Optimal methods, media, and equipment for big data InSAR interferometry based on coherence proxy

Abstract

The present invention discloses a method for optimizing InSAR interferometer pairs based on coherence proxy for big data. The method calculates the coherence graph of sampled interferometer pairs, calculates the time baseline coherence proxy, the space baseline coherence proxy, the seasonal coherence proxy, and the comprehensive coherence proxy; selects a number of interferometer pairs with the highest coherence proxy as additional redundant interferometer pairs, takes the union of the redundant interferometer pairs and the interferometer pair network of the minimum spanning tree, and obtains a high coherence proxy interferometer pair network; detects whether all interferometer pairs in the high coherence proxy interferometer pair network can form a phase triangle closure, and completes the phase triangle closure for the interferometer pairs that have not formed a phase triangle closure to obtain the final InSAR optimized interferometer pair network. This optimization method not only improves the accuracy of phase unwrapping and deformation estimation, but also greatly reduces the number of interferometer pairs that need to be calculated in the case of big data InSAR, reduces the computational burden, and provides support for the realization of near-real-time processing of InSAR.

Description

Technical Field

[0001] The present invention relates to the field of synthetic aperture radar interferometry technology in the field of time series data processing technology, and in particular to a big data InSAR interferometry optimization method, medium and device based on coherence agent. Background Art

[0002] Interferometric pair optimization is an important step in the interferometric synthetic aperture radar (InSAR) time series processing workflow, playing a key role in improving the accuracy and spatial coverage density of InSAR deformation monitoring. Conventional interferometric pair optimization methods empirically set temporal and spatial baseline thresholds to eliminate pairs with significant temporal and spatial incoherence. This not only reduces the adverse effects of low-coherence interferometric pairs on phase unwrapping results and deformation estimation accuracy, but also significantly reduces computational costs.

[0003] However, Ansari et al. found that using only short-baseline interferometer pairs introduces fading signals, and this strategy also fails to account for the impact of seasonal incoherence. Therefore, some researchers have proposed directly calculating coherence metrics such as the average coherence or effective coherence ratio of all available interferometer pairs for interferometer pair optimization, demonstrating improved deformation accuracy and spatial coverage density. Other researchers have combined spectral clustering, all-pair shortest path algorithms, minimum spanning trees, and quadtree algorithms to optimize SAR interferometer pairs. However, the number of available interferometer pairs grows exponentially with the number of SAR data sets: N SAR images covering the same area can generate N(N□1) / 2 interferograms. In the era of InSAR big data, SAR data is rapidly increasing, and the number of interferometer pairs is exponentially increasing. Calculating coherence metrics for all available interferometer pairs is difficult to implement, and the high computational burden hinders the realization of near-real-time InSAR processing. Summary of the Invention

[0004] The purpose of the present invention is to address the problems existing in the prior art and to provide a method, medium and device for optimizing InSAR interferometry based on a coherence agent for big data.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for optimizing InSAR interferometry pairs based on big data based on coherence proxy includes the following steps:

[0007] According to SAR images covering the same area, randomly sampling from all interferometric pairs that can be generated, obtaining a number of sampled interferometric pairs, and calculating the coherence map of the sampled interferometric pairs;

[0008] calculating a temporal baseline coherence proxy, a spatial baseline coherence proxy, a seasonal coherence proxy, and a comprehensive coherence proxy based on the coherence map;

[0009] According to the temporal baseline coherence proxy, the spatial baseline coherence proxy, the seasonal coherence proxy and the comprehensive coherence proxy, a plurality of proportional interference pairs with the highest coherence proxy are selected as additional redundant interference pairs;

[0010] Calculating the interference pair network of the minimum spanning tree, taking the union of the redundant interference pairs and the interference pair network of the minimum spanning tree to obtain a high-coherence proxy interference pair network;

[0011] Whether all interference pairs in the high coherence proxy interference pair network can form phase triangle closure is detected, and the phase triangle closure is supplemented for the interference pairs that have not formed phase triangle closure to obtain the final InSAR optimal interference pair network.

