CN111598929B - Two-dimensional unwrapping method based on time sequence differential interference synthetic aperture radar data - Google Patents
Two-dimensional unwrapping method based on time sequence differential interference synthetic aperture radar data Download PDFInfo
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S13/88—Radar or analogous systems specially adapted for specific applications
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- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9023—SAR image post-processing techniques combined with interferometric techniques
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- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
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Abstract
The application provides a two-dimensional unwrapping method based on time sequence differential interference synthetic aperture radar data, which comprises the following steps of: generating a time sequence SAR image data set; registering the sequential SAR images; extracting high-coherence points of the registered time sequence SAR image data set; carrying out differential interference processing on the SAR image after time sequence registration; performing nearest neighbor interpolation on the interference phase diagram; performing phase unwrapping on each interpolated differential interference phase pattern; and extracting a high-coherence point unwrapping result. According to the method, the phase data on the high-coherence point in the differential interferogram is utilized, not all phases are used, and the interference of points with larger phase errors to the whole unwrapping process is eliminated, so that the phase unwrapping errors are reduced; the sparse grid data and the regular grid data are associated by adopting a nearest neighbor interpolation method, so that the original regular grid unwrapping method is suitable for sparse grids, only the original data processing flow needs to be slightly changed, and the method has higher integration level.
Description
Technical Field
The application relates to the technical field of radar signal processing, in particular to a two-dimensional unwrapping method based on time sequence differential interference synthetic aperture radar data.
Background
The synthetic aperture radar differential interferometry (DInSAR) is a very potential microwave remote sensing technology, and can obtain centimeter-level or even millimeter-level surface deformation in the aspect of deformation detection, and is successfully applied, but the measurement accuracy is still limited by many factors: such as image registration error, decorrelation noise of a region with complex terrain or more vegetation coverage, orbit error, phase unwrapping error of a complex region, atmospheric delay error, terrain phase residual error caused by inaccurate external digital elevation model, and the like. Among these errors, since the phase unwrapping error is limited by the choice of the phase unwrapping method, the unwrapping error that different unwrapping methods will introduce will greatly affect the accuracy of the final monitoring result.
The existing phase unwrapping method is mainly based on phase unwrapping of a two-dimensional regular grid, and specifically includes a path tracking method, a minimum norm method and a network algorithm, namely, a correction method of image self characteristics and a correction method based on external data. The path tracking method is represented by a branch tangent method and a weighted branch tangent method developed according to the branch tangent method, and is firstly proposed by Goldstein in 1986. The minimum norm method attributes the phase unwrapping problem to a mathematical problem of finding the lowest norm, which is typically a least square and weighted least square method, and the main principle is to find the estimated value of the unwrapping phase with the purpose of minimizing the sum of squares of the difference between the unwrapping phase and the wrapping phase. The network algorithm is provided aiming at the defects of the two algorithms, and the two algorithms have two defects, wherein one defect is that the accuracy and the calculation complexity of the algorithm cannot be considered, and the other defect is that the phase unwrapping cannot be well carried out on an interferogram with serious noise pollution. The network algorithm is most proposed by Constantini, and through improvement of Flynn, callaro and the like, the defects of the two algorithms are greatly improved, as long as the idea is to establish a network cost objective function and convert phase unwrapping into a network optimization problem for solving a minimum cost flow, and the unwrapping effect is accurate.
When the interference phase error is small, the unwrapping result obtained by the algorithm is accurate, but when the interference phase error is large, because the error value on each pixel point is small, if all the pixel points are unwrapped, the phase error can be diffused in a two-dimensional plane, and for a time sequence differential interference technology, the error diffusion can influence the accuracy of final deformation monitoring, so that the traditional phase unwrapping method is not suitable for the method.
Disclosure of Invention
The application provides a two-dimensional unwrapping method based on time sequence differential interference synthetic aperture radar data, combines the advantages of traditional grid phase unwrapping and the high coherence point characteristic in the time sequence differential interference technology, and solves the problem that the traditional phase unwrapping method is not suitable for interfering data with large phase errors.
