequency division interferometry and the like, expands the observation dimension, has the technical effect of efficiently acquiring systematic and continuous deformation monitoring information of a complex scene, and effectively improves the measurement precision and reliability.
Step one: and acquiring high-frequency and low-frequency full-polarization SAR images by using a high-frequency and low-frequency full-polarization interference synthetic aperture radar system, and respectively carrying out image registration to acquire high-frequency and low-frequency full-polarization differential interference phases and coherence coefficients.
Step two: and (3) identifying a non-coherent region according to the low-frequency-band coherence coefficient obtained in the step one, judging as a large-scale deformation region by combining SAR amplitude information, and dividing the deformation scale region by using other regions as medium-small-scale deformation regions.
Step three: and (3) calculating the earth surface deformation of the large-scale deformation area determined in the step two by using a pixel tracking method based on the maximum cross correlation coefficient of the amplitude.
Let the coordinates and pixel values of the main image point be (m, n), A m (m, n), the corresponding auxiliary image homonymous point coordinates and pixel values are (m-Deltax, n-Deltay), A respectively s (m-Deltax, n-Deltay), selecting window with (m, n) as center and 5×5 as size, and setting average value of pixels in window as MeanA m (m, n), taking (m-Deltax, n-Deltay) as the center, 21×21 as the size selection window, setting the average value of pixels in the window as Mean A s (m-Deltax, n-Deltay) by the formulaRepresenting normalized cross-correlation coefficient rho (m, n) between coordinates (m, n) and (m-Deltax, n-Deltay), wherein sigma represents summation, traversing pixel points in a large-scale deformation area, calculating a maximum cross-correlation coefficient, acquiring pixel level registration offset between a main image and an auxiliary image, processing the pixel level registration offset by a cubic spline interpolation method, acquiring sub-pixel level registration offset, calculating offset caused by topography fluctuation by using priori DEM data and orbit data and removing the offset, and acquiring registration corresponding to the large-scale deformationAnd (3) shifting the amount, performing geocoding, and converting the amount into a deformation amount corresponding to the real geographic position, wherein the deformation amount is used as a large-scale surface deformation amount.
Step four: and (3) dividing the medium-small-scale deformation area into a vegetation area and a non-vegetation area based on the high-low-frequency-band coherence joint constraint criterion according to the high-low-frequency-band coherence coefficient, the interference phase and the SAR amplitude obtained in the step one.
The low-frequency band coherence of the vegetation area is larger than the high-frequency band, the high-frequency band coherence and the low-frequency band coherence of the non-vegetation area are not different, the high-frequency band coherence coefficient and the low-frequency band coherence coefficient are subjected to homonymous pixel point matching, the difference of threshold constraint coherence coefficients is set, the vegetation area is initially selected, isolated pseudo vegetation points are removed through median filtering, and the real vegetation area is identified.
By Cor L (m, n) represents the coherence coefficient of the low-band pixel point coordinates (m, n), cor X (m, n) represents the coherence coefficient of the high-band pixel point coordinates (m, n), ΔCor (m, n) represents the difference of the coherence coefficients, threshold represents the set Threshold, p (m, n) represents the type of pixel point (m, n), 1 represents the vegetation region, 0 represents the non-vegetation region, and the formula is usedIndicating the high and low frequency band coherence joint constraint criteria.
Step five: based on a principle of phase maximization separation of complex coherent coefficients, scattering phase centers of a vegetation region are separated, phases corresponding to the lowest scattering centers are selected, and deformation of the deepest penetration position of the vegetation region is obtained through differential interference treatment and is used as medium-small scale surface deformation of the vegetation region.
Representing the unit complex vector of the polarized scattering mechanism by omega 12 Representing a non-hermitian complex coherent matrix () H Represents the conjugate transpose of the matrix, j represents the imaginary unit, and the formula is usedCalculating the phase value of the complex coherence coefficient, solving the maximum value, taking the maximum value as the complex coherence coefficient with the maximized phase difference, carrying out differential interference treatment on the interference phase of the center of the lowest scattering phase, and obtaining the medium and small scale of the vegetation areaThe degree surface deformation.
