A Phase Unwrapping Method for InSAR Interferometry in Complex Terrain Regions

Abstract

The present invention relates to an InSAR interferometric phase unwrapping method for a region with complex terrain, comprising: obtaining SAR image data of a target region; obtaining integer cycle differences between adjacent pixels; calculating integer cycles; and obtaining unwrapped phase results. The present invention utilizes InSAR technology to improve the accuracy of interferometric measurement, and can accurately perform interferometric phase unwrapping regardless of the complexity of the terrain, greatly expanding the measurement range of traditional interferometric radar.

Description

Technical Field

[0001] The invention relates to the technical field of remote sensing image processing, and in particular to an InSAR interferometric phase unwrapping method for a region with complex terrain. Background Art

[0002] The core of InSAR technology for monitoring surface deformation is to restore the geometric and deformation characteristics of ground monitoring targets by acquiring the phase information between the radar and the ground targets, and it is highly sensitive to small surface deformations. Due to the changes in the radar's two observation conditions, including the radar orbit deviation, the change in the backscattering characteristics of the ground objects, and the influence of noise, the coherence of the radar signals of the ground targets obtained twice is reduced, making it difficult to complete the phase unwrapping of the radar targets with discrete spatial distribution. In addition, the baseline error of the radar satellite, the inaccuracy of the external DEM data introduced, and the influence of atmospheric fluctuations at the time of the two observations of the radar satellite, all these factors will reduce the accuracy of the results of InSAR technology monitoring surface deformation. To address this problem, there are currently two commonly used solutions: one is the traditional synthetic aperture radar differential interferometry technology (D-InSAR); the other is based on the time series analysis technology of coherent point targets with stable scattering characteristics.

[0003] Due to the periodicity of trigonometric functions, the interference phase is entangled between (-π, π] and cannot reflect the actual ground elevation. The process of restoring the entangled phase to the real phase corresponding to the terrain elevation is called phase unwrapping. Ideally, the purpose of phase unwrapping can be achieved by extracting the phase gradients in the azimuth and range directions, and then integrating the gradients along the azimuth and range directions. However, due to the influence of factors such as terrain slope undersampling, overlap, shadow, and low signal-to-noise ratio, the actual data will cause discontinuity of the interference phase data, thereby causing phase unwrapping errors. The complexity of the interference phase makes phase unwrapping always a difficult and hot topic in research.

[0004] In the early InSAR technology system, phase unwrapping is often based on a single baseline. Such technologies are based on the phase continuity assumption. However, in areas with large terrain fluctuations such as mountainous areas, the phase continuity assumption does not hold. Therefore, the early phase unwrapping technology based on a single baseline is prone to phase jumps in areas with large terrain fluctuations such as mountainous areas, and high-precision true phase results cannot be obtained. To this end, researchers have developed some phase unwrapping methods based on multiple baselines in recent years, so that the phase unwrapping process no longer relies on the phase continuity assumption, and has achieved certain results. However, the existing multi-baseline phase unwrapping method considers a new assumption, that is, it is assumed that the surface has not undergone significant deformation during the acquisition of time series SAR images. Obviously, this assumption is generally satisfied, but in areas with more artificial activities or complex geological conditions, the surface often deforms over time, and the assumption cannot be established, which leads to defects in the existing methods and the accuracy needs to be further improved. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to propose an InSAR interferometric phase unwrapping method for complex terrain areas. By using InSAR technology, interferometric phase unwrapping can be accurately performed, which greatly expands the measurement range of traditional interferometric radars. The unwrapping efficiency is high, and the defects caused by the inapplicability of the phase continuity assumption and the invariant surface elevation assumption in traditional algorithms are avoided, thereby improving the phase unwrapping accuracy.

[0006] The purpose of the present invention is achieved by adopting the following technical solutions:

[0007] The present invention provides an InSAR interferometric phase unwrapping method for a region with complex terrain, the improvement of which is as follows:

[0008] Step 1, obtaining SAR image data of the target area;

[0009] Step 2, find the integer difference between adjacent pixels;

[0010] Step 3, calculate the number of whole weeks;

[0011] Step 4: Obtain the unwrapped phase result.