The technical scheme adopted by the application for solving the technical problems is as follows:
a two-dimensional unwrapping method based on time-series differential interferometric synthetic aperture radar data, the method comprising the steps of:
s1: generating a time sequence SAR image data set which comprises N +1 SAR images;
s2: selecting one image as a main image in the time sequence SAR image data set, taking the rest N images as auxiliary images, registering the auxiliary images with the main image to generate a registered time sequence SAR image data set which comprises N +1 SAR images;
s3: generating high coherence point data of the registered time sequence SAR image data set, comprising:
s301: performing radiation correction on the N +1 SAR images by adopting a relative radiation correction method on the registered time sequence SAR image data set to generate a time sequence SAR image data set after radiation correction;
s302: extracting high coherence point data from the time sequence SAR image data set after radiation correction by using a dual threshold method of amplitude information, wherein the high coherence point data comprises K high coherence points;
s4: selecting two images in the time sequence SAR image data set after radiation correction, wherein one image is a main image and the other image is an auxiliary image, performing differential interference processing, and filtering the processing result to generate M differential interference phase diagrams;
s5: performing nearest neighbor interpolation on the differential interference phase map to obtain an interpolated differential interference phase map, including:
s501: let m =1;
s502: extracting the differential interference phase sequence of K high coherence points in the mth differential interference image, and recording the sequence as
Extracting high-coherence point data and generating an azimuth coordinate sequence NA m And distance coordinate sequence NR m ；
S503: extracting the position, the azimuth coordinate value na and the distance coordinate value nr of each pixel point in the differential interference pattern;
s504: calculating an interference phase value after the pixel point is interpolated by using a nearest neighbor interpolation method;
s505: after the interpolated interference phase value is calculated for each pixel point in the differential interference phase diagram, taking a new interference phase diagram as an mth interpolated differential interference phase diagram, and enabling m = m +1;
s506: if M is less than or equal to M, executing step S502, otherwise executing step S6;
s6: performing phase unwrapping on each interpolation differential interference phase diagram to obtain M unwrapped phase diagrams;
s7: and extracting a high-coherence point unwrapping result from the M unwrapped phase diagrams.
Optionally, the high coherence point data includes an azimuth coordinate value of the high coherence point in the SAR image and a distance coordinate value in the SAR image.
Optionally, the nearest neighbor interpolation method in S5 includes the following steps:
calculating the distance between the pixel point and each high coherence point by using a plane distance formula, finding out a high coherence differential interference phase corresponding to the shortest distance, and replacing the pixel point phase value in the original differential interference image by using the phase value, wherein the plane distance calculation formula is as follows;
wherein, d j Representing the planar distance between the selected pixel point and the jth high coherence point,
expressing the operation of taking a square root, x expressing the coordinate value of the selected pixel point in the azimuth direction of the SAR image, and x j Indicating the azimuth coordinate value, rho, of the jth high-coherence point in the SAR image a The azimuth resolution of the SAR image is represented, y represents the distance coordinate value of the selected pixel point in the SAR image, and y represents the distance coordinate value of the selected pixel point in the SAR image j The distance coordinate value, rho, of the jth high-coherence point in the SAR image r The range-wise resolution of the SAR image is represented.
Optionally, in S7, the high coherence point unwrapping result includes an azimuth coordinate value of the high coherence point in the SAR image, a distance coordinate value of the high coherence point in the SAR image, and M unwrapping phases of the high coherence point.
Optionally, the performing phase unwrapping on each interpolated differential interference phase map in S6 to obtain M unwrapped phase maps includes:
s601: selecting an un-unwrapped differential interference phase image from the M interpolated differential interference phase images;
s602: the differential interference phase diagram is unwrapped by using a minimum cost stream unwrapping method to generate an unwrapped differential interference phase diagram:
s603: and judging whether all the differential interference phase diagrams are unwrapped, if so, executing the step S7, otherwise, continuing to execute the step S6.
The technical scheme provided by the application comprises the following beneficial technical effects:
the application provides a two-dimensional unwrapping method based on time sequence differential interference synthetic aperture radar data, which comprises the following steps of: generating a time sequence SAR image data set; registering the time sequence SAR image; extracting high-coherence points of the registered time sequence SAR image data set; carrying out differential interference processing on the SAR image after time sequence registration; performing nearest neighbor interpolation on the interference phase diagram; performing phase unwrapping on each interpolated differential interference phase image; and extracting a high-coherence point unwrapping result. According to the method, only phase data on high-coherence points in a differential interference diagram are utilized, not all phases are used, and interference of points with large phase errors on the whole unwrapping process is eliminated, so that the phase unwrapping errors are reduced; by means of self information of the synthetic aperture radar image data, the defect that prior models are required to support in the prior art is overcome, the method in the application has high feasibility and applicability, sparse grid data and regular grid data are associated by adopting a nearest neighbor interpolation method, the original regular grid unwrapping method is suitable for sparse grids, only small changes are needed to the original data processing flow, and the method has high integration level. The two-dimensional unwrapping method based on time sequence differential interference synthetic aperture radar data is adopted, interference of unwrapping errors of existing time sequence data can be effectively inhibited, and accordingly phase unwrapping precision is improved.
