A differential SAR tomography deformation monitoring method of distributed scatterers

Abstract

The present application relates to the technical field of remote sensing image, in particular to a differential SAR tomography deformation monitoring method of distributed scatterers, comprising: step S1: for a target area, collecting the interference signal of the target area; step S2: the interference signal is pretreated to obtain a pretreated signal; step S3: the pretreated signal is reduced dimension to obtain a linear deformation rate model corresponding to the distributed scatterer; step S4: the linear deformation rate model is used to obtain the deformation rate corresponding to the distributed scatterer in the target area. The beneficial effect is that: the interference signal collected from the target area is processed to separate the linear deformation rate model corresponding to the distributed scatterer, which realizes better characterization of the distributed scatterer in the synthetic aperture radar image, and then the deformation rate of the distributed scatterer can be obtained according to the linear deformation rate model, which realizes better monitoring effect.

Description

Technical Field

[0001] This invention relates to the field of remote sensing image technology, and specifically to a differential SAR tomography deformation monitoring method for distributed scatterers. Background Technology

[0002] Ground deformation, a type of "gradually changing geological hazard," not only causes enormous damage to infrastructure but also inflicts significant losses on economic development, increasingly becoming a major constraint on the sustainable economic and social development of some regions. Interferometric Synthetic Aperture Radar (InSAR), a novel space measurement technique developed over the past forty years, possesses characteristics such as all-weather capability, high precision, and a certain degree of ground penetration. By utilizing phase information emitted by spaceborne or airborne radar, it can acquire spatial information on continuous surface displacement. Multibaseline InSAR, developed based on InSAR technology, is increasingly used in monitoring minute ground deformations. By jointly analyzing multiple InSAR interferometric images over a time series, the effects of atmospheric delay and spatiotemporal decorrelation can be effectively suppressed, thereby improving the accuracy and application range of ground deformation monitoring. In particular, its potential for millimeter-level measurement accuracy and continuous spatial coverage enable long-term ground deformation monitoring, making it a highly promising space-to-ground observation technology.

[0003] After years of development, multi-baseline InSAR technology has yielded a variety of techniques and methods, including interferogram stacking, permanent scatterer (PS), least square (LS), small baseline subset (SBAS), differential SAR tomography (DTomoSAR), multi-temporal InSAR (MTI), and multi-scale InSAR time series (MInTS). These technologies are based on different principles and address different problems, achieving many successful applications. In particular, differential SAR tomography or differential TomoSAR technology, based on SAR tomography (TomoSAR), forms a synthetic aperture in the tomographic-slant range deformation rate direction by using time-series multi-baseline InSAR datasets. By employing compressed sensing (CS) technology, it not only maintains the azimuth-range resolution but also achieves high-resolution focusing in the tomographic-slant range deformation rate direction, obtaining 4-dimensional spatial information of the target in the azimuth-range-tomographic-deformation rate direction. This enables high-resolution differential SAR tomography deformation monitoring of permanent scatterers (PS).

[0004] However, during implementation, the inventors discovered that CS-based differential SAR tomography focuses on permanent scatterers with relatively high coherence that constitute a small proportion of SAR images and remain relatively stable within a certain time range. Meanwhile, research on distributed scatterers (DS), which constitute a large proportion of SAR images and remain relatively stable within a certain time range with moderate coherence (lower than PS, but higher than water bodies and densely vegetated areas), is scarce. This results in existing SAR images being unable to effectively monitor the deformation of distributed scatterers with moderate coherence. Summary of the Invention

[0005] To address the aforementioned problems in existing technologies, a differential SAR tomography deformation monitoring method for distributed scatterers is provided.

[0006] The specific technical solution is as follows:

[0007] A method for detecting deformation in differential SAR tomography of distributed scatterers, comprising:

[0008] Step S1: For the target distribution area, acquire the temporal multi-baseline interferometric signal of the target distribution area;

[0009] Step S2: Preprocess the time-series multi-baseline interferometric signal to obtain a preprocessed signal;

[0010] Step S3: Dimensionally reduce the preprocessed signal to obtain a linear deformation rate model corresponding to the distributed scatterer;

[0011] Step S4: Obtain the deformation rate corresponding to the distributed scatterer in the target region based on the linear deformation rate model.