Step six: according to low-frequency interference data, using a SqueseAR algorithm under a polarization optimal coherence principle to obtain initial earth surface deformation, calculating deformation difference of adjacent pixels, setting a threshold to select a low-frequency band inapplicable region, according to frequency division interference data, using the SqueseAR algorithm under the polarization optimal coherence principle again to update earth surface deformation, calculating deformation difference of adjacent pixels again, setting the threshold to select the frequency division interference inapplicable region, and according to high-frequency interference data, using the SqueseAR algorithm under the polarization optimal coherence principle again to obtain a medium-and small-scale earth surface deformation result of a non-vegetation region.
The deformation difference of adjacent pixels in the surface deformation is represented by delta R, lambda L Represents the wavelength of the low frequency band lambda X Represents the wavelength of the high frequency band lambda M Represents the equivalent wavelength after the frequency division of the high frequency band, eta represents the conversion coefficient and uses the formula、、/>The division principle of the low-frequency band application region, the frequency division interference application region and the high-frequency band application region is respectively represented.
The SqueseAR algorithm under the polarization optimal coherence principle is used for performing PSIO processing on interference data, establishing an optimal coherence criterion according to coherence amplitude information, acquiring an optimal scattering mechanism and highest coherence, performing SquesAR processing, acquiring stable scattering points with uniform distribution and high density, and acquiring high-precision and high-reliability surface deformation.
With omega opt Represents an optimal scattering state vector, T 11 And T 22 Respectively represent the polarization coherence matrix and omega corresponding to two interference images H And T represents an intermediate variable, phi represents phase, gamma opt Representing the optimal complex coherence coefficient by the formula、/>、/>Representing the optimal coherence criterion.
Step seven: and D, fusing the large-scale deformation obtained in the step three, the small-scale deformation in the vegetation area obtained in the step five and the small-scale deformation in the non-vegetation coverage area obtained in the step six, and obtaining the multi-area and wide-scale surface deformation results of the complex scene.
The invention uses high and low frequency fusion wide scale and high precision deformation monitoring, adapts to various types of earth surface deformation monitoring through iterative fusion, and solves the difficult problems of small deformation monitoring scale range, large scale and high precision; the high-frequency polarization interference and the low-frequency polarization interference are used for accurately extracting the surface deformation of the vegetation area, so that the problems of poor electromagnetic scattering penetrability and unrealistic deformation measurement of the vegetation area are solved; the stable scattering points are extracted by using high-frequency polarization and low-frequency polarization optimal coherence, so that the net density of the deformation monitoring scattering points is increased, and the problem of low reliability of deformation monitoring is solved.
Drawings
Fig. 1 is a flow chart of a method.
Detailed Description
The technical scheme of the invention is specifically described below with reference to the method flow shown in fig. 1.
And acquiring high-frequency and low-frequency full-polarization SAR images by using a high-frequency and low-frequency full-polarization interference synthetic aperture radar system, and respectively carrying out image registration to acquire high-frequency and low-frequency full-polarization differential interference phases and coherence coefficients.
And identifying a non-coherent region according to the low-frequency band coherence coefficient, judging the non-coherent region as a large-scale deformation region by combining SAR amplitude information, and dividing the deformation scale region by other regions which are small-scale and medium-scale deformation regions.
And calculating the surface deformation of the large-scale deformation area by using a pixel tracking method based on the maximum cross correlation coefficient of the amplitude.
