A method and device for optimizing InSAR time series phase

Abstract

The invention discloses an optimization method and device for InSAR time series phase, including: obtaining a time series SAR data set, and performing L-view processing on the time series SAR data set to obtain an L-view intensity data set and an interference data set respectively; taking the L-view intensity data set as a reference, obtaining and respectively registering and geocoding a preset digital elevation model and a preset land cover map, respectively obtaining a digital elevation in a SAR image coordinate system and a land cover map in a SAR image coordinate system; performing a differential operation on the interference data set according to the digital elevation in the SAR image coordinate system to obtain a differential interference data set; estimating the covariance matrix of the differential interference data set according to the land cover map in the SAR image coordinate system and the differential interference data set, and estimating and obtaining the optimized time series phase according to a preset time series phase maximum likelihood estimation formula. The invention can be applied to a wide range of efficient time series phase optimization estimation processes.

Description

Technical Field

[0001] The invention relates to the technical field of synthetic aperture radar interferometry, and in particular to an InSAR timing phase optimization method and device. Background Art

[0002] As the frequency of contemporary SAR satellite re-entry increases, the massive amount of observation data not only provides an opportunity to more objectively understand the development dynamics and laws of deformation, but also brings new challenges to fast and efficient time-series InSAR. In particular, in the European Space Agency's Copernicus program, the TOPS Sentinel-1 data with a satellite re-entry period of 12 days and a swath width of up to 200km*200km, traditional data processing methods often fail to meet the monitoring timeliness requirements.

[0003] Large-scale temporal phase optimization is one of the most critical steps in InSAR data processing. It can resist radar signal decorrelation, enhance the interference signal-to-noise ratio, thereby achieving high-quality process control and reducing the propagation of unwrapping errors. At present, the mainstream methods of temporal phase optimization are divided into two categories. One is to only consider spatial information and use noise reduction methods such as Goldstein to filter the interference pattern one by one. The representative method is the small baseline technology, but this method does not take into account the image details and loses spatial resolution. It is a method that increases the deviation in exchange for improved accuracy. Therefore, the technology represented by SBAS is often efficient in large-scale temporal InSAR deformation monitoring, but it cannot achieve fine monitoring and has low reliability; the second is to take into account temporal and spatial information. Based on the assumption that similar objects have similar backscattering properties, the KS (Kolmogorov-Smirnov) test and BFGS (BroydenFletcher Goldfarb Shanno) phase triangulation algorithm are used to optimize the temporal phase. The representative method is the SqueeSAR technology. This method can take into account the image details, but it is necessary to search for similar targets pixel by pixel in the single-view complex image sequence in order to meet the assumptions of the statistical model. Therefore, the computational efficiency of technologies represented by SqueeSAR is very low and it is difficult to apply them to large-scale deformation monitoring applications.

[0004] Therefore, there is still a need for a method to achieve efficient and high-precision InSAR timing phase optimization to solve the problem of the lack of efficient and high-precision InSAR timing phase optimization methods in the prior art. Summary of the invention

[0005] The present invention provides an InSAR timing phase optimization method and device to solve the technical problem that there is a lack of a timing phase optimization method with both high efficiency and high precision in the prior art.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides an InSAR timing phase optimization method, comprising:

[0007] A time series SAR data set is acquired, and L-view processing is performed on the time series SAR data set to obtain an L-view intensity data set and an interference data set respectively; with the L-view intensity data set as a reference, a preset digital elevation model and a preset land cover map are acquired and registered and geocoded respectively to obtain a digital elevation in a SAR image coordinate system and a land cover map in a SAR image coordinate system respectively; according to the digital elevation in the SAR image coordinate system, a differential operation is performed on the interference data set to obtain a differential interference data set; according to the land cover map in the SAR image coordinate system and the differential interference data set, a covariance matrix of the differential interference data set is estimated, and according to a preset time series phase maximum likelihood estimation formula, an optimized time series phase is estimated and obtained.

[0008] It can be understood that after acquiring the time-series SAR data set, L-view processing is performed on the time-series SAR data set. Compared with the prior art, the existing single-view complex data set is converted into an L-view data set. The workspace space occupied by the L-view data input and parameter output is much lower than that of the single-view, which can greatly reduce the volume of the data set. The covariance matrix of the differential interferometric data set is estimated by using the land cover map obtained in the SAR image coordinate system, which greatly improves the computational efficiency and avoids the estimation through the homogeneous sample statistical selection method in the prior art.