[0012] Preferably, step S1 includes:

[0013] Step S11: For the target distribution area, acquire multiple echo images from SAR satellites to form a time-series multi-baseline dataset;

[0014] Step S12: Select the main image from the time-series multi-baseline dataset, and use the remaining echo images as flight auxiliary images;

[0015] Step S13: Generate multiple sets of time-series multi-baseline interferometric signals based on the main image and all the flight auxiliary images.

[0016] Preferably, the time-series multi-baseline interferometric signal in step S13 is:

[0017]

[0018] In the formula, u0(R, s) is the main image, u m (R, s) represents the m-th flight auxiliary image, where m = (1, 2, ..., M), R is the slant range direction, v is the slant range deformation rate direction, and s is the tomography direction, which is perpendicular to the azimuth-range plane and has the following relationship with the altitude direction z: s p Let γ(R, s) be the p-th sampling point in the tomographic direction, where p = (1, 2, ..., P) and P >> M. p ) represents sampling point s p Scattering energy at the scattering point, φ m (R, s) p ) represents the path phase of different scattering targets in each observed pixel of the m-th flight auxiliary image, φ0(R, s) p φ represents the path phase of different scattering targets in each observed pixel of the main image. m(v) represents the target scattering phase of different scattering targets in each observed pixel of the m-th auxiliary flight image, and φ0(v) represents the target scattering phase of different scattering targets in each observed pixel of the main image.

[0019] Preferably, step S2 includes:

[0020] Step S21: Perform deskewing processing on the time-series multi-baseline interferometric signal to obtain a deskewing signal;

[0021] Step S22: Perform atmospheric phase compensation on the desloping signal to obtain the preprocessed signal.

[0022] Preferably, in step S21, the method for obtaining the de-skewed signal by de-skewing the interference signal includes:

[0023] Multiply the interference signal by the phase factor u dm (R, s) yields the descrambling signal g m ;

[0024] in,

[0025] In the formula, μ dm (R, s) is the phase factor, B / / m B is the horizontal baseline between the m-th aerial secondary image and the main image; ⊥m Let R be the vertical baseline between the m-th aerial auxiliary image and the main image, where R is the slant range and s is the tomographic direction.

[0026] The de-scratching signal includes:

[0027]

[0028] In the formula, g m For the descrambling signal, s p Let γ(R, s) be the p-th sampling point in the tomographic direction, where p = (1, 2, ..., P) and P >> M. p ) represents sampling point s p Scattering energy at point B ⊥m Let R be the vertical baseline between the m-th aerial image and the main image, λ be the slant range, λ be the wavelength, R0 be the slant range of the main image to the target area, and v be the slant range deformation rate. The deformation phase is in the direction of the ground slant range deformation rate; The phase of the atmospheric term; This is the error phase.

[0029] Preferably, in step S22, the method for obtaining the atmospheric phase-compensated signal by performing atmospheric phase compensation on the desloping signal includes:

[0030]

[0031] In the formula, g m For the atmospheric phase compensation signal, s p Let γ(R, s) be the p-th sampling point in the tomographic direction, where p = (1, 2, ..., P) and P >> M. p ) represents sampling point s p Scattering energy at point B ⊥m Let R be the vertical baseline between the m-th auxiliary image and the main image, where R is the slant range direction and v is the slant range deformation rate direction. e represents the deformation phase along the ground slant range deformation rate direction. m This is residual noise.

[0032] Preferably, in step S3, the differential SAR tomography linear deformation rate model obtained from the dimensionality reduction processing of the preprocessed signal includes:

[0033]

[0034] In the formula, For the differential SAR tomography linear deformation rate model, exp(-j2πη) M v Q ) represents the deformation phase under the sampling of the impact function, and η is the deformation phase. M For time variables, Where λ is the wavelength, T m Let γ1 = γ(R, s1) be the temporal baseline between the m-th passing auxiliary image and the main image, where R is the slant range direction and s1 is the tomographic sampling point after dimensionality reduction. Q For the slant range deformation rate towards the sampling point, δ(v) Q -v) is the Dikra function, v is the slant range deformation rate, and e M This is residual noise.

[0035] Preferably, in step S4, the method for obtaining the deformation rate based on the linear deformation rate model includes:

[0036]

[0037] In the formula, γ QL Let G be the target matrix. ML Let e ​​be the interference value of the Lth interference signal. ML Let φ be the residual noise of the Lth interferometric signal, φ be the observation matrix, and γ be the residual noise of the Lth interferometric signal. l Let L be the column vector of scattered energy corresponding to the Lth interference signal.

