InSAR atmospheric vertical layering delay correction method and device

Abstract

The application discloses an InSAR atmospheric vertical layering delay correction method and device, belongs to the technical field of synthetic aperture radar interferometry, and comprises the following steps: acquiring time sequence SAR image data of a monitoring area, performing differential interference processing, phase unwrapping processing and least square calculation, and obtaining time sequence unwrapping phases corresponding to each SAR image; performing time domain low-pass filtering on the time sequence unwrapping phases to obtain time sequence low-frequency phases; performing local window segmentation according to a digital elevation model; dividing height difference levels according to the statistical distribution characteristics of the elevation data in each window; constructing a phase-elevation model in each height difference level, estimating an atmospheric vertical layering delay phase, removing the atmospheric vertical layering delay phase, and obtaining a phase after atmospheric vertical layering delay correction. The application deals with the spatial heterogeneity of the atmospheric delay through local window segmentation, avoids fitting deviation caused by the mixing of samples in different elevation intervals through the standard score method height difference layering, and effectively improves the estimation accuracy of the atmospheric vertical layering delay in a complex terrain area.

Description

Technical Field

[0001] This application relates to the field of interferometric synthetic aperture radar (InSAR) technology, specifically to an InSAR atmospheric vertical stratification delay correction method and device, which is applicable to atmospheric delay correction in spaceborne synthetic aperture radar interferometry, and is especially suitable for high-precision surface deformation monitoring in complex terrain areas. Background Technology

[0002] Synthetic Aperture Radar (SAR) plays a crucial role in remote sensing due to its all-weather, all-day observation capabilities and strong penetration power. Synthetic Aperture Radar Interferometry (InSAR), a powerful geodetic technique, can directly acquire large-scale, high-precision surface elevation and deformation information from space. With the development of time-series InSAR technologies (such as SBAS-InSAR and PS-InSAR), surface deformation monitoring accuracy can reach the millimeter level, and the monitoring point density far exceeds that of traditional leveling and GPS measurements. However, InSAR measurement accuracy is significantly affected by atmospheric delay, especially the atmospheric vertical stratification delay (also known as tropospheric static delay), which is strongly correlated with topographic height and is one of the main factors limiting the accuracy of InSAR deformation monitoring.

[0003] Existing atmospheric vertical stratification delay correction methods are mainly divided into two categories: The first category is based on external data, such as using GNSS station data, satellite imaging spectrometers, or numerical weather models (e.g., ERA5) to calculate the total zenith delay, and then obtaining the atmospheric delay phase through spatial interpolation. This type of method depends on the accuracy and spatiotemporal resolution of the external data, and its applicability is limited in areas with sparse or missing data. The second category is based on phase-elevation models, which utilize the strong correlation between atmospheric vertical stratification delay and elevation to construct global or local phase-elevation regression models for correction. This type of method is currently the mainstream approach, but most existing methods assume a strictly linear relationship between atmospheric vertical stratification delay and elevation within a local window, failing to fully consider the nonlinear variation characteristics of atmospheric delay with elevation in complex terrain areas. This results in poor correction effects in steep terrain and areas with drastic elevation changes, and residual delay still affects the accuracy of deformation inversion.

[0004] For example, Liang Hongyu et al. implemented quadtree window segmentation by setting a minimum unit side length and an elevation threshold, and then constructed a joint local linear vertical atmospheric, elevation error, and deformation model within each window. However, this method still uses a single linear model within each window, failing to address the issue of the linear relationship between atmospheric delay and elevation deviation when the elevation difference within the window is large. Béjar-Pizarro et al. constructed a phase-elevation linear model within a 10km×10km window, but the model fitting error increased significantly when the elevation difference within the window was large. Therefore, there is an urgent need for a correction method that can adapt to complex terrain and accurately characterize the atmospheric vertical stratification delay as a function of elevation. Summary of the Invention

[0005] To address the technical problem of insufficient correction accuracy in complex terrain areas using existing InSAR atmospheric vertical stratification delay correction methods, this application provides an InSAR atmospheric vertical stratification delay correction method and apparatus. By combining local window segmentation with elevation difference stratification, phase-elevation models are constructed in each elevation difference level, significantly improving the accuracy and adaptability of atmospheric vertical stratification delay correction.