[0009] As a preferred solution, the acquisition of a time series SAR data set and the L-view processing of the time series SAR data set to obtain an L-view intensity data set and an interference data set are specifically as follows:

[0010] A time-series SAR data set with n time-series SAR single-look images is obtained, and the n time-series SAR single-look images are registered, and L-view processing is performed on the registered n time-series SAR single-look images to obtain n SAR intensity sequences and n(n-1) / 2 interferometric data sets respectively; wherein n is a positive integer greater than 1.

[0011] It can be understood that after obtaining a time-series SAR data set with n time-series SAR single-view images, the time-series SAR data set is registered and L-view processed, which can improve the accuracy of the SAR intensity sequence and interference data set obtained after the L-view processing, while also avoiding the need to process the time-series SAR single-view images later. The multi-view data set obtained after the L-view processing can greatly reduce the volume of the data set to be processed.

[0012] As a preferred solution, the L visual intensity dataset is used as a reference to obtain and respectively align and geocode the preset digital elevation model and the preset land cover map, and obtain the digital elevation in the SAR image coordinate system and the land cover map in the SAR image coordinate system, specifically:

[0013] Taking the L apparent intensity dataset as a reference, a preset cross-correlation maximization algorithm is adopted to obtain and align and geocode a preset digital elevation model with the L apparent intensity dataset to obtain a digital elevation in a SAR image coordinate system; and obtain and align and geocode preset satellite orbit data information with a preset land cover map to obtain a land cover map in a SAR image coordinate system.

[0014] It can be understood that, by using the L apparent intensity data set as a reference, aligning and geocoding the preset digital elevation model with the SAR intensity image in the L apparent intensity data set, the accuracy of terrain depiction can be improved, and the preset satellite orbit data information and the preset land cover map are aligned and geocoded, so that reference pixels can be provided for the subsequent step of estimating the covariance matrix of the differential interferometer data set, avoiding the method of homogeneous sample selection used in the prior art, thereby improving the overall calculation efficiency.

[0015] As a preferred solution, the interferometric data set is subjected to a differential operation according to the digital elevation in the SAR image coordinate system to obtain a differential interferometric data set, specifically:

[0016] According to the digital elevation in the SAR image coordinate system, a differential operation is performed on the n(n-1) / 2 interference data sets to obtain n(n-1) / 2 differential interference data sets; wherein the differential interference data sets correspond one-to-one to the pixel positions of the land cover map in the SAR image coordinate system.

[0017] It can be understood that performing a differential operation on the interferometric data set according to the digital elevation in the SAR image coordinate system can eliminate the terrain phase contribution and effectively improve the accuracy of the interferometric phase. At the same time, the obtained differential interferometric data set corresponds one-to-one to the pixel position of the land cover map in the SAR image coordinate system, so that in the subsequent step of estimating the covariance matrix of the differential interferometric data set, the pixels in the window with the same category label as the center pixel can be accurately counted. At the same time, compared with the prior art, the overall calculation efficiency is also improved.

[0018] As a preferred solution, the covariance matrix of the differential interferometry dataset is estimated based on the land cover map in the SAR image coordinate system and the differential interferometry dataset, specifically:

[0019] A sliding window is established for the n(n-1) / 2 differential interferometry data sets in sequence, and pixels with the same attributes as a preset reference pixel in the sliding window corresponding to each differential interferometry data set are marked in sequence according to the land cover map in the SAR image coordinate system, so that pixels with the same category mark as the central pixel in the sliding window are counted in sequence, and elements of the covariance matrix are calculated, so as to estimate the covariance matrix of the differential interferometry data set.

[0020] It can be understood that in the step of estimating the covariance matrix of the differential interferometry data set, instead of the method of statistical selection of homogeneous samples in the traditional distributed target data processing step, the pixels with the same attributes as the reference pixels in the current sliding window are marked by the land cover map under the SAR image coordinate system, and the pixels marked with the same category, i.e., homogeneous pixels, are directly used to estimate the covariance matrix, which greatly improves the calculation efficiency and has obvious advantages in terms of efficient calculation.