[0038] The above technical solution has the following advantages or beneficial effects: by processing the interferometric signals collected from the target area, a linear deformation rate model corresponding to the distributed scatterer is obtained, which realizes a better characterization of the distributed scatterer in the synthetic aperture radar image. Then, the deformation rate of the distributed scatterer can be obtained according to the linear deformation rate model, thus achieving a better monitoring effect. Attached Figure Description

[0039] Embodiments of the invention will be described more fully with reference to the accompanying drawings. However, the drawings are for illustration and explanation only and do not constitute a limitation on the scope of the invention.

[0040] Figure 1 This is an overall schematic diagram of an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of sub-step S1 in an embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram of step S2 in an embodiment of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0045] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0046] This invention includes:

[0047] A method for detecting deformation in differential SAR tomography of distributed scatterers, such as Figure 1 As shown, it includes:

[0048] Step S1: For the target distribution area, acquire the temporal multi-baseline interferometric signal of the target distribution area;

[0049] Step S2: Preprocess the time-series multi-baseline interferometric signal to obtain the preprocessed signal;

[0050] Step S3: Reduce the dimension of the preprocessed signal to obtain a linear deformation rate model corresponding to the distributed scatterer;

[0051] Step S4: Obtain the deformation rate corresponding to the scatterers distributed in the target region based on the linear deformation rate model.

[0052] Specifically, in order to achieve a better monitoring effect on the deformation of distributed scatterers, in this embodiment, multiple echo images of the target area are pre-acquired and combined into an interference signal. The interference signal is then processed and analyzed to obtain a linear deformation rate model for characterizing the distributed scatterers, thereby obtaining the deformation rate of the distributed scatterers and achieving a better monitoring effect.

[0053] In implementation, the aforementioned differential SAR tomography deformation monitoring method is set as a software embodiment in a specific computing device, which receives echo images of the target area scanned by remote sensing satellites through a software interface. The interferometric signal is an interferometric signal obtained based on or processed from the echo image, containing four-dimensional spatial information in the target azimuth, range, tomography, and deformation rate directions, enabling effective characterization of the target area. The preprocessed signal refers to the interferometric signal after preprocessing operations, mainly including deskewing correction and atmospheric phase compensation. The linear deformation rate model refers to the interferometric signal matrix obtained by reducing the dimensionality of the preprocessed signal based on the characteristics of the distributed scatterers, derived from scattering targets at approximately the same ground elevation, which can effectively characterize the distributed scatterers on the ground. Distributed scatterers refer to land features that constitute a large proportion of SAR images, such as bare land, idle farmland, barren mountains, deserts, low grasslands / shrubs, concrete surfaces, roads, and rooftops. Their physical characteristics, such as surface roughness, complex dielectric constant, and backscattering coefficient, are highly consistent, and they maintain uniformity within a certain spatial range and relative stability within a certain time range.

[0054] In a preferred embodiment, such as Figure 2 As shown, step S1 includes:

[0055] Step S11: For the target area, acquire multiple echo images from SAR satellites to form a time-series multi-baseline dataset;

[0056] Step S12: Select the main image from the time-series multi-baseline dataset and use the remaining echo images as auxiliary images for flight;

[0057] Step S13: Generate multiple sets of time-series multi-baseline interferometric signals based on the main image and all the flight over auxiliary images.

[0058] Specifically, to achieve better monitoring of distributed scatterers, this embodiment uses remote sensing satellites to acquire echo images of the target area and generates multiple sets of echo images with time sequences as a dataset. Subsequently, based on parameters such as azimuth, range, and time sequence, one echo image is selected as the primary image, and the remaining echo images are used as secondary images, i.e., flyby secondary images. Interference processing is then performed on the primary and secondary images to generate interference signals, with the number of interference signals matching the number of flyby secondary images.

[0059] As an optional implementation, after step S12 and before step S13, the main image and each flight auxiliary image are registered separately.