Drawings
In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.
Fig. 1 is a flowchart of an implementation provided in this embodiment;
FIG. 2 is a graph of the phase of the noiseless differential interference used;
FIG. 3 is a graph of the phase of the noisy differential interference used;
FIG. 4 is a diagram of an interpolated differential interference phase;
FIG. 5 is a graph of the unwrapped phase result error comparison;
FIG. 6 is a graph of mean error comparison of unwrapping phase results from repeated experiments.
Detailed Description
In order to make the technical solutions in the present application better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application; it is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The application provides a two-dimensional unwrapping method based on time sequence differential interference synthetic aperture radar data, which comprises the following steps:
s1: and generating a time sequence SAR image data set which comprises N +1 SAR images.
S2: selecting one image as a main image in the time sequence SAR image data set, taking the rest N images as auxiliary images, registering the auxiliary images with the main image to generate a registered time sequence SAR image data set which comprises N +1 SAR images.
S3: generating high coherence point data of the registered time sequence SAR image data set, comprising:
s301: performing radiation correction on the N +1 SAR images by adopting a relative radiation correction method on the registered time sequence SAR image data set to generate a time sequence SAR image data set after radiation correction;
s302: and extracting high-coherence point data from the time sequence SAR image data set after radiation correction by using a dual threshold method of amplitude information, wherein the high-coherence point data comprises K high-coherence points, and the high-coherence point data comprises an azimuth coordinate value of the high-coherence point in the SAR image and a distance coordinate value of the high-coherence point in the SAR image.
S4: selecting two images in the time sequence SAR image data set after radiation correction, wherein one image is a main image and the other image is an auxiliary image, carrying out differential interference processing, and filtering the processing result to generate M differential interference phase images.
S5: performing nearest neighbor interpolation on the differential interference phase map to obtain an interpolated differential interference phase map, wherein the interpolation process comprises the following steps:
s501: let m =1;
s502: extracting the differential interference phase sequence of K high coherence points in the mth differential interference image, and recording the sequence as
Extracting high-coherence point data and generating methodSequence of bit-wise coordinates NA m And distance coordinate sequence NR m ；
S503: extracting the position, the azimuth coordinate value na and the distance coordinate value nr of each pixel point in the differential interference pattern;
s504: calculating an interference phase value after pixel point interpolation by using a nearest neighbor interpolation method;
s505: after an interpolated interference phase value is calculated for each pixel point in the differential interference phase diagram, taking a new interference phase diagram as an mth interpolated differential interference phase diagram, and enabling m = m +1;
s506: if M is less than or equal to M, executing step S502, otherwise executing step S6;
the nearest neighbor interpolation method comprises the following steps:
calculating the distance between the pixel point and each high coherence point by using a plane distance formula, finding out a high coherence differential interference phase corresponding to the shortest distance, and replacing the phase value of the pixel point in the original differential interference image by using the phase value, wherein the plane distance formula is as follows;
wherein d is j Representing the planar distance between the selected pixel point and the jth high coherence point,
expressing the operation of taking a square root, x expressing the coordinate value of the selected pixel point in the azimuth direction of the SAR image, and x j Indicating the azimuth coordinate value, rho, of the jth high-coherence point in the SAR image a The azimuth resolution of the SAR image is represented, y represents the distance coordinate value of the selected pixel point in the SAR image, and y represents the distance coordinate value of the selected pixel point in the SAR image j The distance coordinate value, rho, of the jth high-coherence point in the SAR image r The range-wise resolution of the SAR image is represented.
S6: performing phase unwrapping on each interpolated differential interference phase map to obtain M unwrapped phase maps, including:
s601: selecting an un-unwrapped differential interference phase image from the M interpolated differential interference phase images;
s602: the differential interference phase diagram is unwrapped by using a minimum cost stream unwrapping method to generate an unwrapped differential interference phase diagram:
s603: and judging whether all the differential interference phase diagrams are unwrapped, if so, executing the step S7, otherwise, continuing to execute the step S6.
S7: and extracting a high-coherence point unwrapping result from the M unwrapped phase diagrams, wherein the high-coherence point unwrapping result comprises an azimuth coordinate value of the high-coherence point in the SAR image, a distance coordinate value of the high-coherence point in the SAR image, and M unwrapped phases of the high-coherence point.