[0021] As a preferred solution, the method of estimating and obtaining the optimized timing phase according to the preset timing phase maximum likelihood estimation formula is as follows:

[0022] According to the preset timing phase maximum likelihood estimation formula: ξ=e jθ ; Thus estimating and obtaining the optimized timing phase;

[0023] in, is the optimized timing phase, θ is the positive value of the timing phase, represents the real part operator, and ∑ is the covariance matrix of the differential interference data set.

[0024] It can be understood that the optimized timing phase can be accurately estimated through the preset timing phase maximum likelihood estimation formula and the covariance matrix of the differential interferometer data set, thereby further improving the accuracy of SAR timing phase optimization.

[0025] Accordingly, the present invention also provides an InSAR timing phase optimization device, comprising: an L-view processing module, a registration encoding module, a differential operation module and a timing optimization module;

[0026] The L-view processing module is used to acquire a time-series SAR data set, and perform L-view processing on the time-series SAR data set to obtain an L-view intensity data set and an interference data set respectively;

[0027] The registration and coding module is used to obtain and respectively register and geocode a preset digital elevation model and a preset land cover map with reference to the L visual intensity dataset, so as to obtain a digital elevation in a SAR image coordinate system and a land cover map in a SAR image coordinate system respectively;

[0028] The differential operation module is used to perform a differential operation on the interference data set according to the digital elevation in the SAR image coordinate system to obtain a differential interference data set;

[0029] The timing optimization module is used to estimate the covariance matrix of each pixel according to the land cover map in the SAR image coordinate system and the differential interference data set, and estimate and obtain the optimized timing phase according to a preset timing phase maximum likelihood estimation formula.

[0030] As a preferred solution, the L-view processing module is used to obtain a time-series SAR data set and perform L-view processing on the time-series SAR data set to obtain an L-view intensity data set and an interference data set, respectively, specifically:

[0031] Obtain n time-series SAR data sets, align the n time-series SAR data sets, perform L-view processing on the aligned n time-series SAR data sets, and obtain n SAR intensity sequences and n(n-1) / 2 interference data sets respectively; wherein n is a positive integer greater than 1.

[0032] As a preferred solution, the registration and coding module is used to obtain and respectively register and geocode the preset digital elevation model and the preset land cover map with reference to the L visual intensity dataset, and obtain the digital elevation in the SAR image coordinate system and the land cover map in the SAR image coordinate system, specifically:

[0033] Taking the L apparent intensity dataset as a reference, a preset cross-correlation maximization algorithm is adopted to obtain and align and geocode a preset digital elevation model with the L apparent intensity dataset to obtain a digital elevation in a SAR image coordinate system; and obtain and align and geocode preset satellite orbit data information with a preset land cover map to obtain a land cover map in a SAR image coordinate system.

[0034] As a preferred solution, the differential operation module is used to perform a differential operation on the interferometric data set according to the digital elevation in the SAR image coordinate system to obtain a differential interferometric data set, specifically:

[0035] According to the digital elevation in the SAR image coordinate system, a differential operation is performed on the n(n-1) / 2 interference data sets to obtain n(n-1) / 2 differential interference data sets; wherein the differential interference data sets correspond one-to-one to the pixel positions of the land cover map in the SAR image coordinate system.

[0036] As a preferred solution, the timing optimization module is used to estimate the covariance matrix of each pixel according to the land cover map in the SAR image coordinate system and the differential interferometry data set, specifically:

[0037] A sliding window is established for the n(n-1) / 2 differential interferometric data sets in sequence, and pixels with the same attributes as a preset reference pixel in the sliding window corresponding to each differential interferometric data set are marked in sequence according to the land cover map in the SAR image coordinate system, so that pixels with the same category mark as the central pixel in the sliding window are counted in sequence, and elements of the covariance matrix are calculated, so as to estimate the covariance matrix of each pixel.