[0006] The technical solution adopted by this application to solve its technical problem is: On the one hand, an InSAR atmospheric vertical stratification delay correction method is provided, including the following steps: Step S1: Acquire time-series SAR image data of the monitoring area, perform differential interferometry processing, phase unwrapping processing and least squares calculation to obtain the time-series unwrapped phase corresponding to each SAR image; Step S2: Perform time-domain low-pass filtering on the time-series unwrapped phase to separate the time-domain high-frequency components and obtain the time-series low-frequency phase; Step S3: Based on the digital elevation model of the monitoring area, the monitoring area is divided into local windows to obtain several local windows; Step S4: Within each local window, the elevation difference levels are divided according to the statistical distribution characteristics of the elevation data, and each local window is divided into several elevation difference levels. Step S5: Within each elevation difference level, construct a phase-elevation model, estimate the atmospheric vertical stratification delay phase of each elevation difference level, and remove the estimated atmospheric vertical stratification delay phase from the time-series low-frequency phase to obtain the atmospheric vertical stratification delay-corrected phase.

[0007] Preferably, step S1 specifically includes: acquiring time-series SAR image data of the monitoring area, and extracting corresponding digital elevation model data based on the latitude and longitude range of the monitoring area; preprocessing the time-series SAR image data, including format conversion to generate single-view complex data, orbit parameter update, and image registration, to obtain registered time-series SAR images; performing interferometric processing on the registered time-series SAR images, based on the small baseline principle, forming interferometric pairs of image pairs that meet the time baseline threshold and spatial baseline threshold, and performing interferometric and differential interferometric processing on each interferometric pair to obtain multiple wrapped interferometric phases; performing phase unwrapping processing on the wrapped interferometric phases to obtain unwrapped phases of each interferogram; and performing least squares calculation on each unwrapped phase of the interferogram to obtain the time-series unwrapped phase corresponding to each SAR image.

[0008] Preferably, in step S2, the time-domain low-pass filtering is a dual-scale time-domain low-pass filtering, which includes two stages: small-scale filtering and large-scale filtering.

[0009] Preferably, in step S3, the local window segmentation is either quadtree segmentation or uniform window segmentation.

[0010] Preferably, in step S4, the elevation difference hierarchy division based on the statistical distribution characteristics of the elevation data includes calculating the average value and standard deviation of the elevation data within a local window, calculating the standard score of the elevation of each high coherence point, and using integer multiples of the standard score as hierarchical nodes for division. Pixels with coherence higher than a preset threshold are selected as high coherence points.

[0011] Preferably, in step S5, the phase-elevation model is a linear model, and the model parameters are solved using the least squares method.

[0012] On the other hand, an InSAR atmospheric vertical stratification delay correction device is provided, comprising: The interferogram processing module is used to acquire time-series SAR image data of the monitoring area, perform differential interferometry, phase unwrapping, and least squares calculations to obtain the time-series unwrapped phase corresponding to each SAR image; The time-domain filtering module is used to perform time-domain low-pass filtering on the time-series unwrapped phase to separate the time-domain high-frequency components and obtain the time-series low-frequency phase. The window segmentation module is used to segment the monitoring area into several local windows based on the digital elevation model of the monitoring area. The elevation difference layering module is used to divide the elevation difference into several elevation difference layers within each local window based on the statistical distribution characteristics of the elevation data. The model building module is used to build a phase-elevation model within each elevation difference level, estimate the atmospheric vertical stratification delay phase of each elevation difference level, and remove the estimated atmospheric vertical stratification delay phase from the time-series low-frequency phase to obtain the atmospheric vertical stratification delay-corrected phase.

[0013] Thirdly, a computer device is provided, including a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor executes the machine-readable instructions to implement any of the above-mentioned InSAR atmospheric vertical stratification delay correction methods.

[0014] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program for executing any of the above-described InSAR atmospheric vertical stratification delay correction methods.

[0015] One of the above technical solutions has the following advantages or beneficial effects: (1) Improved the estimation accuracy of atmospheric vertical stratification delay. This application further divides the elevation difference into levels based on local window segmentation, decomposing windows with large elevation variation ranges into multiple subsets with smaller elevation variation ranges, and independently constructing phase-elevation models in each subset. This avoids fitting bias caused by mixing samples from different elevation intervals, enabling the phase-elevation model to more accurately capture the local variation law of atmospheric vertical stratification delay with elevation.