[0012] This coherence agent-based big data InSAR interferometer pair optimization method not only improves the phase unwrapping and deformation estimation accuracy, but also greatly reduces the number of interferometer pairs that need to be calculated in the case of big data InSAR, alleviates the computational burden, and provides support for the realization of near-real-time InSAR processing.

[0013] During the data processing process, this preferred method fully considers the time baseline coherence proxy, the space baseline coherence proxy, the seasonal coherence proxy and the comprehensive coherence proxy, and can select a certain proportion of interference pairs with the highest coherence proxy. Combined with the minimum spanning tree algorithm, it avoids the loss of accuracy caused by too many independent subsets.

[0014] This optimization method can further detect whether the obtained high-coherence proxy interference pair network can form phase triangle closure, and can also complement the interference pairs that have not formed phase triangle closure, providing support for phase unwrapping error correction. The obtained interference pair network is the most preferred in the case of big data, which greatly improves the InSAR deformation monitoring accuracy and spatial coverage density.

[0015] Furthermore, the coherence graph is calculated as follows:

[0016] ,

[0017] Where, i and j represents the pixel position in the coherence map, γ(i, j) Indicates that in the pixel (i, j) The coherence coefficient at S 1 (i, j) and S2 (i, j) The reference image and secondary image are respectively in the pixel ( i , j ), S * represents the conjugate complex number, such as S 2 * (i, j) for S 2 (i, j) The complex conjugate of m and n is the size of the coherence window. m and n can be positive integers. |·| represents the absolute value of a complex number, for example | S 1 (i, j) |for S 1 (i, j) The absolute value of .

[0018] Furthermore, the time baseline coherence proxy is calculated as follows:

[0019] The average coherence of the interferometer pairs with the same time baseline is calculated. The average coherence is used as the dependent variable and the time baseline is used as the independent variable. The functional relationship between the average coherence and the time baseline is fitted using the following formula as a proxy for the time baseline coherence:

[0020] ,

[0021] Where, c time is the average coherence of the interferometric pairs with equal time baselines, B T is the time baseline, c 0 t and c ∞ t denote the short-term coherence and long-term coherence related to the time baseline, t t represents the temporal decorrelation rate, A t 、oh t 、 f t They represent the amplitude, angular frequency, and phase of the periodic variation of the baseline over time, respectively. |·| represents the absolute value, for example, | B T |for BT The absolute value of .

[0022] Furthermore, the calculation method of the spatial baseline coherence proxy is as follows:

[0023] The spatial baseline interval is divided into intervals of several meters. The average coherence of the interferometer pairs in the same spatial baseline interval is calculated and used as the dependent variable. The spatial baseline is used as the independent variable. The functional relationship between the two is fitted using the following formula as a proxy for the spatial baseline coherence:

[0024] ,

[0025] Where, c spatial is the average coherence in the same spatial baseline interval, B P is the spatial baseline, c 0 P and c ∞ P denote the short-term coherence and long-term coherence related to the spatial baseline, t P represents the spatial decorrelation rate.

[0026] Furthermore, the seasonal coherence proxy is calculated as follows:

[0027] For a certain date, the average coherence of all interferometric pairs whose primary or secondary image dates are the same as the date is calculated and used as the dependent variable. The date is used as the independent variable, and the functional relationship between the two is fitted using the following formula:

[0028] ,

[0029] Where, c P&S is the average coherence of images with the same image date, day P&S is the date difference between the main image or auxiliary image constituting the interferometric pair and the first SAR image. A S 、the S 、e S Represent the amplitude, horizontal offset and vertical offset of the cosine function respectively;

[0030] Then use the following formula as a proxy for seasonal coherence:

[0031] ,

[0032] Where, cseasonal is a proxy for seasonal coherence, day P and day S It is the date of the primary and secondary images that constitute the interferometric pair relative to the first SAR image.