[0038] As a preferred solution, the timing optimization module is used to estimate and obtain the optimized timing phase according to a preset timing phase maximum likelihood estimation formula, specifically:

[0039] According to the preset timing phase maximum likelihood estimation formula: ξ=e jθ ; Thus estimating and obtaining the optimized timing phase;

[0040] in, is the optimized timing phase, θ is the positive value of the timing phase, represents the real part operator, and ∑ is the covariance matrix of the differential interference data set. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 : A flow chart of the steps of an InSAR timing phase optimization method provided by an embodiment of the present invention;

[0042] Figure 2 : A flow chart of an InSAR timing phase optimization method provided by an embodiment of the present invention;

[0043] Figure 3 : A schematic diagram of a flow chart of covariance matrix estimation in an InSAR time series phase optimization method provided by an embodiment of the present invention;

[0044] Figure 4 : A comparison diagram of the optimization method for InSAR timing phase provided by an embodiment of the present invention, the original differential interferogram, and the optimization result of the existing method;

[0045] Figure 5 : Schematic diagram of an InSAR timing phase optimization device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0047] Embodiment 1

[0048] Please refer to Figure 1 and Figure 2 , an InSAR timing phase optimization method provided by an embodiment of the present invention, comprising the following steps S101-S104:

[0049] S101: Acquire a time-series SAR data set, and perform L-view processing on the time-series SAR data set to obtain an L-view intensity data set and an interference data set respectively.

[0050] Specifically, a time-series SAR data set with n time-series SAR single-view images is obtained, and the n time-series SAR single-view images are registered, and L-view processing is performed on the registered n time-series SAR single-view images to obtain n SAR intensity sequences and n(n-1) / 2 interference data sets respectively; wherein n is a positive integer greater than 1.

[0051] It should be noted that, preferably, the acquired time-series SAR data set of n time-series SAR single-view images is the SAR data of Sentinel-1. After registration, L-view processing is performed on it to obtain n SAR intensity sequences and n(n-1) / 2 interference data sets respectively; the selection of the view number L is determined according to actual needs. Preferably, in this embodiment, the view number L is determined by the resolution of the preset land cover map in step S102. The spatial resolution of the SAR data of Sentinel-1 is about 20m*5m (azimuth*range). For the preset land cover map of 20 meters, L=1*5 views; wherein, the Sentinel-1 satellite is an earth observation satellite in the Copernicus program (GMES) of the European Space Agency, consisting of two satellites, carrying a C-band synthetic aperture radar, which can provide continuous images, including but not limited to image data information during the day, night and various weather conditions.

[0052] It can be understood that after obtaining a time-series SAR data set with n time-series SAR single-view images, the time-series SAR data set is registered and L-view processed, which can improve the accuracy of the SAR intensity sequence and interference data set obtained after the L-view processing, while also avoiding the need to process the time-series SAR single-view images later. The multi-view data set obtained after the L-view processing can greatly reduce the volume of the data set to be processed.

[0053] S102: Using the L visual intensity dataset as a reference, obtaining and respectively registering and geocoding a preset digital elevation model and a preset land cover map, and obtaining a digital elevation in a SAR image coordinate system and a land cover map in a SAR image coordinate system, respectively.

[0054] Specifically, taking the L apparent intensity dataset as a reference, a preset cross-correlation maximization algorithm is adopted to obtain and align and geocode a preset digital elevation model with the L apparent intensity dataset to obtain a digital elevation in a SAR image coordinate system; and obtain and align and geocode preset satellite orbit data information with a preset land cover map to obtain a land cover map in a SAR image coordinate system.

[0055] It should be noted that the preset cross-correlation maximization algorithm has the characteristics of being insensitive to noise and accurate in matching positions, while the digital elevation model realizes digital simulation of ground terrain through limited terrain elevation data, that is, digital expression of terrain surface morphology. After aligning and geocoding the preset digital elevation model with the L apparent intensity data set, the digital elevation in the SAR image coordinate system can be obtained; preferably, in this embodiment, the preset satellite orbit data information is provided by the Sentinel-1 satellite, and the preset land cover map reflects the condition of the surface. By aligning and geocoding the preset satellite orbit data information with the preset land cover map, the land cover map in the SAR image coordinate system can be obtained.

[0056] It can be understood that, by using the L apparent intensity data set as a reference, aligning and geocoding the preset digital elevation model with the SAR intensity image in the L apparent intensity data set, the accuracy of terrain depiction can be improved, and the preset satellite orbit data information and the preset land cover map are aligned and geocoded, so that reference pixels can be provided for the subsequent step of estimating the covariance matrix of the differential interferometer data set, avoiding the method of homogeneous sample selection used in the prior art, thereby improving the overall calculation efficiency.