[0016] (2) Adaptive elevation difference stratification is achieved. This application introduces the standard score method for elevation difference stratification, which can adaptively determine the stratification boundary according to the actual statistical distribution characteristics of the elevation data within the window, without the need to preset a fixed stratification interval, and is especially suitable for complex areas with uneven terrain distribution.

[0017] (3) Enhanced the stability of the phase-elevation model. Through the stratification of elevation differences, the number of samples in each level is moderate and the elevation distribution is more concentrated, which significantly improves the robustness of the least squares estimation and the estimation accuracy of the model parameters is higher.

[0018] (4) The synergy of multiple technical means produced unexpected technical effects. The three technical means of this application, namely "local window segmentation", "standard score method for elevation difference level division", and "independent construction of phase-elevation models within each level", have a close synergistic relationship: local window segmentation solves the spatial heterogeneity problem of atmospheric delay; standard score method for elevation difference level division further solves the nonlinear fitting deviation caused by uneven elevation distribution within the window on the basis of window segmentation; and independent modeling within each level makes full use of the processing results of the first two steps. The three complement each other and advance step by step, producing a significant synergistic effect. Experimental data show that after processing with the method of this application, the phase standard deviation of the interferogram decreased from the original 2.87 rad to 0.63 rad, which is about 36% lower than that of using only dual-scale time-domain low-pass filtering (0.99 rad), and about 43.8% lower than that of the closest existing technology (global iterative linear phase-elevation regression model, 1.12 rad), achieving unexpected technical effects. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an InSAR atmospheric vertical stratification delay correction method according to an exemplary embodiment; Figure 2 This is a schematic diagram of the structure of an InSAR atmospheric vertical stratification delay correction device according to an exemplary embodiment; Figure 3 This is a schematic diagram of an experimental area according to an exemplary embodiment; Figure 4 This is a quadtree partitioning graph illustrated according to an exemplary embodiment; Figure 5 This is a schematic diagram illustrating a specific elevation difference layering according to an exemplary embodiment; Figure 6 This is a schematic diagram illustrating the comparison of unwrapped interferograms after being corrected by different methods, according to an exemplary embodiment. Detailed Implementation

[0020] To more clearly illustrate the technical features of this application, the following detailed description is provided through specific embodiments and in conjunction with the accompanying drawings.

[0021] Example 1 like Figure 1 As shown in this embodiment, an InSAR atmospheric vertical stratification delay correction method includes the following steps: Step S1: Obtain time-series SAR image data of the monitoring area, perform differential interferometry processing, phase unwrapping processing, and least squares calculation to obtain the time-series unwrapped phase corresponding to each SAR image.

[0022] Step S1 specifically includes: acquiring time-series SAR image data of the monitoring area; extracting corresponding digital elevation model data based on the latitude and longitude range of the monitoring area; preprocessing the time-series SAR image data, including format conversion to generate single-view complex data, orbit parameter updates, and image registration, to obtain registered time-series SAR images; performing interferometric processing on the registered time-series SAR images; based on the small baseline principle, forming interferometric pairs by combining image pairs that meet the time baseline threshold and spatial baseline threshold; performing interferometric and differential interferometric processing on each interferometric pair to obtain multiple wrapped interferometric phases; performing phase unwrapping processing on the wrapped interferometric phases to obtain unwrapped phases of each interferogram; and performing least squares calculation on each unwrapped phase of the interferogram to obtain the temporal unwrapped phase corresponding to each SAR image. The small baseline principle refers to selecting image pairs whose time baseline and spatial baseline are both less than a preset threshold to form interferometric pairs.

[0023] Step S2: Perform time-domain low-pass filtering on the time-series unwrapped phase to separate the time-domain high-frequency components and obtain the time-series low-frequency phase.

[0024] The time-domain low-pass filtering is a dual-scale time-domain low-pass filtering, which includes two stages: small-scale filtering and large-scale filtering. First, small-scale filtering is performed to suppress atmospheric turbulence delay and random noise in the time-series unwrapped phase, resulting in a first low-frequency phase and a first high-frequency phase. Then, large-scale filtering is performed to smooth the first high-frequency phase and extract a small amount of high-frequency deformation signal to obtain a second low-frequency phase. The first low-frequency phase and the second low-frequency phase are superimposed to obtain the time-series low-frequency phase.