[0012] Preferably, the step 1, obtaining SAR image data, specifically includes:

[0013] Acquire N SAR images in time sequence, where the first SAR image is the primary image and the remaining N-1 SAR images are secondary images, and register the N-1 pairs of primary and secondary images.

[0014] Preferably, the step 2 of obtaining the integer number difference between adjacent pixels specifically includes:

[0015] Step 2-1, the first SAR main image and the remaining N-1 SAR secondary images are sequentially composed of N-1 SAR interferometric image pairs;

[0016] Step 2-2: Since the first and second SAR images were taken very close to each other, the surface deformation during that period was very small and could be ignored. However, the surface deformation from the third image to the first SAR image could not be ignored.

[0017] For each interferometric image pair, a first relationship model of the surface elevation deformation from the N-1 scene secondary image to the 1st scene primary image and the absolute phase and integer number corresponding to the sth pixel is established according to the time sequence:

[0018]

[0019] in, represents the absolute phase corresponding to the sth pixel in the ith interference image pair; k i (s)(i=1,2,…,N-1) represents the integer number of cycles corresponding to the sth pixel in the i-th interferometric image pair; B i (i=1,2,…,N-1) represents the vertical baseline length of the i-th interferometric image pair; Δh m (s)(m=2,3,…,N-1) represents the surface elevation deformation that occurred when the m+1th SAR image was taken relative to the first SAR image; λ represents wavelength;

[0020] Step 2-3, for each interferometric image pair, a second relationship model of the surface elevation deformation from the N-1 scene secondary image to the 1st scene main image and the absolute phase and integer number corresponding to the s-1th pixel is established according to the time sequence, and the s-1th pixel represents the adjacent pixel of the sth pixel:

[0021]

[0022] Step 2-4, since the s-1th pixel and the s-th pixel are adjacent pixels, let the elevation difference between them Δh(s)-Δh(s-1)=0, combine the first relationship model and the second relationship model, and for each interference image pair, establish a third relationship model of the absolute phase difference and the integer cycle difference between the s-th pixel and the s-1th pixel according to the time sequence:

[0023]

[0024] in, represents the absolute phase difference between the sth pixel and the s-1th pixel in the i-th interference image pair, Δk i (s,s-1)=k i(s)-k i (s-1)(i=1,2,…,N-1), represents the integer difference between the sth pixel and the s-1th pixel in the i-th interferometric image pair;

[0025] Step 2-5, to obtain Δk i (s,s-1), convert the third relational model into the optimal solution model:

[0026]

[0027] Among them, i,j=1,2,…,N-1 and are different from each other;

[0028] Step 2-6, find the optimal solution of the optimal solution model and obtain the integer difference Δk between adjacent pixels i (s,s-1).

[0029] Preferably, the step 3, calculating the integer number k, specifically includes:

[0030] Step 3-1, determine the coherence coefficient diagram: let the primary and secondary images of the i-th interferometric image pair, i.e., the 1st and i+1th SAR images, be S 1 and S 2 , establish the coherence coefficient diagram of the i-th interference image pair, expressed as: Where, E() represents the expected value; * represents conjugation; represents the conjugate complex number of S2;

[0031] Step 3-2, coherence coefficient map segmentation: mark the residual points in the coherence coefficient map and determine the distribution of the residual points in the coherence coefficient map, determine the correlation coefficient value corresponding to the residual point on the coherence coefficient map, use the maximum correlation coefficient value among all the residual points as the threshold for segmenting data, and segment the coherence coefficient map and the corresponding winding phase map into high-quality areas and low-quality areas;

[0032] Step 3-3, phase unwrapping of high-quality regions;

[0033] Step 3-4, phase unwrapping of low-mass regions.

[0034] Furthermore, the step 3-3, phase unwrapping of high-quality regions, specifically includes:

[0035] Step 3-3-1, obtaining GPS information of any point in the high-quality area of ​​the winding phase diagram;

[0036] Step 3-3-2, using the GPS information to invert and obtain the integer number corresponding to the point;

[0037] Step 3-3-3, perform integer integration on the high-quality area to obtain the integer corresponding to each pixel in the high-quality area.