The coordinates and pixel values of the main image point are respectively (m, n),A m (m, n), the corresponding auxiliary image homonymous point coordinates and pixel values are (m-Deltax, n-Deltay), A respectively s (m-Deltax, n-Deltay), selecting window with (m, n) as center and 5×5 as size, and setting average value of pixels in window as MeanA m (m, n), taking (m-Deltax, n-Deltay) as the center, 21×21 as the size selection window, setting the average value of pixels in the window as Mean A s (m-Deltax, n-Deltay) by the formulaRepresenting normalized cross-correlation coefficient rho (m, n) between coordinates (m, n) and (m-deltax, n-deltay), wherein Σ represents summation, traversing pixel points in a large-scale deformation area, calculating the maximum cross-correlation coefficient, acquiring pixel level registration offset between a main image and an auxiliary image, processing the pixel level registration offset by a cubic spline interpolation method, acquiring sub-pixel level registration offset, calculating offset caused by topography fluctuation by using priori DEM data and track data, removing the offset, acquiring registration offset corresponding to the large-scale deformation, performing geographic coding, converting the registration offset into a deformation corresponding to a real geographic position, and taking the deformation as a large-scale surface deformation.
And dividing the medium-small scale deformation region into a vegetation region and a non-vegetation region based on the high-low frequency band coherence joint constraint criterion according to the high-low frequency band coherence coefficient, the interference phase and the SAR amplitude.
The low-frequency band coherence of the vegetation area is larger than the high-frequency band, the high-frequency band coherence and the low-frequency band coherence of the non-vegetation area are not different, the high-frequency band coherence coefficient and the low-frequency band coherence coefficient are subjected to homonymous pixel point matching, the difference of threshold constraint coherence coefficients is set, the vegetation area is initially selected, isolated pseudo vegetation points are removed through median filtering, and the real vegetation area is identified.
By Cor L (m, n) represents the coherence coefficient of the low-band pixel point coordinates (m, n), cor X (m, n) represents the coherence coefficient of the high-band pixel point coordinates (m, n), ΔCor (m, n) represents the difference of the coherence coefficients, threshold represents a set Threshold value, for example, 0.18, p (m, n) represents the type of pixel point (m, n), 1 represents the vegetation region, 0 represents the non-vegetation region, and the formula isIndicating the high and low frequency band coherence joint constraint criteria.
Based on a principle of phase maximization separation of complex coherent coefficients, scattering phase centers of a vegetation region are separated, phases corresponding to the lowest scattering centers are selected, and deformation of the deepest penetration position of the vegetation region is obtained through differential interference treatment and is used as medium-small scale surface deformation of the vegetation region.
Representing the unit complex vector of the polarized scattering mechanism by omega 12 Representing a non-hermitian complex coherent matrix () H Represents the conjugate transpose of the matrix, j represents the imaginary unit, and the formula is usedAnd calculating a complex coherence coefficient phase value, solving the maximum value, taking the complex coherence coefficient as a complex coherence coefficient with the maximized phase difference, and carrying out differential interference treatment on the interference phase of the center of the lowest scattering phase to obtain the medium-small scale surface deformation of the vegetation region.
According to low-frequency interference data, using a SqueseAR algorithm under a polarization optimal coherence principle to obtain initial earth surface deformation, calculating deformation difference of adjacent pixels, setting a threshold to select a low-frequency band inapplicable region, according to frequency division interference data, using the SqueseAR algorithm under the polarization optimal coherence principle again to update earth surface deformation, calculating deformation difference of adjacent pixels again, setting the threshold to select the frequency division interference inapplicable region, and according to high-frequency interference data, using the SqueseAR algorithm under the polarization optimal coherence principle again to obtain a medium-and small-scale earth surface deformation result of a non-vegetation region.
The deformation difference of adjacent pixels in the surface deformation is represented by delta R, lambda L Represents the wavelength of the low frequency band lambda X Represents the wavelength of the high frequency band lambda M Represents the equivalent wavelength after the division of the high frequency band, eta represents the conversion coefficient, such as 0.93, and is expressed by the formula、、/>The division principle of the low-frequency band application region, the frequency division interference application region and the high-frequency band application region is respectively represented.
The traditional SquesAR algorithm combines a permanent scatterer and a distributed scatterer, the number of observation points of a target area is increased, a high-density deformation field of the target area is obtained, and the obtained observation points are uneven in number distribution and low in point density due to complex scattering characteristics of different types of non-vegetation areas, so that the observation requirements cannot be met.