[0057] S103: performing a differential operation on the interferometric dataset according to the digital elevation in the SAR image coordinate system to obtain a differential interferometric dataset.

[0058] Specifically, according to the digital elevation in the SAR image coordinate system, a differential operation is performed on the n(n-1) / 2 interference data sets to obtain n(n-1) / 2 differential interference data sets; wherein the differential interference data sets correspond one-to-one to the pixel positions of the land cover map in the SAR image coordinate system.

[0059] It should be noted that performing differential operations on n(n-1) / 2 interferometric data sets to eliminate the contribution of terrain phase, that is, removing terrain and unwrapping the phase of the interferometric data sets, helps to improve the deformation monitoring capability of InSAR, thereby obtaining n(n-1) / 2 differential interferometric data sets.

[0060] It can be understood that performing a differential operation on the interferometric data set according to the digital elevation in the SAR image coordinate system can eliminate the terrain phase contribution and effectively improve the accuracy of the interferometric phase. At the same time, the obtained differential interferometric data set corresponds one-to-one to the pixel position of the land cover map in the SAR image coordinate system, so that in the subsequent step of estimating the covariance matrix of the differential interferometric data set, the pixels in the window with the same category label as the center pixel can be accurately counted. At the same time, compared with the prior art, the overall calculation efficiency is also improved.

[0061] S104: Estimate the covariance matrix of the differential interferometry data set according to the land cover map in the SAR image coordinate system and the differential interferometry data set, and estimate and obtain the optimized time series phase according to a preset time series phase maximum likelihood estimation formula.

[0062] Specifically, sliding windows are established for the n(n-1) / 2 differential interferometry data sets in turn, and pixels with the same attributes as preset reference pixels in the sliding window corresponding to each differential interferometry data set are marked in turn according to the land cover map in the SAR image coordinate system, so that pixels with the same category label as the central pixel in the sliding window are counted in turn, and elements of the covariance matrix are calculated, so as to estimate the covariance matrix of the differential interferometry data set.

[0063] Specifically, according to the preset time series phase maximum likelihood estimation formula: ξ=e jθ ; thereby estimating and obtaining the optimized timing phase; wherein, is the optimized timing phase, θ is the positive value of the timing phase, represents the real part operator, and ∑ is the covariance matrix of the differential interference data set.

[0064] It should be noted that the L-view differential interference pattern is defined as s i and S k denote the i-th and k-th single-view complex images respectively, * denotes the conjugate operator, represents the terrain phase compensation term, which can be obtained from the Sentinel-1 satellite set parameters and the preset digital elevation model in this embodiment. In this embodiment, if the SAR intensity is not considered, the L-view differential interferogram is simplified to where φ i,kIn this embodiment, the covariance matrix of the single-view complex image set is defined as: in η i,k Transformed to Where M = L·Q. From the above formula, it can be seen that any element of the covariance matrix of the single-view complex image set can be obtained through the L-view differential interference pattern. Using M samples for the single-view complex image set is equivalent to using Q samples for the L-view data, thus greatly reducing the memory space of the input data. At the same time, through the above formula transformation, the covariance matrix ∑0 of the original data set can be restored from the covariance matrix ∑ of the L-view data set.

[0065] See also Figure 3 , which is a schematic diagram of the covariance matrix estimation of the L-view differential interferogram assisted by the land cover map in the SAR image coordinate system, by obtaining the i, k-th L-view average window in the original data and selecting homogeneous pixels through the land cover map in the SAR image coordinate system to estimate the covariance.

[0066] It can be understood that in the step of estimating the covariance matrix of the differential interferometry data set, instead of the method of statistical selection of homogeneous samples in the traditional distributed target data processing step, the pixels with the same attributes as the reference pixels in the current sliding window are marked by the land cover map under the SAR image coordinate system, and the pixels marked with the same category, i.e., homogeneous pixels, are directly used to estimate the covariance matrix, which greatly improves the calculation efficiency and has obvious advantages in terms of efficient calculation.