[0033] Furthermore, the comprehensive coherence proxy is calculated as follows: the product of the time baseline coherence proxy, the space baseline coherence proxy and the seasonal coherence proxy is used as the comprehensive coherence proxy.

[0034] Furthermore, the minimum spanning tree interference pair network is calculated by using the comprehensive coherence agent as the edge weight, the SAR image and the interference pair as the points and edges in the graph theory, and using the minimum spanning tree algorithm to obtain the minimum spanning tree interference pair network.

[0035] Furthermore, the method for detecting and completing the phase triangle closure is as follows:

[0036] For any interference pair to be detected, all dates except the two dates of the main image and auxiliary image of the interference pair are traversed. If there is any date that forms an interference pair with the two dates of the main image and auxiliary image of the interference pair and is in the current interference pair network, then the interference pair is considered to have phase triangle closure, otherwise it does not have phase triangle closure; for interference pairs that do not have phase triangle closure, all dates except the two dates of the main image and auxiliary image of the interference pair are traversed, and the average coherence proxy of the interference pair formed with the two dates of the main image and auxiliary image of the interference pair is calculated. The highest value is taken and the corresponding interference pair is added to the interference pair network.

[0037] The present invention also provides a medium, which is a computer-readable storage medium, comprising a stored program, which implements the above-mentioned coherence agent-based big data InSAR interference optimization method when the program is executed by a processor.

[0038] The present invention also provides a device, which is an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the above-mentioned big data InSAR interference optimization method based on coherence agent.

[0039] Compared with the prior art, the beneficial effects of the present invention are: 1. This big data InSAR interferometer pair optimization method based on coherence proxy not only improves the phase unwrapping and deformation estimation accuracy, but also greatly reduces the number of interferometer pairs that need to be calculated in the case of big data InSAR, reduces the computational burden, and provides support for the realization of InSAR near-real-time processing; 2. During the data processing process, this optimization method fully considers the time baseline coherence proxy, the spatial baseline coherence proxy, the seasonal coherence proxy and the comprehensive coherence proxy, and can select a certain proportion of interferometer pairs with the highest coherence proxy. Combined with the minimum spanning tree algorithm, it avoids the accuracy drop caused by too many independent subsets; 3. This optimization method can further detect whether the obtained high-coherence proxy interferometer pair network can form a phase delta closure, and can also complement the interferometer pairs that have not formed a phase delta closure, providing support for phase unwrapping error correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of a method for optimizing InSAR interferometry based on a coherence agent for big data according to the present invention;

[0041] Figure 2 Calculation results of the coherence coefficient of the present invention: (a) is an example diagram of the temporal baseline coherence coefficient, (b) is an example diagram of the spatial baseline coherence coefficient, (c) is an example diagram of the seasonal coherence coefficient, and (d) is an example diagram of the comprehensive coherence coefficient;

[0042] Figure 3 Schematic diagram of the results of the intermediate process of interference graph optimization: (a) is the minimum spanning tree interference pair network, (b) is the high coherence proxy interference pair network;

[0043] Figure 4 Schematic diagram of interference pattern optimization results: (a) is the high coherence agent combined triangular phase closure interference network of the present invention, and (b) is the spatiotemporal baseline threshold method interference network. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] like Figure 1 As shown in FIG, a method for optimizing InSAR interferometry pairs based on a coherence agent based on big data includes the following steps:

[0046] Step 1: Based on N SAR images covering the same area, randomly sample a certain proportion of interferometer pairs from all the interferometer pairs that can be generated to obtain M interferometer pairs. Calculate the coherence map of these M interferometer pairs. The calculation method of the coherence map is as follows:

[0047] ,

[0048] Where, i and j represents the pixel position in the coherence map, S 1 (i, j) and S 2 (i, j) The reference image and secondary image are respectively in the pixel ( i , j ), S * represents the conjugate complex number, m and n is the size of the coherence window, and |·| represents the absolute value of the complex number.