[0025] Both the small-scale filtering and the large-scale filtering are implemented using a Gaussian weighted average method. The low-frequency phase of the time sequence is obtained according to equations (1) to (6): (1), (2), (3), (4), (5), (6), In the formula, , They are The imaging time of the SAR image; Indicates the imaging date of the filtered data; This refers to the size of a small-scale time window; This refers to the size of a large-scale time window; This represents the Gaussian function; it represents the first low-frequency phase after small-scale filtering. This represents the first high-frequency phase after small-scale filtering; This represents the second low-frequency phase after large-scale filtering; This is the low-frequency phase of the timing sequence.

[0026] Step S3: Based on the digital elevation model of the monitoring area, the monitoring area is divided into local windows to obtain several local windows.

[0027] The local window segmentation can be achieved using quadtree segmentation or uniform window segmentation.

[0028] The local window segmentation adopts a quadtree segmentation method, which includes: setting a minimum window side length and a maximum elevation difference threshold, recursively segmenting the digital elevation model until the elevation difference within each window is less than the maximum elevation difference threshold or the window side length reaches the minimum window side length.

[0029] As an alternative, local window segmentation can also employ uniform window segmentation, dividing the monitoring area into several uniform rectangular windows of a preset size. Overlapping areas can be set between local windows, with an overlap width of 1 / 4 to 1 / 2 of the window size. The window size can be adaptively determined based on the terrain undulation—smaller windows are used in areas with dramatic terrain undulations, and larger windows are used in areas with gentle terrain. In quadtree segmentation, the minimum window size is determined based on the minimum spatial distribution density of highly coherent points within the monitoring area, ensuring that each minimum window contains a sufficient number of highly coherent points for subsequent modeling.

[0030] Step S4: Within each local window, the elevation difference levels are divided according to the statistical distribution characteristics of the elevation data, and each local window is divided into several elevation difference levels.

[0031] The specific process for dividing elevation difference levels based on the statistical distribution characteristics of elevation data is as follows: Calculate the average elevation data of all highly coherent points within a local window. and standard deviation ; Calculate the standard score of the elevation of each high coherence point according to formula (7). z : (7), in, h The elevation value of the high coherence point; Elevation is divided into several elevation difference levels using integer multiples of standard scores as grading nodes.

[0032] In step S4, if the difference between the maximum elevation value and the minimum elevation value within a local window is less than a preset elevation threshold, then the local window will not be divided into elevation difference levels, but will be directly treated as a single elevation difference level.

[0033] In step S4, the minimum and maximum values ​​of the elevation data within the local window are used as boundary constraints to ensure that the divided elevation intervals completely cover the entire range of elevation values ​​within the window.

[0034] Step S5: Within each elevation difference level, construct a phase-elevation model, estimate the atmospheric vertical stratification delay phase of each elevation difference level, and remove the estimated atmospheric vertical stratification delay phase from the time-series low-frequency phase to obtain the atmospheric vertical stratification delay-corrected phase.

[0035] The phase-elevation model is a linear model, expressed as follows: (8), (9), (10) (11), (12), In the formula, Indicates the atmospheric vertical stratification delay phase. This represents the atmospheric vertical stratification delay phase vector corresponding to the i-th elevation difference level. Indicates the coefficient to be determined; Indicates the elevation value of the high coherence point; Represents the Kronecker tensor product; The matrix representing the correspondence between the main and auxiliary images of SAR images and differential interferograms within each local elevation difference level (1 and -1 represent the main and auxiliary images, respectively, and 0 represents irrelevant images). This represents the design matrix constructed based on the elevation of highly coherent points within each elevation difference level; This represents the parameter matrix that needs to be solved.

[0036] Further, after step S5, the phase corrected for atmospheric vertical stratification delay can be subjected to spatiotemporal filtering of residual turbulent delay to remove the residual turbulent delay component. Specifically, this includes: performing time-dimensional high-pass filtering on the time-dimensional differential phase sequence of each high coherence point to obtain a high-pass component; performing spatial-dimensional low-pass filtering on each interferogram to obtain a low-pass component as the residual turbulent delay component; and subtracting the residual turbulent delay component from the phase corrected for atmospheric vertical stratification delay.