[0067] It should be noted that maximum likelihood estimation is a parameter estimation method that estimates the approximate value of the parameter by observing the results of several experiments. In this embodiment, the positive value θ of the timing phase and the covariance matrix of the differential interference data set are used to estimate the optimized timing phase

[0068] It can be understood that the optimized timing phase can be accurately estimated through the preset timing phase maximum likelihood estimation formula and the covariance matrix of the differential interferometer data set, thereby further improving the accuracy of SAR timing phase optimization.

[0069] See also Figure 2, which is a flow chart of an InSAR time series phase optimization method in an embodiment of the present invention. The acquired time series SAR data set is subjected to L-view processing to obtain an L-view SAR intensity data set and an L-view interferometric data set, and the digital elevation model and the land cover map are respectively registered and geocoded to obtain the digital elevation and land cover map in the SAR coordinate system; the digital elevation in the SAR coordinate system is subjected to a differential operation with the L-view interferometric data set to obtain an L-view differential interferometric data set, and with the assistance of the land cover map in the SAR coordinate system, the L-view similar targets are selected and the covariance matrix is ​​estimated, and the time series optimization phase is obtained according to the maximum likelihood time series phase estimation.

[0070] See also Figure 4 , which is a comparison diagram of the effects of another embodiment of the present invention and the original differential interference pattern and the optimization results of the existing method. The single-view complex image difference in the existing method and the embodiment of the present invention can effectively suppress noise and obtain a high signal-to-noise ratio phase. However, since the embodiment of the present invention uses L=5-view data, the image size is five times smaller than the result obtained by the traditional method. In this embodiment, a 2017 Sentinel-1TOPS mode 21-scene single-view complex data set, SRTM 30-meter resolution DEM and Esri 2020 global 10-meter resolution land cover map are selected, all data sets are aligned to a 20-meter grid, and the process in the third is used to estimate the time series phase. At the same time, the traditional method is used to select homogeneous samples for the data set using the KS test under single view, and then the same likelihood estimator is used to estimate the time series phase. In the process of estimating the covariance matrix, the KS test selects a window size of 11*21. The method of the present invention uses the slant range L=5 view to preprocess the data, and selects a 11*11 window size to estimate the covariance matrix, which is equivalent to an estimation window of 11*55 under single view; the results under the MATLAB2021a version test are shown in Table 1. The multi-view data (i.e., the data after L-view processing) input and parameter output occupy about 17% of the workspace space (169.98M) of the single view (978.73M), which proves that the use of multi-view data can greatly reduce memory space. The sample selection of the land cover map under the SAR image coordinates only takes one minute, and the efficiency is 272 times that of the traditional method, which proves the efficient computing capability of this embodiment and the potential for massive data processing.

[0071] Table 1 Comparison of MATLAB workspace memory consumption and statistical selection time consumption of different methods

[0072]

[0073]

[0074] Implementing the embodiments of the present invention has the following effects:

[0075] The embodiment of the present invention performs L-view processing on the time-series SAR data set after acquiring the time-series SAR data set. Compared with the prior art, the existing single-view complex data set is converted into an L-view data set. The workspace occupied by the L-view data input and parameter output is much lower than that of the single-view, which can greatly reduce the volume of the data set. The covariance matrix of the differential interferometric data set is estimated through the obtained land cover map in the SAR image coordinate system, which greatly improves the calculation efficiency and avoids the estimation through the homogeneous sample statistical selection method in the prior art.

[0076] Embodiment 2

[0077] Correspondingly, the present invention also provides an InSAR timing phase optimization device, comprising: an L-view processing module 201, a registration encoding module 202, a differential operation module 203 and a timing optimization module 204.

[0078] The L-view processing module 201 is used to acquire a time-series SAR data set, and perform L-view processing on the time-series SAR data set to obtain an L-view intensity data set and an interference data set respectively.

[0079] Specifically, the L-view processing module 201 is used to acquire n time-series SAR data sets, align the n time-series SAR data sets, and perform L-view processing on the aligned n time-series SAR data sets to obtain n SAR intensity sequences and n(n-1) / 2 interference data sets respectively; wherein n is a positive integer greater than 1.

[0080] The registration and coding module 202 is used to obtain and respectively register and geocode the preset digital elevation model and the preset land cover map with reference to the L visual intensity dataset, so as to obtain the digital elevation in the SAR image coordinate system and the land cover map in the SAR image coordinate system respectively.