[0060] In a preferred embodiment, the interference signal in step S13 is:

[0061]

[0062] In the formula, u0(R, s) is the main image, u m (R, s) represents the m-th flight overlay image, where m = (1, 2, ..., M), R is the slant range direction, v is the slant range deformation rate direction, and s is the tomography direction. The tomography direction is perpendicular to the azimuth-range plane and is related to the altitude direction z as follows: s p Let γ(R, s) be the p-th sampling point in the tomographic direction, where p = (1, 2, ..., P) and P >> M. p ) represents sampling point s p Scattering energy at the scattering point, φ m (R, s) p Let φ0(R, s) represent the path phase of different scattering targets in each observed pixel of the m-th flight auxiliary image. p ) is the path phase of different scattering targets in each observed pixel of the main image, φ m (v) represents the target scattering phase of different scattering targets in each observed pixel of the m-th flight auxiliary image, and φ0(v) represents the target scattering phase of different scattering targets in each observed pixel of the main image.

[0063] In the implementation process, assuming ideal focusing in the azimuth-range direction of the time-series multi-baseline InSAR dataset, a synthetic aperture is formed in the tomographic-slant range deformation rate direction of the time-series dataset, considering multiple scattering targets γ1, γ2, ..., γ p By measuring the slant range deformation rate or settling rate of multiple scattering targets at different altitudes, four-dimensional spatial information in the target azimuth-range-tomography-deformation rate direction is obtained. Subsequently, for each set of main images and flight auxiliary images, the interferometric signal described above can be represented.

[0064] In a preferred embodiment, such as Figure 3 As shown, step S2 includes:

[0065] Step S21: Perform deskewing processing on the time-series multi-baseline interferometric signal to obtain the deskewing signal;

[0066] Step S22: Perform atmospheric phase compensation on the deslope signal to obtain a preprocessed signal.

[0067] Specifically, in order to achieve better analysis results, in this embodiment, after generating the interference signals, for each set of interference signals, the secondary phase curvature of the focused signal is removed by a deskewing step, and the atmospheric phase image is removed by atmospheric phase compensation, thereby achieving better processing results in subsequent processes.

[0068] In a preferred embodiment, step S21, the method for obtaining a de-skewed signal by de-skewing the interference signal, includes:

[0069] Multiply the interference signal by the phase factor u dm (R, s) yields the descrambling signal g m ;

[0070] in,

[0071] In the formula, μ dm (H, s) is the phase factor, B / / m B is the horizontal baseline between the m-th aerial secondary image and the main image; ⊥m Let R be the vertical baseline between the m-th aerial auxiliary image and the main image, where R is the slant range and s is the tomographic direction.

[0072] Based on the phase factor mentioned above, the two-dimensional focusing data can be adjusted as follows:

[0073]

[0074] Because of B / / m << R0, therefore the two-dimensional focusing data in the above formula can be approximated as:

[0075]

[0076] After further subdividing the interference phase in the above two-dimensional focused data, the final output de-skew signal includes:

[0077] The de-slope signal includes:

[0078]

[0079] In the formula, g m For descrambling signal, s pLet γ(R, s) be the p-th sampling point in the tomographic direction, where p = (1, 2, ..., P) and P >> M. p ) represents sampling point s p Scattering energy at point B ⊥m Let R be the vertical baseline between the m-th auxiliary image and the main image, λ be the slant range, λ be the wavelength, R0 be the slant range of the main image to the target area, and v be the slant range deformation rate. The deformation phase is in the direction of the ground slant range deformation rate; The phase of the atmospheric term; This is the error phase.

[0080] In a preferred embodiment, step S22, the method for obtaining an atmospheric phase-compensated signal by performing atmospheric phase compensation on the deslope signal includes:

[0081]

[0082] In the formula, g m For atmospheric phase compensation signal, s p Let γ(R, s) be the p-th sampling point in the tomographic direction, where p = (1, 2, ..., P) and P >> M. p ) represents sampling point s p Scattering energy at point B ⊥m Let R be the vertical baseline between the m-th auxiliary image and the main image, where R is the slant range direction and v is the slant range deformation rate direction. e represents the deformation phase along the ground slant range deformation rate direction. m This is residual noise.

[0083] Specifically, in order to better eliminate the influence of atmospheric phase on the echo signal, this embodiment also achieves high-precision atmospheric phase compensation by utilizing multiple external data sources such as ground-based station measurements, remote sensing satellite inversion, and meteorological model predictions, based on the deslant signal.