[0037] Then, based on the phase correction after atmospheric vertical stratification delay, deformation information modeling is performed to obtain the average deformation rate and time-series deformation at each point. Simultaneously, the phase standard deviation of the interferogram after atmospheric vertical stratification delay correction is calculated as a quantitative indicator to evaluate the correction effect.

[0038] Example 2 like Figure 2 As shown, an InSAR atmospheric vertical stratification delay correction device according to this embodiment includes: The interferogram processing module is used to acquire time-series SAR image data of the monitoring area, perform differential interferometry, phase unwrapping, and least squares calculations to obtain the time-series unwrapped phase corresponding to each SAR image; The time-domain filtering module is used to perform time-domain low-pass filtering on the time-series unwrapped phase to separate the time-domain high-frequency components and obtain the time-series low-frequency phase. The window segmentation module is used to segment the monitoring area into several local windows based on the digital elevation model of the monitoring area. The elevation difference layering module is used to divide the elevation difference into several elevation difference layers within each local window based on the statistical distribution characteristics of the elevation data. The model building module is used to build a phase-elevation model within each elevation difference level, estimate the atmospheric vertical stratification delay phase of each elevation difference level, and remove the estimated atmospheric vertical stratification delay phase from the time-series low-frequency phase to obtain the atmospheric vertical stratification delay-corrected phase.

[0039] The specific data processing flow of each module is the same as steps S1 to S5 in Example 1, and will not be repeated here. Among them, the dual-scale time-domain low-pass filtering module performs the dual-scale time-domain low-pass filtering calculation as described above.

[0040] This application constructs a phase-elevation model within each elevation level based on local elevation differences and calculates the atmospheric vertical stratification delay within each elevation level, thus realizing the correction of InSAR atmospheric vertical stratification delay. The following section provides a detailed introduction to several key technologies involved in this application.

[0041] 1. Dual-scale time-domain low-pass filtering: In time-series InSAR technology, based on the time-domain characteristics of the interferogram phase—namely, atmospheric turbulence and noise are high-frequency features, while deformation and atmospheric vertical stratification delay are typically low-frequency features—a suitable time-domain low-pass filter can be designed to separate high-frequency noise and obtain the low-frequency phase.

[0042] The low-frequency phase of the timing sequence is obtained according to equation (1-6): (1), (2), (3), (4), (5), (6), In the formula, , They are The imaging time of the SAR image; Indicates the imaging date of the filtered data; This refers to the size of a small-scale time window; This refers to the size of a large-scale time window; This represents the Gaussian function; it represents the first low-frequency phase after small-scale filtering. This represents the first high-frequency phase after small-scale filtering; This represents the second low-frequency phase after large-scale filtering; This is the low-frequency phase of the timing sequence.

[0043] 2. Local elevation difference stratification and correction: (1) Local elevation difference stratification: Based on the filtered phase, a local window segmentation method is used for segmentation. Within each window, the elevation is standardized and calculated. Based on the elevation standard score, the elevation difference is divided into several elevation difference levels using grading nodes that are multiples of the standard deviation.

[0044] (7), In the formula, h The elevation value of the high coherence point; This represents the average elevation. This represents the standard deviation of elevation.

[0045] Furthermore, for any given local window, if the maximum elevation value within the window is less than 400m, no stratification is performed. Simultaneously, the minimum and maximum elevation values ​​within the window are introduced as boundary constraints to ensure that the divided elevation intervals completely cover the entire elevation range within the window, avoiding data omissions or missing boundaries.

[0046] (2) Atmospheric vertical stratification delay correction: Within the defined elevation difference levels, based on the topographic high correlation characteristics of atmospheric vertical stratification delay, a phase-elevation linear model is constructed as shown in Equation (8-12). The atmospheric vertical stratification delay phase is calculated, and the differential low-frequency phase is corrected to obtain the corrected phase.