[0081] Specifically, the registration and coding module 202 is used to obtain and register and geocode a preset digital elevation model with the L apparent intensity dataset using a preset cross-correlation maximization algorithm with the L apparent intensity dataset as a reference to obtain a digital elevation in a SAR image coordinate system; and obtain and register and geocode preset satellite orbit data information with a preset land cover map to obtain a land cover map in a SAR image coordinate system.

[0082] The differential operation module 203 is used to perform a differential operation on the interferometric dataset according to the digital elevation in the SAR image coordinate system to obtain a differential interferometric dataset.

[0083] Specifically, the differential operation module 203 is used to perform a differential operation on the n(n-1) / 2 interference data sets according to the digital elevation in the SAR image coordinate system to obtain n(n-1) / 2 differential interference data sets; wherein the differential interference data sets correspond one-to-one to the pixel positions of the land cover map in the SAR image coordinate system.

[0084] The timing optimization module 204 is used to estimate the covariance matrix of each pixel according to the land cover map in the SAR image coordinate system and the differential interference data set, and estimate and obtain the optimized timing phase according to a preset timing phase maximum likelihood estimation formula.

[0085] Specifically, the timing optimization module 204 is used to sequentially establish sliding windows for the n(n-1) / 2 differential interference data sets, and sequentially mark pixels with the same attributes as preset reference pixels in the sliding window corresponding to each differential interference data set according to the land cover map in the SAR image coordinate system, thereby sequentially counting pixels with the same category label as the central pixel in the sliding window, calculating the elements of the covariance matrix, and thus estimating the covariance matrix of each pixel.

[0086] Specifically, the timing optimization module 204 is further configured to estimate the timing phase according to a preset maximum likelihood estimation formula: ξ=e jθ ; thereby estimating and obtaining the optimized timing phase; wherein, is the optimized timing phase, θ is the positive value of the timing phase, represents the real part operator, and ∑ is the covariance matrix of the differential interference data set.

[0087] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0088] Implementing the above embodiments has the following effects:

[0089] The embodiment of the present invention performs L-view processing on the time-series SAR data set after acquiring the time-series SAR data set. Compared with the prior art, the embodiment of the present invention converts the existing single-view complex data set into an L-view data set. The workspace occupied by the L-view data input and parameter output is much lower than that of the single-view data. Therefore, the volume of the data set can be greatly reduced. At the same time, the covariance matrix of the differential interferometric data set is estimated by obtaining the land cover map in the SAR image coordinate system, which greatly improves the operation efficiency and avoids the estimation by the homogeneous sample statistical selection method in the prior art. The embodiment of the present invention has the ability of efficient operation and the potential for massive data processing.

[0090] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An InSAR timing phase optimization method, characterized in that: include: Acquire a time-series SAR data set, and perform L-view processing on the time-series SAR data set to obtain an L-view intensity data set and an interferometric data set respectively; Taking the L visual intensity dataset as a reference, obtaining and respectively registering and geocoding a preset digital elevation model and a preset land cover map, and obtaining a digital elevation in a SAR image coordinate system and a land cover map in a SAR image coordinate system, respectively; According to the digital elevation in the SAR image coordinate system, a differential operation is performed on the interferometric data set to obtain a differential interferometric data set; According to the land cover map in the SAR image coordinate system and the differential interferometry data set, the covariance matrix of the differential interferometry data set is estimated, and according to a preset maximum likelihood estimation formula for the time series phase, the optimized time series phase is estimated and obtained; The step of acquiring a time series SAR data set and performing L-view processing on the time series SAR data set to obtain an L-view intensity data set and an interferometric data set is as follows: Acquire a time-series SAR data set having n time-series SAR single-view images, register the n time-series SAR single-view images, perform L-view processing on the registered n time-series SAR single-view images, and obtain n SAR intensity sequences and n(n-1) / 2 interferometric data sets respectively; wherein n is a positive integer greater than 1; The step of performing a differential operation on the interferometric data set according to the digital elevation in the SAR image coordinate system to obtain a differential interferometric data set is specifically: According to the digital elevation in the SAR image coordinate system, a difference operation is performed on the n(n-1) / 2 interferometric data sets to obtain n(n-1) / 2 differential interferometric data sets; wherein the differential interferometric data sets correspond one-to-one to the pixel positions of the land cover map in the SAR image coordinate system; The method of estimating the covariance matrix of the differential interferometry dataset according to the land cover map in the SAR image coordinate system and the differential interferometry dataset is specifically: A sliding window is established for the n(n-1) / 2 differential interferometry data sets in sequence, and pixels with the same attributes as a preset reference pixel in the sliding window corresponding to each differential interferometry data set are marked in sequence according to the land cover map in the SAR image coordinate system, so that pixels with the same category label as the central pixel in the sliding window are counted in sequence, and elements of the covariance matrix are calculated, so as to estimate the covariance matrix of the differential interferometry data set.