[0049] Step 2: Based on the coherence graph, calculate the temporal baseline coherence proxy, the spatial baseline coherence proxy, the seasonal coherence proxy and the comprehensive coherence proxy.

[0050] The calculation method of the time baseline coherence proxy is as follows:

[0051] The average coherence of the interferometer pairs with the same time baseline is calculated. The average coherence is used as the dependent variable and the time baseline is used as the independent variable. The functional relationship between the average coherence and the time baseline is fitted using the following formula as a proxy for the time baseline coherence:

[0052] ,

[0053] Where, c time is the average coherence of the interferometric pairs with equal time baselines, i.e., a proxy for the time baseline coherence, B T is the time baseline, c 0 t and c ∞ t denote the short-term coherence and long-term coherence related to the time baseline, t t represents the temporal decorrelation rate, A t 、oht ,f t represent the amplitude, angular frequency, and phase of the periodic variation of the baseline over time, respectively, and |·| represents the absolute value.

[0054] The spatial baseline coherence proxy is calculated as follows:

[0055] The spatial baseline interval is divided into 5-meter intervals. The average coherence of the interference pairs in the same spatial baseline interval is calculated and used as the dependent variable. The spatial baseline is used as the independent variable. The functional relationship between the two is fitted using the following formula as a proxy for the spatial baseline coherence:

[0056] ,

[0057] Where, c spatial is the average coherence in the same spatial baseline interval, i.e., the spatial baseline coherence proxy, B P is the spatial baseline, c 0 P and c ∞ P denote the short-term coherence and long-term coherence related to the spatial baseline, t P represents the spatial decorrelation rate.

[0058] The seasonal coherence proxy is calculated as follows:

[0059] For a certain date, the average coherence of all interferometric pairs whose primary or secondary image dates are the same as the date is calculated and used as the dependent variable. The date is used as the independent variable, and the functional relationship between the two is fitted using the following formula:

[0060] ,

[0061] Where, c P&S is the average coherence of images with the same image date, day P&S is the date difference between the main image or auxiliary image constituting the interferometric pair and the first SAR image. A S 、the S 、e S Represent the amplitude, horizontal offset and vertical offset of the cosine function respectively;

[0062] Then use the following formula as a proxy for seasonal coherence:

[0063] ,

[0064] Where, c seasonal is a proxy for seasonal coherence, day P and day S is the date of the primary and secondary images constituting the interferometric pair relative to the first SAR image, A S 、the S 、e S All are utilization Calculated parameters.

[0065] The comprehensive coherence proxy is calculated as follows: the product of the temporal baseline coherence proxy, the spatial baseline coherence proxy, and the seasonal coherence proxy is used as a comprehensive coherence proxy, which is used as a proxy for coherence, as shown in the following formula:

[0066] .

[0067] In this embodiment, the calculation results of the time baseline coherence proxy, the space baseline coherence proxy, the seasonal coherence proxy and the comprehensive coherence proxy are as follows: Figure 2 shown.

[0068] Step 3: Using the comprehensive coherence agent as the edge weight, and the SAR image and interferometer pairs as the points and edges in the graph theory, the minimum spanning tree algorithm is used to obtain the interferometer pair network of the minimum spanning tree, as shown in Figure 3 (a)

[0069] According to the temporal baseline coherence proxy, the spatial baseline coherence proxy, the seasonal coherence proxy and the comprehensive coherence proxy, a comprehensive coherence proxy threshold is obtained, that is, a number of interference pairs with the highest coherence proxy ratio are selected as additional redundant interference pairs; the redundant interference pairs are combined with the interference pair network of the minimum spanning tree to obtain a high coherence proxy interference pair network, such as Figure 3 (b)