[0047] (8), (9), (10) (11), (12), In the formula, Indicates the atmospheric vertical stratification delay phase. This represents the atmospheric vertical stratification delay phase vector corresponding to the i-th elevation difference level. Indicates the coefficient to be determined; Indicates the elevation value of the high coherence point; Represents the Kronecker tensor product; The matrix representing the correspondence between the main and auxiliary images of SAR images and differential interferograms within each local elevation difference level (1 and -1 represent the main and auxiliary images, respectively, and 0 represents irrelevant images). This represents the design matrix constructed based on the elevation of highly coherent points within each elevation difference level; This represents the parameter matrix that needs to be solved.

[0048] Example 3 (Experimental Example) The feasibility and practicality of the technical solution in this application will be verified through the following specific calculation examples.

[0049] 1. Experimental Data: This experiment selected a specific island, such as... Figure 3 As shown, the island is located at the southernmost tip of the Hawaiian Islands. Its topography is centered around five shield volcanoes, and its climate exhibits a "multi-climate" characteristic. The diverse climate and complex topography make this island an ideal region for conducting InSAR tropospheric atmospheric delay corrections.

[0050] The experimental data include: (1) Copernicus Digital Elevation Model (GLO-30 DEM), which has few missing values ​​and relatively high accuracy. (2) 27 Sentinel-1A SAR images acquired from February 9, 2024 to January 10, 2025.

[0051] Experimental Procedure: This experiment is based on MT-InSAR technology. The acquired SAR images are subjected to interferometric image pair combination (with a time baseline of 36 days and a spatial baseline of 400 meters), coarse image registration, fine image registration, azimuth / range spectral filtering, differential interferogram, deflating and de-topography, phase filtering, phase unwrapping (minimum cost flow method), and atmospheric vertical stratification delay correction according to the method of this application.

[0052] 2. Experimental Results: The atmospheric vertical stratification delay correction method for InSAR described in this application is used to perform atmospheric delay correction on all unwrapped interferograms. Taking the unwrapped interferogram 20240209_20240316 as an example, the results of the original uncorrected, dual-scale time-domain low-pass filter correction, and correction by the proposed method are shown as follows. Figure 6 As shown.

[0053] Based on equations (1) to (6), a dual-scale time-domain low-pass filter was implemented, resulting in a differential interferogram that removes high-frequency noise such as atmospheric turbulence and mainly contains low-frequency components, as shown below. Figure 6 As shown in (b).

[0054] The study area was then segmented using a quadtree, and the specific segmentation results are as follows: Figure 4 As shown in the figure. Subsequently, elevation differences were categorized within each window according to the standard score method. The elevation difference levels within a given window are shown below. Figure 5 As shown. Subsequently, phase-elevation models were constructed within each elevation difference level to correct for atmospheric delay, and the final correction results were obtained.

[0055] like Figure 6 As shown in (a), the uncorrected interferogram not only faces severe incoherence and poor phase quality in vegetated areas, but also exhibits strong tropospheric atmospheric delay in complex terrain areas. This results in a visually apparent "hierarchical stripe structure" in the interferograms around Mauna Kea and Mauna Loa volcanoes, with its spatial distribution showing strong consistency with topographic elevation changes. However, after removing this structure using a dual-scale time-domain low-pass filter, as shown in (a), the interferogram becomes clearer. Figure 6 As shown in (b), the "strip" effect is weakened and merged into a single sheet, reflecting that this method still has certain shortcomings in suppressing tropospheric atmospheric delay. The interferogram processed by the method shown in this application generally exhibits relatively consistent correction characteristics, such as... Figure 6 As shown in (c). Compared with the aforementioned methods, the proposed method can not only effectively reduce the terrain-related strip-shaped layered delay, but also further suppress the local noise phase in the interferogram, making the corrected phase field smoother and more continuous, and the spatial structure clearer.

[0056] 3. Analysis of Experimental Results: Based on the above correction results, it can be found that the advantages of using local window elevation difference layering are mainly to avoid the deviation caused by mixing different elevation intervals; to enhance the atmospheric consistency between pixels with similar elevations; and to improve the stability of the local phase-elevation regression model. Subsequently, quantitative calculations were performed on the interferograms corrected by different methods. The phase standard deviation of interferograms 20240209-20240316 decreased from the uncorrected 2.87 rad to 0.99 rad and 0.63 rad, respectively. The proposed method effectively removes tropospheric atmospheric delay, reduces the phase standard deviation in the interferograms, and improves the phase quality of the differential interferograms.