According to the two-dimensional unwrapping method based on the time sequence differential interference synthetic aperture radar data, the interference of points with larger phase errors on the whole unwrapping process is eliminated by only utilizing the phase data on high coherent points in the differential interference pattern instead of using all phases, so that the phase unwrapping errors are reduced; the method overcomes the defect that prior model support is needed in the prior art by depending on the self information of the synthetic aperture radar image data, so that the method in the application has higher feasibility and applicability, sparse grid data and regular grid data are linked by adopting a nearest neighbor interpolation method, the original regular grid unwrapping method is suitable for sparse grids, the original data processing flow is slightly changed, and the method has higher integration level. The two-dimensional unwrapping method based on time sequence differential interference synthetic aperture radar data is adopted, interference of unwrapping errors of existing time sequence data can be effectively inhibited, and accordingly phase unwrapping precision is improved.
The technical solution in the present application is further described below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart of an implementation provided in this embodiment, and specific steps implemented in this embodiment are further described with reference to fig. 1:
s100: and generating a time sequence SAR image data set which comprises N +1 SAR images.
S200: selecting one image as a main image in the time sequence SAR image data set, registering the auxiliary image with the main image, generating a registered time sequence SAR image data set which comprises N +1 SAR images, and comprising the following steps:
s2001: extracting 1 main IMAGE IMAGE0 and N IMAGE sequences IMAGE n ，n＝1,......,N；
S2002: selecting an unregistered secondary IMAGE from the sequence of N IMAGEs n ；
S2003: registering the auxiliary image and the main image to the same space by using an SAR data registration method to generate a registered auxiliary image
S2004: judging whether all the auxiliary images are registered with the main image, if so, executing S2005, otherwise, executing S2002;
s2005: using all registered secondary images
And the master image generating a registered time series SAR image data set.
S300: generating high coherence point data for a time series SAR image dataset, comprising:
s3001: performing radiation correction on the N +1 SAR images by applying a relative radiation correction method to the registered time sequence SAR image data set to generate a time sequence SAR image data set after radiation correction;
s3002: extracting high coherence point data from the time sequence SAR image data set after radiation correction by using a dual threshold method of amplitude information, wherein the high coherence point data comprises K high coherence points;
s400: selecting two images in the time sequence SAR image data set after radiation correction, wherein one image is a main image and the other image is an auxiliary image, carrying out differential interference processing, and filtering the processing result to generate M differential interference phase images.
S500: performing nearest neighbor interpolation on the interference phase map to obtain an interpolated differential interference phase map, including:
s5001: let m =1;
s5002: extracting the differential interference phase sequence of K high coherence points in the mth differential interference image, and recording the sequence as
Extracting high-coherence point data and generating an azimuth coordinate sequence NA m And distance coordinate sequence NR m ；
S5003: selecting an unselected pixel point in the differential interference image, and extracting the position, the azimuth coordinate value na and the distance coordinate value nr of the pixel point in the differential interference image;
s5004: calculating the plane distance between the nth pixel point and each high coherence point by using a plane distance calculation formula as follows:
wherein d is j Representing the planar distance between the selected pixel point and the jth pixel point in the planar distance sequence,
expressing the operation of taking a square root, x expressing the coordinate value of the selected pixel point in the azimuth direction of the SAR image, and x j Indicating the azimuth coordinate value rho of the jth pixel point in the plane distance sequence in the SAR image a The azimuth resolution of the SAR image is represented, y represents the distance coordinate value of the selected pixel point in the SAR image, and y represents the distance coordinate value of the selected pixel point in the SAR image j Indicating the distance coordinate value rho of the jth pixel point in the plane distance sequence in the SAR image r Representing a range-wise resolution of the SAR image;
s5005: finding a high coherence point with the shortest plane distance with the pixel point, and replacing the differential interference phase value of the pixel point with the differential interference phase value of the high coherence point;
s5006: judging whether all unselected pixel points are selected, if so, executing the step 8, otherwise, executing the step 3;
s5007: and taking the new interference phase diagram as the mth interpolation differential interference phase diagram, enabling M = M +1, if M is less than or equal to M, executing S5002, otherwise executing S600.
S600: and performing phase unwrapping on each interpolation differential interference phase diagram to obtain M unwrapped phase diagrams.
S6001: selecting an un-unwrapped differential interference phase image from the M interpolated differential interference phase images;
s6002: the differential interference phase diagram is unwrapped by using a minimum cost stream unwrapping method to generate an unwrapped differential interference phase diagram:
s6003: and judging whether all the differential interference phase diagrams are unwrapped, if so, executing the step S700, otherwise, continuing to execute the step S600.