[0070] Step 4: Check whether all the interference pairs in the interference pair network obtained in step 3 can form phase triangle closure, and complete the phase triangle closure for the interference pairs that do not form phase triangle closure to obtain the final InSAR optimal interference pair network, as shown in Figure 4. Figure 4(a) shows the specific process for detecting and completing phase delta closure. For any interferometer pair to be detected, all dates except the two dates in the pair's primary and secondary images are traversed. If any date forms an interferometer pair with the two dates in the pair's primary and secondary images and is in the current interferometer pair network, the interferometer pair is considered to have phase delta closure. Otherwise, it is considered to have no phase delta closure. For interferometer pairs that do not have phase delta closure, all dates except the two dates in the pair's primary and secondary images are traversed. The average coherence proxy for the interferometer pair formed with the two dates in the pair's primary and secondary images is calculated. The highest value is taken and the corresponding interferometer pair is added to the interferometer pair network.

[0071] As a comparative method of the present invention, the interference effect of the network obtained by the spatiotemporal baseline threshold method is as follows: Figure 4 (b)

[0072] This coherence agent-based big data InSAR interferometer pair optimization method not only improves the phase unwrapping and deformation estimation accuracy, but also greatly reduces the number of interferometer pairs that need to be calculated in the case of big data InSAR, alleviates the computational burden, and provides support for the realization of near-real-time InSAR processing.

[0073] During data processing, this preferred method fully considers the temporal baseline coherence proxy, the spatial baseline coherence proxy, the seasonal coherence proxy, and the comprehensive coherence proxy. This method selects a certain proportion of interferometric pairs with the highest coherence proxies. Combined with the minimum spanning tree algorithm, this method avoids the accuracy loss caused by an excessive number of independent subsets. Combined with the detection and completion of phase delta closure, this method supports phase unwrapping error correction.

[0074] During specific implementation, the above process can be automatically operated using computer software (program) technology.

[0075] Specifically, the present invention also provides a medium, which is a computer-readable storage medium, and the computer-readable storage medium includes a stored program. When the program is executed by a processor, the above-mentioned big data InSAR interference optimization method based on coherence agent is implemented, and steps 1 to 4 are executed.

[0076] The present invention also provides a device, which is an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory, execute the above-mentioned coherence agent-based big data InSAR interference optimization method, and execute steps 1 to 4.

[0077] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired or wireless method. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0078] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A big data InSAR interferometry pair optimization method based on coherence proxy, characterized by: The steps include: According to SAR images covering the same area, randomly sampling from all interferometric pairs that can be generated, obtaining a number of sampled interferometric pairs, and calculating the coherence map of the sampled interferometric pairs; calculating a temporal baseline coherence proxy, a spatial baseline coherence proxy, a seasonal coherence proxy, and a comprehensive coherence proxy based on the coherence map; According to the temporal baseline coherence proxy, the spatial baseline coherence proxy, the seasonal coherence proxy and the comprehensive coherence proxy, a plurality of proportional interference pairs with the highest coherence proxy are selected as additional redundant interference pairs; Calculating the interference pair network of the minimum spanning tree, taking the union of the redundant interference pairs and the interference pair network of the minimum spanning tree to obtain a high-coherence proxy interference pair network; detecting whether all interference pairs in the high-coherence proxy interference pair network can form phase delta closure, and completing the phase delta closure for the interference pairs that have not formed phase delta closure, to obtain the final InSAR optimal interference pair network; Among them, the time baseline coherence agent calculates the average coherence of the interference pairs with the same time baseline, takes the average coherence as the dependent variable, takes the time baseline as the independent variable, and fits the functional relationship between the average coherence and the time baseline; the spatial baseline coherence agent divides the spatial baseline interval into intervals of several meters, calculates the average coherence of the interference pairs in the same spatial baseline interval, and takes it as the dependent variable, takes the spatial baseline as the independent variable, and fits the functional relationship between the two; the seasonal coherence agent is to calculate the average coherence of all interference pairs with the same main image or auxiliary image date as a certain date, and takes it as the dependent variable, takes the date as the independent variable, fits the functional relationship between the two, and calculates the product of the function values ​​with the main image date and the auxiliary image date of the interference pattern as the independent variables; the comprehensive coherence agent is to take the product of the time baseline coherence agent, the spatial baseline coherence agent and the seasonal coherence agent as the comprehensive coherence agent.