[0038] Furthermore, the step 3-4, phase unwrapping of the low-quality region, specifically includes:

[0039] Step 3-4-1, classify the original SAR image according to the range of the low-quality area to obtain X types of ground object categories, and mark the range of the X types of ground object categories in the low-quality area of ​​the interference fringe pattern;

[0040] Step 3-4-2, select 2 GPS points within the range of each type of land feature, and select 2X GPS points in total. For each GPS point, use GPS information to invert and obtain the corresponding integer number;

[0041] Step 3-4-3, establish a fitting function f(x,y) to fit the integer number k of any point:

[0042] f(x,y)=a 0 +a 1 x+a 2 y+a 3 x 2 +a 4 xy+a 5 y 2 , where a 0 , a 1 , a 2 , a 3 , a 4 , a 5 is the fitting coefficient,

[0043] Step 3-4-4, determine the mean square error E of the fitting function f(x,y):

[0044] Among them, k i,j Indicates the integer week number corresponding to the GPS point in the i-th row and j-th column, x i,j and i.j are the row and column values ​​of the pixels in the i-th row and j-th column respectively;

[0045] Step 3-4-5, minimize the mean square error E to obtain the coefficients {a i};

[0046] Step 3-4-6, use the fitting function f(x,y) to calculate the integer number corresponding to each pixel in the low-quality area.

[0047] Furthermore, the step 4, obtaining the unwrapped phase result, specifically includes:

[0048] Step 4-1: According to the integer number k corresponding to each pixel in the high-quality area h The number of integer cycles k corresponding to each pixel in the low-quality area l , get the integer number k corresponding to each pixel in the entire area i (s);

[0049] Step 4-2, establish the phase unwrapping model: Calculate the unwrapped phase result, where represents the absolute phase corresponding to the sth pixel in the ith interference image pair, ψ i (s) represents the unwrapped phase result of the s-th pixel in the interference image pair i.

[0050] Furthermore, in step 2-6, a projection method is used to obtain the optimal solution of the optimal solution model.

[0051] Compared with the closest prior art, the present invention has the following beneficial effects:

[0052] First, the present invention utilizes InSAR technology to improve the accuracy of interferometric measurement, can accurately perform interferometric phase unwrapping, and greatly expands the measurement range of traditional interferometric radar. Secondly, the present invention provides a phase unwrapping method, which further improves the quality of unwrapped phase while ensuring the success rate of phase unwrapping, and does not require complex phase time-space calculations, and has high unwrapping efficiency. Finally, the present invention is particularly suitable for high-precision phase unwrapping in areas with large terrain undulations (such as mountainous areas) and frequent surface deformations (such as artificial activity areas). It can avoid the defects caused by the inapplicability of the phase continuity assumption and the surface elevation invariance assumption in traditional algorithms, improve the phase unwrapping accuracy, and then improve the surface elevation deformation inversion accuracy, providing a high-precision monitoring means for geological disasters along the transmission lines in the regional range. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Basic flow chart of the method of the present invention;

[0054] Figure 2 An example diagram of phase unwrapping in the low-quality region of the present invention;

[0055] Figure 3 Comparison diagram of the phase unwrapping results of the present invention. DETAILED DESCRIPTION

[0056] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Example

[0059] The present invention provides an InSAR interferometric phase unwrapping method for complex terrain areas. Figure 1 As shown, including:

[0060] Step 1, obtaining SAR image data of the target area;

[0061] Step 2, find the integer difference between adjacent pixels;

[0062] Step 3, calculate the number of whole weeks;

[0063] Step 4: Obtain the unwrapped phase result.

[0064] Preferably, the step 1, obtaining SAR image data, specifically includes:

[0065] Acquire N SAR images in time sequence, where the first SAR image is the primary image and the remaining N-1 SAR images are secondary images, and register the N-1 pairs of primary and secondary images.

[0066] Preferably, the step 2 of obtaining the integer number difference between adjacent pixels specifically includes:

[0067] Step 2-1, the first SAR main image and the remaining N-1 SAR secondary images are sequentially composed of N-1 SAR interferometric image pairs;

[0068] Step 2-2: Since the first and second SAR images were taken very close to each other, the surface deformation during that period was very small and could be ignored. However, the surface deformation from the third image to the first SAR image could not be ignored.