[0084] In a preferred embodiment, step S3, based on the differential SAR tomography linear deformation rate model obtained from the dimensionality reduction processing of the preprocessed signal, includes:

[0085]

[0086] In the formula, For differential SAR tomography linear deformation rate model, exp(-j2πη) M v Q ) represents the deformation phase under the sampling of the impact function, and η is the deformation phase. M For time variables, Where λ is the wavelength, T mLet γ1 = γ(R, s1) be the temporal baseline between the m-th passing auxiliary image and the main image, where R is the slant range direction and s1 is the tomographic sampling point after dimensionality reduction. Q For the slant range deformation rate towards the sampling point, δ(v) Q -v) is the Dikra function, v is the slant range deformation rate, and e M This is residual noise.

[0087] Specifically, since distributed scatterers have high consistency in physical properties such as surface roughness, complex dielectric constant, and backscattering coefficient, and maintain uniformity of ground features within a certain spatial range and relative stability within a certain time range, it can be assumed that pixels in the same type of distributed scatterer region have the same distribution characteristics. Therefore, for distributed scatterer points, each azimuth-slant range resolution cell in the m-th flight pass auxiliary image usually contains only scattering targets from approximately the same ground elevation.

[0088] Therefore, after obtaining the preprocessed signal, it can be further assumed that:

[0089] P=1, meaning that only one dimension of the sampling point is retained in the tomographic direction, denoted as s. p | P＝1 =s1, then the above preprocessed signal can be reduced in dimensionality to obtain

[0090]

[0091] remember This can then be further simplified to:

[0092]

[0093] Under the linear deformation rate model, deformation phase It can be written as:

[0094]

[0095] In the formula, T m is the time baseline between the m-th passing auxiliary image and the main image, and v is the linear slant range deformation rate at sampling point s1.

[0096] This can then be further simplified to

[0097]

[0098] When v represents the sampled signal of the impulse function, then:

[0099]

[0100] In the formula, T m Let v be the temporal baseline between the m-th passing auxiliary image and the main image, and v be the linear slant range deformation rate at sampling point s1.q For the slant range deformation rate towards the sampling point, δ(v) q -v) is the Diclave function.

[0101] make Then we have:

[0102]

[0103] Subsequently, substituting the M interference values ​​into the above equation yields the signal matrix used to characterize the linear deformation rate.

[0104] In a preferred embodiment, step S4, the method for obtaining the deformation rate based on the linear deformation rate model, includes:

[0105]

[0106] In the formula, γ QL Let G be the target matrix. ML Let e ​​be the interference value of the Lth interference signal. ML Let φ be the residual noise of the Lth interferometric signal, φ be the observation matrix, and γ be the residual noise of the Lth interferometric signal. l Let L be the column vector of scattered energy corresponding to the Lth interference signal.

[0107] Specifically, distributed scatterers maintain uniformity of ground features within a certain spatial range and relative stability within a certain time range, and the deformation rate of the ground surface varies very little within a certain range. Therefore, it can be assumed that pixels in the same type of distributed scatterer region within a certain range also have the same deformation rate. Considering the influence of factors such as atmospheric delay, regions of the same type of distributed scatterer within a range of less than 2000m can generally be considered to have the same atmospheric delay effect. In this case, the corresponding region is smaller than L = N. x ×N y ,in and ρ represents the magnitude of the region in the azimuth and distance directions. x and ρ y Here, the azimuth resolution and the ground range resolution are given; therefore, the phase information of the distributed scatterer region can be approximately considered as the phase information of the entire region, and the joint matrix form of the L signals in the distributed scatterer region is:

[0108]

[0109] The above formula can be further simplified to G ML =φ·γ QL +e ML Its target signal γ QL Satisfying sparse representationability and identical sparse structure as well as the observation matrix Φ v The conditions for satisfying the RIP criterion are met, therefore GML =φ·γ QL +e ML Solving this problem is equivalent to solving the problem of Multiple Measurement Vectors-Compressive Sensing (MMV-CS), which leads to the reconstruction of the deformation rate based on the following equation:

[0110]

[0111] In the formula, γ QL Let G be the target matrix. ML Let φ be the interference value of the Lth interference signal, and φ be the observation matrix.