The SqueseAR algorithm under the polarization optimal coherence principle is used for performing PSIO processing on interference data, establishing an optimal coherence criterion according to coherence amplitude information, acquiring an optimal scattering mechanism and highest coherence, performing SquesAR processing, acquiring stable scattering points with uniform distribution and high density, and acquiring high-precision and high-reliability surface deformation.
With omega opt Represents an optimal scattering state vector, T 11 And T 22 Respectively represent the polarization coherence matrix and omega corresponding to two interference images H And T represents an intermediate variable, phi represents phase, gamma opt Representing the optimal complex coherence coefficient by the formula、/>、/>Representing the optimal coherence criterion.
And fusing large-scale deformation, small-scale deformation in a vegetation area and small-scale deformation in a non-vegetation coverage area to obtain the multi-area and wide-scale surface deformation result of the complex scene.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as being included within the spirit and scope of the present invention.
Claims (8)
1. A wide-scale earth surface deformation refined remote sensing method based on multidimensional electromagnetic information depth fusion is characterized by comprising the following steps:
step one: acquiring high-frequency and low-frequency full-polarization SAR images by using a high-frequency and low-frequency full-polarization interference synthetic aperture radar system, and respectively carrying out image registration to acquire high-frequency and low-frequency full-polarization differential interference phases and coherence coefficients;
step two: according to the low-frequency band coherence coefficient obtained in the first step, a coherence loss region is identified, and a large-scale deformation region is determined by combining SAR amplitude information, and other regions are small-scale and medium-scale deformation regions, so that the deformation scale regions are divided;
step three: calculating the earth surface deformation of the large-scale deformation area determined in the second step by using a pixel tracking method based on the maximum cross correlation coefficient of the amplitude;
step four: dividing a medium-small-scale deformation area into a vegetation area and a non-vegetation area based on a high-low-frequency-band coherence joint constraint criterion according to the high-low-frequency-band coherence coefficient, the interference phase and the SAR amplitude obtained in the first step;
step five: separating scattering phase centers of the vegetation region based on a principle of phase maximization separation of complex coherence coefficients, selecting phases corresponding to the lowest scattering centers, and obtaining deformation of the deepest penetration position of the vegetation region through differential interference treatment to serve as medium-small scale surface deformation of the vegetation region;
step six: according to low-frequency interference data, using a SqueseAR algorithm under a polarization optimal coherence principle to obtain initial earth surface deformation, calculating deformation difference of adjacent pixels, setting a threshold to select a low-frequency band inapplicable region, according to frequency division interference data, using the SqueseAR algorithm under the polarization optimal coherence principle again to update earth surface deformation, calculating deformation difference of adjacent pixels again, setting the threshold to select the frequency division interference inapplicable region, and according to high-frequency interference data, using the SqueseAR algorithm under the polarization optimal coherence principle again to obtain a medium-and small-scale earth surface deformation result of a non-vegetation region;
step seven: and D, fusing the large-scale deformation obtained in the step three, the small-scale deformation in the vegetation area obtained in the step five and the small-scale deformation in the non-vegetation coverage area obtained in the step six, and obtaining the multi-area and wide-scale surface deformation results of the complex scene.