S700: and extracting a high-coherence point unwrapping result from the M unwrapped phase diagrams.
In order to explain the practical application performance of the technical solution in this embodiment, the following further explains the effect of this embodiment by combining with a simulation experiment:
simulation experiment conditions are as follows:
the hardware platform of the simulation experiment in this embodiment is: the processor is Inteli74710HQCPU, the main frequency is 2.5GHz, and the memory is 8GB.
The software platform of the simulation experiment of the embodiment is as follows: windows7 operating system and matlab2018b.
Simulation content and result analysis thereof:
all data of the simulation experiment are self-generated simulated unwrapping phase images with the given size of 512 multiplied by 512 and the phase distribution interval of [ -3 pi, 3 pi ], a group of Gaussian noise phases with the average value of 0 and the standard deviation of 0.5 are added to the data, and the data are wrapped to obtain a simulated differential interference phase diagram.
Referring to fig. 2, fig. 2 is a diagram of a noise-free analog differential interference phase, in which an abscissa represents a distance coordinate value of a pixel point, and an ordinate represents an azimuth coordinate value of the pixel point, as can be seen from fig. 2, the noise-free analog differential interference phase is distributed between [ -pi, pi ], and the left and right sides of the noise-free analog differential interference phase are respectively provided with a concentric circle, which is clearly distributed and can be used as true value data of a subsequent experiment. Because high coherence point data is adopted for processing in a time sequence, points with the added noise absolute value smaller than one percent of the noise standard deviation are selected as the high coherence points during simulation, and subsequent processing is carried out.
Referring to fig. 3, fig. 3 is a diagram of analog differential interference phase with noise added, in which the abscissa represents a pixel point distance coordinate value, and the ordinate represents a pixel point azimuth coordinate value, as can be seen from fig. 3, the analog differential interference phase with noise added is distributed between [ -pi, pi ], and the left and right sides of the analog differential interference phase are respectively provided with a concentric circle, which is more blurred than the edge of fig. 2, and can be used as processing sample data of a subsequent experiment.
Compared with the traditional method, the method comprises the following steps:
referring to fig. 4, fig. 4 is a diagram of an interpolated differential interference phase diagram, in which an abscissa represents a pixel point distance coordinate value, and an ordinate represents a pixel point direction coordinate value, as can be seen from fig. 4, dark circles are simulated high coherent points, and a comparison with fig. 3 illustrates that the differential interference phase diagram after difference better restores the phase characteristics in the absence of noise than the original phase diagram, which can prove that the method in this embodiment can effectively suppress the noise phase in the differential interference phase diagram.
Referring to fig. 5, fig. 5 is a graph showing the comparison of the phase result error of the unwrapping phase, wherein the abscissa indicates the number of the selected high coherence point and the ordinate indicates the difference between the unwrapping phase and the true phase, and the graph shows the relationship between the unwrapping error distribution and the unwrapping method, as can be seen from fig. 5, the error of the method of the present embodiment is smaller than that of the conventional method as a whole, which can prove that the phase unwrapping performance of the method of the present embodiment is better than that of the conventional method.