[0112] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting deformation in differential SAR tomography of distributed scatterers, characterized in that, include: Step S1: For the target distribution area, acquire the temporal multi-baseline interferometric signal of the target distribution area; Step S2: Preprocess the time-series multi-baseline interferometric signal to obtain a preprocessed signal; Step S3: Dimensionally reduce the preprocessed signal to obtain a linear deformation rate model corresponding to the distributed scatterer; Step S4: Obtain the deformation rate of the distributed scatterer corresponding to the distributed target region based on the linear deformation rate model; In step S3, the differential SAR tomography linear deformation rate model obtained by dimensionality reduction of the preprocessed signal includes: ； In the formula, This is the linear deformation rate model for differential SAR tomography. The deformation phase is sampled under the impact function. For time variables, ,in For wavelength, The time baseline between the m-th passing auxiliary image and the main image. = , For slant range, For the tomographic sampling points after dimensionality reduction, For the slant range deformation rate towards the sampling point, ) represents the Diclave function. For the slant range deformation rate direction, This is residual noise; The differential SAR tomography linear deformation rate model is constructed for scattering targets that are approximately at the same ground elevation within each azimuth-slant range resolution cell.

2. The differential SAR tomography deformation monitoring method according to claim 1, characterized in that, Step S1 includes: Step S11: For the target distribution area, acquire multiple echo images from SAR satellites to form a time-series multi-baseline dataset; Step S12: Select the main image from the time-series multi-baseline dataset, and use the remaining echo images as flight auxiliary images; Step S13: Generate multiple sets of time-series multi-baseline interferometric signals based on the main image and all the flight auxiliary images.

3. The differential SAR tomography deformation monitoring method according to claim 2, characterized in that, The time-series multi-baseline interferometric signal in step S13 is: ； In the formula, The main image, For the first Axis image over auxiliary image, , For slant range, For the slant range deformation rate direction, The tomography direction is perpendicular to the azimuth-range plane and is parallel to the height direction. The relationship is , For the first in the tomographic direction One sampling point, ,and , Sampling points Scattering energy at the scattering point Let m be the path phase of different scattering targets in each observed pixel of the aforementioned flight auxiliary image. This refers to the path phase of different scattering targets in each observed pixel of the main image. For the first In the aforementioned flight auxiliary image, the target scattering phase of different scattering targets in each observed pixel is... The target scattering phase is the target scattering phase of different scattering targets in each observed pixel of the main image.

4. The differential SAR tomography deformation monitoring method according to claim 1, characterized in that, Step S2 includes: Step S21: Perform deskewing processing on the time-series multi-baseline interferometric signal to obtain a deskewing signal; Step S22: Perform atmospheric phase compensation on the desloping signal to obtain the preprocessed signal.

5. The differential SAR tomography deformation monitoring method according to claim 4, characterized in that, In step S21, the method for obtaining the de-skewed signal by de-skewing the interference signal includes: Multiply the interference signal by a phase factor The de-skewing signal is obtained. ; in, ; In the formula, The phase factor, For the first The horizontal baseline of the secondary image and the main image; For the first The vertical baseline of the secondary image and the main image is crossed. For slant range, For chromatography; The de-scratching signal includes: ; In the formula, The descrambling signal, For the first in the tomographic direction One sampling point, ,and , Sampling points Scattering energy at the scattering point Let m be the vertical baseline between the secondary and primary images. For slant range, For wavelength, The slant distance of the main image to the target distribution area is given. For the slant range deformation rate direction, The deformation phase is in the direction of the ground slant range deformation rate; The phase of the atmospheric term; This is the error phase.

6. The differential SAR tomography deformation monitoring method according to claim 4, characterized in that, In step S22, the method for obtaining the atmospheric phase-compensated signal by performing atmospheric phase compensation on the deskewing signal includes: ； In the formula, This refers to the atmospheric phase compensation signal. For the first in the tomographic direction One sampling point, ,and , Sampling points Scattering energy at the scattering point Let m be the vertical baseline between the secondary and primary images. For slant range, For the slant range deformation rate direction, The deformation phase is the direction of the ground slant range deformation rate. This is residual noise.

7. The differential SAR tomography deformation monitoring method according to claim 1, characterized in that, In step S4, the method for obtaining the deformation rate based on the linear deformation rate model includes: ； In the formula, For the target matrix, For the first The interference value of each interference signal. For the first The residual noise of each interference signal, denoted as the observation matrix. Let be the column vector of scattered energy corresponding to the i-th interference signal.

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