[0069] For each interferometric image pair, a first relationship model of the surface elevation deformation from the N-1 scene secondary image to the 1st scene primary image and the absolute phase and integer number corresponding to the sth pixel is established according to the time sequence:

[0070]

[0071] in, represents the absolute phase corresponding to the sth pixel in the ith interference image pair; k i(s)(i=1,2,…,N-1) represents the integer number of cycles corresponding to the sth pixel in the i-th interferometric image pair; B i (i=1,2,…,N-1) represents the vertical baseline length of the i-th interferometric image pair; Δh m (s)(m=2,3,…,N-1) represents the surface elevation deformation that occurred when the m+1th SAR image was taken relative to the first SAR image; λ represents wavelength;

[0072] Step 2-3, for each interferometric image pair, a second relationship model of the surface elevation deformation from the N-1 scene secondary image to the 1st scene main image and the absolute phase and integer number corresponding to the s-1th pixel is established according to the time sequence, and the s-1th pixel represents the adjacent pixel of the sth pixel:

[0073]

[0074] Step 2-4, since the s-1th pixel and the s-th pixel are adjacent pixels, let the elevation difference between them Δh(s)-Δh(s-1)=0, combine the first relationship model and the second relationship model, and for each interference image pair, establish a third relationship model of the absolute phase difference and the integer cycle difference between the s-th pixel and the s-1th pixel according to the time sequence:

[0075]

[0076] in, represents the absolute phase difference between the sth pixel and the s-1th pixel in the i-th interference image pair, Δk i (s,s-1)=k i (s)-k i (s-1)(i=1,2,…,N-1), represents the integer difference between the sth pixel and the s-1th pixel in the i-th interferometric image pair;

[0077] Step 2-5, to obtain Δk i (s,s-1), convert the third relational model into the optimal solution model:

[0078]

[0079] Among them, i,j=1,2,…,N-1 and are different from each other;

[0080] Step 2-6, find the optimal solution of the optimal solution model and obtain the integer difference Δk between adjacent pixels i (s,s-1).

[0081] Preferably, the step 3, calculating the integer number k, specifically includes:

[0082] Step 3-1, determine the coherence coefficient diagram: let the primary and secondary images of the i-th interferometric image pair, i.e., the 1st and i+1th SAR images, be S 1 and S 2 , establish the coherence coefficient diagram of the i-th interference image pair, expressed as: Where, E() represents the expected value; * represents conjugation; represents the conjugate complex number of S2;

[0083] Step 3-2, coherence coefficient map segmentation: mark the residual points in the coherence coefficient map and determine the distribution of the residual points in the coherence coefficient map, determine the correlation coefficient value corresponding to the residual point on the coherence coefficient map, use the maximum correlation coefficient value among all the residual points as the threshold for segmenting data, and segment the coherence coefficient map and the corresponding winding phase map into high-quality areas and low-quality areas;

[0084] Step 3-3, phase unwrapping of high-quality regions;

[0085] Step 3-4, phase unwrapping of low-mass regions.

[0086] Specifically, the step 3-3, phase unwrapping of the high-quality region, specifically includes:

[0087] Step 3-3-1, obtaining GPS information of any point in the high-quality area of ​​the winding phase diagram;

[0088] Step 3-3-2, using the GPS information to invert and obtain the integer number corresponding to the point;

[0089] Step 3-3-3, perform integer integration on the high-quality area to obtain the integer corresponding to each pixel in the high-quality area.

[0090] Specifically, the phase unwrapping of the low-quality region in step 3-4 specifically includes:

[0091] Step 3-4-1, classify the original SAR image according to the range of the low-quality area to obtain X types of ground object categories, and mark the range of the X types of ground object categories in the low-quality area of ​​the interference fringe pattern;

[0092] Step 3-4-2, select 2 GPS points within the range of each type of land feature, and select 2X GPS points in total. For each GPS point, use GPS information to invert and obtain the corresponding integer number;

[0093] Step 3-4-3, establish a fitting function f(x,y) to fit the integer number k of any point:

[0094] f(x,y)=a 0 +a 1 x+a2 y+a 3 x 2 +a 4 xy+a 5 y 2 , where a 0 , a 1 , a 2 , a 3 , a 4 , a 5 is the fitting coefficient,

[0095] Step 3-4-4, determine the mean square error E of the fitting function f(x,y):

[0096] Among them, k i,j Indicates the integer week number corresponding to the GPS point in the i-th row and j-th column, x i,j and i.j are the row and column values ​​of the pixels in the i-th row and j-th column respectively;

[0097] Step 3-4-5, minimize the mean square error E to obtain the coefficients {a i};

[0098] Step 3-4-6, use the fitting function f(x,y) to calculate the integer number corresponding to each pixel in the low-quality area.

[0099] Figure 2 An example of phase unwrapping in low-quality areas is given. The left figure shows the classification of objects in the original SAR image, and the right figure shows the calibration of the low-quality area range of the corresponding interference fringe pattern. In the right figure, we can see that the area with speckle noise is a low-quality area. According to the range of the low-quality area, the original SAR image is classified to obtain different types of objects, which are mainly water bodies here (left figure). Then, two GPS points are selected within the water body range of the interference fringe pattern. On this basis, the corresponding integer number is inverted for each GPS point, and subsequent fitting is performed.

[0100] Preferably, the step 4, obtaining the unwrapped phase result, specifically comprises:

[0101] Step 4-1: According to the integer number k corresponding to each pixel in the high-quality area h The number of integer cycles k corresponding to each pixel in the low-quality area l , get the integer number k corresponding to each pixel in the entire area i (s);

[0102] Step 4-2, establish the phase unwrapping model: Calculate the unwrapped phase result, where represents the absolute phase corresponding to the sth pixel in the ith interference image pair, ψ i (s) represents the unwrapped phase result of the s-th pixel in the interference image pair i.

[0103] Figure 3 The figure shows the phase unwrapping results of a mountainous area. The left figure is the result obtained by using the traditional algorithm, and the right figure is the result obtained by using the method of the present invention. It can be seen that there is an obvious phase jump in the white frame in the left figure; while the corresponding area in the right figure is relatively smooth, and the error is significantly reduced.

[0104] Specifically, in step 2-6, the optimal solution of the optimal solution model is obtained by using the projection method.

[0105] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0106] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An InSAR interferometric phase unwrapping method for complex terrain areas, characterized in that: include: Step 1, obtaining SAR image data of the target area; Step 2, find the integer difference between adjacent pixels; Step 3, calculate the number of whole weeks; Step 4, obtaining the unwrapped phase result; Wherein, the step 2, obtaining the integer difference between adjacent pixels, specifically includes: Step 2-1, the first SAR main image and the remaining N-1 SAR secondary images are sequentially composed of N-1 SAR interferometric image pairs; Step 2-2: Since the first and second SAR images were taken very close to each other, the surface deformation during that period was very small and could be ignored. However, the surface deformation from the third image to the first SAR image could not be ignored. For each interferometric image pair, a first relationship model of the surface elevation deformation from the N-1 scene secondary image to the 1st scene primary image and the absolute phase and integer number corresponding to the sth pixel is established according to the time sequence: in, represents the absolute phase corresponding to the sth pixel in the ith interference image pair; k i (s)(i=1,2,…,N-1) represents the integer number of cycles corresponding to the sth pixel in the i-th interferometric image pair; B i (i=1,2,…,N-1) represents the vertical baseline length of the i-th interferometric image pair; Δh m (s)(m=2,3,…,N-1) represents the surface elevation deformation that occurred when the m+1th SAR image was taken relative to the first SAR image; λ represents wavelength; Step 2-3, for each interferometric image pair, a second relationship model of the surface elevation deformation from the N-1 scene secondary image to the 1st scene main image and the absolute phase and integer number corresponding to the s-1th pixel is established according to the time sequence, and the s-1th pixel represents the adjacent pixel of the sth pixel: Step 2-4, since the s-1th pixel and the s-th pixel are adjacent pixels, let the elevation difference between them Δh(s)-Δh(s-1)=0, combine the first relationship model and the second relationship model, and for each interference image pair, establish a third relationship model of the absolute phase difference and the integer cycle difference between the s-th pixel and the s-1th pixel according to the time sequence: in, represents the absolute phase difference between the sth pixel and the s-1th pixel in the i-th interference image pair, Δk i (s,s-1)=k i (s)-k i (s-1)(i=1,2,…,N-1), represents the integer difference between the sth pixel and the s-1th pixel in the i-th interferometric image pair; Step 2-5, to obtain Δk i (s,s-1), convert the third relational model into the optimal solution model: Among them, i,j=1,2,…,N-1 and are different from each other; Step 2-6, find the optimal solution of the optimal solution model and obtain the integer difference Δk between adjacent pixels i (s,s-1).