2. The InSAR timing phase optimization method according to claim 1, characterized in that: The L visual intensity dataset is used as a reference to obtain and respectively register and geocode the preset digital elevation model and the preset land cover map, and obtain the digital elevation in the SAR image coordinate system and the land cover map in the SAR image coordinate system, specifically: Taking the L apparent intensity dataset as a reference, a preset cross-correlation maximization algorithm is adopted to obtain and align and geocode a preset digital elevation model with the L apparent intensity dataset to obtain a digital elevation in a SAR image coordinate system; and obtain and align and geocode preset satellite orbit data information with a preset land cover map to obtain a land cover map in a SAR image coordinate system.

3. The InSAR timing phase optimization method according to claim 1, characterized in that: The optimized timing phase is estimated and obtained according to the preset timing phase maximum likelihood estimation formula, specifically: According to the preset timing phase maximum likelihood estimation formula: , ; Thus estimating and obtaining the optimized timing phase; in, is the optimized timing phase, is a positive value of the timing phase, represents the real part operator, is the covariance matrix of the differential interferometry data set.

4. An InSAR timing phase optimization device, characterized in that: include: L-view processing module, registration encoding module, differential operation module and timing optimization module; The L-view processing module is used to acquire a time-series SAR data set, and perform L-view processing on the time-series SAR data set to obtain an L-view intensity data set and an interference data set respectively; The registration and coding module is used to obtain and respectively register and geocode a preset digital elevation model and a preset land cover map with reference to the L visual intensity dataset, so as to obtain a digital elevation in a SAR image coordinate system and a land cover map in a SAR image coordinate system respectively; The differential operation module is used to perform a differential operation on the interference data set according to the digital elevation in the SAR image coordinate system to obtain a differential interference data set; The timing optimization module is used to estimate the covariance matrix of the differential interferometry data set according to the land cover map in the SAR image coordinate system and the differential interferometry data set, and estimate and obtain the optimized timing phase according to a preset timing phase maximum likelihood estimation formula; The L-view processing module is used to obtain a time-series SAR data set and perform L-view processing on the time-series SAR data set to obtain an L-view intensity data set and an interference data set, respectively, specifically: Acquire a time-series SAR data set having n time-series SAR single-view images, register the n time-series SAR single-view images, perform L-view processing on the registered n time-series SAR single-view images, and obtain n SAR intensity sequences and n(n-1) / 2 interferometric data sets respectively; wherein n is a positive integer greater than 1; The differential operation module is used to perform a differential operation on the interferometric data set according to the digital elevation in the SAR image coordinate system to obtain a differential interferometric data set, specifically: According to the digital elevation in the SAR image coordinate system, a difference operation is performed on the n(n-1) / 2 interferometric data sets to obtain n(n-1) / 2 differential interferometric data sets; wherein the differential interferometric data sets correspond one-to-one to the pixel positions of the land cover map in the SAR image coordinate system; The timing optimization module is used to estimate the covariance matrix of the differential interferometry dataset according to the land cover map in the SAR image coordinate system and the differential interferometry dataset, specifically: A sliding window is established for the n(n-1) / 2 differential interferometry data sets in sequence, and pixels with the same attributes as a preset reference pixel in the sliding window corresponding to each differential interferometry data set are marked in sequence according to the land cover map in the SAR image coordinate system, so that pixels with the same category label as the central pixel in the sliding window are counted in sequence, and elements of the covariance matrix are calculated, so as to estimate the covariance matrix of the differential interferometry data set.

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