2. The method for refining the remote sensing of the surface deformation of the wide scale based on the depth fusion of the multidimensional electromagnetic information according to claim 1, wherein the third step comprises the following steps: let the coordinates and pixel values of the main image point be (m, n), A m (m, n), the corresponding auxiliary image homonymous point coordinates and pixel values are (m-Deltax, n-Deltay), A respectively s (m-Deltax, n-Deltay), selecting window with (m, n) as center and 5×5 as size, and setting average value of pixels in window as MeanA m (m, n), taking (m-Deltax, n-Deltay) as the center, 21×21 as the size selection window, setting the average value of pixels in the window as Mean A s (m-Deltax, n-Deltay) by the formulaRepresenting normalized cross-correlation coefficient rho (m, n) between coordinates (m, n) and (m-deltax, n-deltay), wherein Σ represents summation, traversing pixel points in a large-scale deformation area, calculating the maximum cross-correlation coefficient, acquiring pixel level registration offset between a main image and an auxiliary image, processing the pixel level registration offset by a cubic spline interpolation method, acquiring sub-pixel level registration offset, calculating offset caused by topography fluctuation by using priori DEM data and track data, removing the offset, acquiring registration offset corresponding to the large-scale deformation, performing geographic coding, converting the registration offset into a deformation corresponding to a real geographic position, and taking the deformation as a large-scale surface deformation.
3. The method for refining the remote sensing of the surface deformation of the wide scale based on the depth fusion of the multidimensional electromagnetic information according to claim 1, wherein the fourth step comprises the following steps: and matching the high-frequency band coherence coefficient and the low-frequency band coherence coefficient with pixel points of the same name, setting a threshold value to restrict the difference of the coherence coefficients, primarily selecting a vegetation area, removing isolated pseudo vegetation points through median filtering, and identifying a real vegetation area.
4. The wide-scale earth surface deformation refined remote sensing method based on multidimensional electromagnetic information depth fusion of claim 3The method is characterized by further comprising the following steps: by Cor L (m, n) represents the coherence coefficient of the low-band pixel point coordinates (m, n), cor X (m, n) represents the coherence coefficient of the high-band pixel point coordinates (m, n), ΔCor (m, n) represents the difference of the coherence coefficients, threshold represents the set Threshold, p (m, n) represents the type of pixel point (m, n), 1 represents the vegetation region, 0 represents the non-vegetation region, and the formula is usedIndicating the high and low frequency band coherence joint constraint criteria.
5. The method for refining the remote sensing of the surface deformation of the wide scale based on the depth fusion of the multidimensional electromagnetic information according to claim 1, wherein the fifth step comprises the following steps: representing the unit complex vector of the polarized scattering mechanism by omega 12 Representing a non-hermitian complex coherent matrix () H Represents the conjugate transpose of the matrix, j represents the imaginary unit, and the formula is usedAnd calculating a complex coherence coefficient phase value, solving the maximum value, taking the complex coherence coefficient as a complex coherence coefficient with the maximized phase difference, and carrying out differential interference treatment on the interference phase of the center of the lowest scattering phase to obtain the medium-small scale surface deformation of the vegetation region.
6. The method for refining the remote sensing of the surface deformation of the wide scale based on the depth fusion of the multidimensional electromagnetic information according to claim 1, wherein the step six comprises the following steps: the deformation difference of adjacent pixels in the surface deformation is represented by delta R, lambda L Represents the wavelength of the low frequency band lambda X Represents the wavelength of the high frequency band lambda M Represents the equivalent wavelength after the frequency division of the high frequency band, eta represents the conversion coefficient and uses the formula、/>、/>The division principle of the low-frequency band application region, the frequency division interference application region and the high-frequency band application region is respectively represented.
7. The wide-scale earth surface deformation refined remote sensing method based on multidimensional electromagnetic information depth fusion according to claim 6, wherein the SqueeSAR algorithm under the polarization optimal coherence principle comprises the following steps: based on the polarization optimal coherence principle, PSIO processing is carried out on interference data, an optimal coherence criterion is established according to coherence amplitude information, an optimal scattering mechanism and highest coherence are obtained, and SqueseSAR processing is carried out.
8. The wide-scale earth surface deformation refined remote sensing method based on multidimensional electromagnetic information depth fusion according to claim 7, wherein the optimal coherence principle comprises the following steps: with omega opt Represents an optimal scattering state vector, T 11 And T 22 Respectively represent the polarization coherence matrix and omega corresponding to two interference images H And T represents an intermediate variable, phi represents phase, gamma opt Representing the optimal complex coherence coefficient by the formula、/>、/>Representing the optimal coherence criterion.
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