2. The method according to claim 1, wherein: The step 1, obtaining SAR image data, specifically includes: Acquire N SAR images in time sequence, where the first SAR image is the primary image and the remaining N-1 SAR images are secondary images, and register the N-1 pairs of primary and secondary images.

3. The method according to claim 1, wherein: The step 3, calculating the integer number k, specifically includes: Step 3-1, determine the coherence coefficient map: let the primary and secondary images of the i-th interferometric image pair, that is, the 1st and i+1th SAR images be S1 and S2 respectively, and establish the coherence coefficient map of the i-th interferometric image pair, expressed as: Where E() represents the expected value; * represents conjugation; S2 * represents the conjugate complex number of S2; Step 3-2, coherence coefficient map segmentation: mark the residual points in the coherence coefficient map and determine the distribution of the residual points in the coherence coefficient map, determine the correlation coefficient value corresponding to the residual point on the coherence coefficient map, use the maximum correlation coefficient value among all the residual points as the threshold for segmenting data, and segment the coherence coefficient map and the corresponding winding phase map into high-quality areas and low-quality areas; Step 3-3, phase unwrapping of high-quality regions; Step 3-4, phase unwrapping of low-mass regions.

4. The method according to claim 3, wherein: The step 3-3, phase unwrapping in the high-quality region, specifically includes: Step 3-3-1, obtaining GPS information of any point in the high-quality area of ​​the winding phase diagram; Step 3-3-2, using the GPS information to invert and obtain the integer number corresponding to the point; Step 3-3-3, perform integer integration on the high-quality area to obtain the integer corresponding to each pixel in the high-quality area.

5. The method according to claim 3, wherein: The step 3-4, phase unwrapping of the low-mass region, specifically includes: Step 3-4-1, classify the original SAR image according to the range of the low-quality area to obtain X types of ground object categories, and mark the range of the X types of ground object categories in the low-quality area of ​​the interference fringe pattern; Step 3-4-2, select 2 GPS points within the range of each type of land feature, and select 2X GPS points in total. For each GPS point, use GPS information to invert and obtain the corresponding integer number; Step 3-4-3, establish a fitting function f(x,y) to fit the integer number k of any point: f(x,y)=a0+a1x+a2y+a3x 2 +a4xy+a5y 2 , where a0, a1, a2, a3, a4, a5 are fitting coefficients, Step 3-4-4, determine the mean square error E of the fitting function f(x,y): Among them, k i,j Indicates the integer week number corresponding to the GPS point in the i-th row and j-th column, x i,j and i.j are the row and column values ​​of the pixels in the i-th row and j-th column respectively; Step 3-4-5, minimize the mean square error E to obtain the coefficients {a i }; Step 3-4-6, use the fitting function f(x,y) to calculate the integer number corresponding to each pixel in the low-quality area.

6. The method according to claim 1, wherein: The step 4, obtaining the unwrapped phase result, specifically includes: Step 4-1: According to the integer number k corresponding to each pixel in the high-quality area h The number of integer cycles k corresponding to each pixel in the low-quality area l , get the integer number k corresponding to each pixel in the entire area i (s); Step 4-2, establish the phase unwrapping model: Calculate the unwrapped phase result, where represents the absolute phase corresponding to the sth pixel in the ith interference image pair, ψ i (s) represents the unwrapped phase result of the s-th pixel in the interference image pair i.

7. The method according to claim 3, wherein: In step 2-6, the optimal solution of the optimal solution model is obtained by using the projection method.

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