Referring to fig. 6, fig. 6 is a comparison graph of error mean values of unwrapping phase results of repeated experiments, wherein the abscissa represents a serial number of the repeated experiments, and the ordinate represents an error mean value generated in each experiment, which illustrates a relationship between unwrapping stability and unwrapping method, and illustrates that as the number of experiments increases, fig. 6 shows that, in repeated experiments, the error mean distribution of the method in the embodiment is mainly near 0, while the error mean distribution of the conventional method is significant in two polarization phenomena, which can prove that the phase unwrapping stability of the method is superior to that of the conventional method.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be understood that the present application is not limited to what has been described above and shown in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (5)
1. A two-dimensional unwrapping method based on time-series differential interferometric synthetic aperture radar data, the method comprising the steps of:
s1: generating a time sequence SAR image data set which comprises N +1 SAR images;
s2: selecting one image as a main image in the time sequence SAR image data set, taking the rest N images as auxiliary images, registering the auxiliary images with the main image to generate a registered time sequence SAR image data set which comprises N +1 SAR images;
s3: generating high coherence point data of the registered time sequence SAR image data set, comprising:
s301: performing radiation correction on the N +1 SAR images by adopting a relative radiation correction method on the registered time sequence SAR image data set to generate a time sequence SAR image data set after radiation correction;
s302: extracting high coherence point data from the time sequence SAR image data set after radiation correction by using a dual threshold method of amplitude information, wherein the high coherence point data comprises K high coherence points;
s4: selecting two images in the time sequence SAR image data set after radiation correction, wherein one image is a main image and the other image is an auxiliary image, performing differential interference processing, and filtering the processing result to generate M differential interference phase diagrams;
s5: performing nearest neighbor interpolation on the differential interference phase map to obtain an interpolated differential interference phase map, including:
s501: let m =1;
s502: extracting the differential interference phase sequence of K high coherent points in the mth differential interference image, and recording the sequence as
Extracting high-coherence point data and generating an azimuth coordinate sequence NA m And distance coordinate sequence NR m ；
S503: extracting the position, the azimuth coordinate value na and the distance coordinate value nr of each pixel point in the differential interference image;
s504: calculating an interference phase value after the pixel point is interpolated by using a nearest neighbor interpolation method;
s505: after the interpolated interference phase value is calculated for each pixel point in the differential interference phase diagram, taking a new interference phase diagram as an mth interpolated differential interference phase diagram, and enabling m = m +1;
s506: if M is less than or equal to M, executing step S502, otherwise executing step S6;
s6: performing phase unwrapping on each interpolation differential interference phase diagram to obtain M unwrapped phase diagrams;
s7: and extracting a high-coherence point unwrapping result from the M unwrapped phase diagrams.
2. The two-dimensional unwrapping method based on time-series differential interferometric synthetic aperture radar data as recited in claim 1, wherein the high coherence point data includes azimuth coordinate values of the high coherence point in the SAR image and distance coordinate values of the high coherence point in the SAR image.
3. A method for two-dimensional unwrapping based on time-series differential interferometric synthetic aperture radar data according to claim 1, wherein said nearest neighbor interpolation method in S5 includes the steps of:
calculating the distance between the pixel point and each high coherence point by using a plane distance formula, finding out a high coherence differential interference phase corresponding to the shortest distance, and replacing the pixel point phase value in the original differential interference image by using the phase value, wherein the plane distance calculation formula is as follows;
wherein d is j Representing the planar distance between the selected pixel point and the jth high coherence point,
expressing the operation of taking a square root, x expressing the coordinate value of the selected pixel point in the azimuth direction of the SAR image, and x j The azimuth coordinate value, rho, of the jth high-coherence point in the SAR image a The azimuth resolution of the SAR image is represented, y represents the distance coordinate value of the selected pixel point in the SAR image, and y represents the distance coordinate value of the selected pixel point in the SAR image j The distance coordinate value, rho, of the jth high-coherence point in the SAR image r The range-wise resolution of the SAR image is represented.
4. The two-dimensional unwrapping method based on time-series differential interferometric synthetic aperture radar data as recited in claim 1, wherein the unwrapping result of the high coherent point in S7 includes an azimuth coordinate value of the high coherent point in the SAR image, a distance coordinate value in the SAR image, and M unwrapping phases of the high coherent point.
5. The two-dimensional unwrapping method based on time-series differential interferometric synthetic aperture radar data as recited in claim 1, wherein the phase unwrapping each interpolated differential interferometric phase map in S6 to obtain M unwrapped phase maps comprises:
s601: selecting an un-unwrapped differential interference phase image from the M interpolated differential interference phase images;
s602: the differential interference phase diagram is unwrapped by using a minimum cost stream unwrapping method to generate an unwrapped differential interference phase diagram:
s603: and judging whether all the differential interference phase diagrams are unwrapped, if so, executing the step S7, otherwise, continuing to execute the step S6.
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| CN107132539A (en) * | 2017-05-03 | 2017-09-05 | 中国地质科学院探矿工艺研究所 | Landslide early-stage identification method of time sequence InSAR (interferometric synthetic Aperture Radar) based on small baseline set |
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| CN106772342A (en) * | 2017-01-11 | 2017-05-31 | 西南石油大学 | A kind of Timing Difference radar interference method suitable for big gradient surface subsidence monitoring |
| CN107132539A (en) * | 2017-05-03 | 2017-09-05 | 中国地质科学院探矿工艺研究所 | Landslide early-stage identification method of time sequence InSAR (interferometric synthetic Aperture Radar) based on small baseline set |
| CN109031301A (en) * | 2018-09-26 | 2018-12-18 | 云南电网有限责任公司电力科学研究院 | Alpine terrain deformation extracting method based on PSInSAR technology |
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