2. The method for optimizing InSAR interferometry based on a coherence proxy for big data according to claim 1, characterized in that: The coherence graph is calculated as follows: ， Where, i and j represents the pixel position in the coherence map, γ(i, j) Indicates that in the pixel (i, j) The coherence coefficient at S 1 (i, j) and S 2 (i, j) The reference image and secondary image are respectively in the pixel ( i , j ), S * represents the conjugate complex number, m and n is the size of the coherence window.

3. The method for optimizing InSAR interferometry based on a coherence proxy for big data according to claim 1, characterized in that: The temporal baseline coherence proxy is calculated as follows: ， Where, γ time is the average coherence of the interferometric pairs with equal time baselines, B T is the time baseline, γ 0 t and γ ∞ t denote the short-term coherence and long-term coherence related to the time baseline, τ t represents the temporal decorrelation rate, A t 、ω t 、φ t They represent the amplitude, angular frequency and phase of the periodic variation of the baseline over time.

4. The method for optimizing InSAR interferometry based on a coherence proxy for big data according to claim 1, wherein: The spatial baseline coherence proxy is calculated as follows: ， Where, γ spatial is the average coherence in the same spatial baseline interval, B P is the spatial baseline, γ 0 P and γ ∞ P denote the short-term coherence and long-term coherence related to the spatial baseline, τ P represents the spatial decorrelation rate.

5. The method for optimizing InSAR interferometry based on a coherence proxy for big data according to claim 1, wherein: The seasonal coherence proxy is calculated as follows: ， Where, γ P&S is the average coherence of images with the same image date, day P&S is the date difference between the main image or auxiliary image constituting the interferometric pair and the first SAR image. A S ,θ S , ε S Represent the amplitude, horizontal offset and vertical offset of the cosine function respectively; Then use the following formula as a proxy for seasonal coherence: ， Where, γ seasonal is a proxy for seasonal coherence, day P and day S It is the date of the primary and secondary images that constitute the interferometric pair relative to the first SAR image.

6. The method for optimizing InSAR interferometry based on a coherence proxy for big data according to claim 1, wherein: The minimum spanning tree interference pair network is calculated by using the comprehensive coherence agent as the edge weight, the SAR image and the interference pair as the points and edges in the graph theory, and using the minimum spanning tree algorithm to obtain the minimum spanning tree interference pair network.

7. The method for optimizing InSAR interferometry based on a coherence proxy for big data according to claim 1, wherein: The method for detecting and completing phase delta closure is as follows: For any interference pair to be detected, all dates except the two dates of the main image and auxiliary image of the interference pair are traversed. If there is any date that forms an interference pair with the two dates of the main image and auxiliary image of the interference pair and is in the current interference pair network, then the interference pair is considered to have phase triangle closure, otherwise it does not have phase triangle closure; for interference pairs that do not have phase triangle closure, all dates except the two dates of the main image and auxiliary image of the interference pair are traversed, and the average coherence proxy of the interference pair formed with the two dates of the main image and auxiliary image of the interference pair is calculated. The highest value is taken and the corresponding interference pair is added to the interference pair network.

8. A medium, which is a computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, and when the program is executed by a processor, the big data InSAR interferometry optimization method based on coherence agent according to any one of claims 1 to 7 is implemented.

9. A device, which is an electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the coherence agent-based big data InSAR interference optimization method according to any one of claims 1 to 7.

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