[0057] Example 4 This application provides a computer device including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to implement any of the InSAR atmospheric vertical stratification delay correction methods described above.

[0058] Specifically, the aforementioned memory and processor can be general-purpose memory and processor, without any specific limitations. When the processor runs the computer program stored in the memory, it can execute the aforementioned method for comprehensively optimizing test resources.

[0059] Those skilled in the art will understand that the structure of the computer device does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine some components, or split some components, or have different component arrangements.

[0060] In some embodiments, the computer device may further include a touchscreen for displaying a graphical user interface (e.g., an application launch screen) and receiving user actions on the graphical user interface (e.g., launching an application). Specifically, the touchscreen may include a display panel and a touch panel. The display panel may be configured as an LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or similar type. The touch panel can collect user touch or non-touch operations on or near it and generate pre-set operation instructions, such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel. Additionally, the touch panel may include a touch detection device and a touch controller. The touch detection device detects the user's touch position and posture, and detects the signals generated by the touch operation, transmitting the signals to the touch controller. The touch controller receives touch information from the touch detection device, converts it into information that the processor can process, sends it to the processor, and can also receive and execute commands from the processor. Furthermore, touch panels can be implemented using various types of sensors, including resistive, capacitive, infrared, and surface acoustic wave sensors, as well as any future technologies. Moreover, the touch panel can cover the display panel. Users can operate on or near the touch panel, which is covered by the graphical user interface displayed on the display panel. After detecting the operation on or near the touch panel, the touch panel transmits it to the processor to determine the user input. The processor then responds to the user input by providing corresponding visual output on the display panel. Additionally, the touch panel and display panel can be implemented as two separate components or integrated together.

[0061] Corresponding to the above application startup method, this application embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the InSAR atmospheric vertical stratification delay correction method as described above.

[0062] The application launch device provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The device provided in this application embodiment has the same implementation principle and technical effects as the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0063] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface, and the indirect coupling or communication connection of the apparatus or modules may be electrical, mechanical, or other forms.

[0065] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0066] In addition, the functional modules in the embodiments provided in this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0067] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0068] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation methods of this application. Any modifications or equivalent substitutions that do not depart from the spirit and scope of this application should be covered within the protection scope of the claims of this application.

Claims

1. A method for InSAR atmospheric vertical stratification delay correction, characterized in that, Includes the following steps: Step S1: Acquire time-series SAR image data of the monitoring area, perform differential interferometry processing, phase unwrapping processing and least squares calculation to obtain the time-series unwrapped phase corresponding to each SAR image; Step S2: Perform time-domain low-pass filtering on the time-series unwrapped phase to separate the time-domain high-frequency components and obtain the time-series low-frequency phase; Step S3: Based on the digital elevation model of the monitoring area, the monitoring area is divided into local windows to obtain several local windows; Step S4: Within each local window, the elevation difference levels are divided according to the statistical distribution characteristics of the elevation data, and each local window is divided into several elevation difference levels. Step S5: Within each elevation difference level, construct a phase-elevation model, estimate the atmospheric vertical stratification delay phase of each elevation difference level, and remove the estimated atmospheric vertical stratification delay phase from the time-series low-frequency phase to obtain the atmospheric vertical stratification delay-corrected phase.

2. The InSAR atmospheric vertical stratification delay correction method according to claim 1, characterized in that, Step S1 includes: acquiring time-series SAR image data of the monitoring area, and extracting corresponding digital elevation model data based on the latitude and longitude range of the monitoring area; preprocessing the time-series SAR image data, including format conversion to generate single-view complex data, orbit parameter update, and image registration, to obtain registered time-series SAR images; performing interferometric processing on the registered time-series SAR images, based on the small baseline principle, forming interferometric pairs of image pairs that meet the time baseline threshold and spatial baseline threshold, and performing interferometric and differential interferometric processing on each interferometric pair to obtain multiple wrapped interferometric phases; performing phase unwrapping processing on the wrapped interferometric phases to obtain unwrapped phases of each interferogram; and performing least squares calculation on each unwrapped phase of the interferogram to obtain the temporal unwrapped phase corresponding to each SAR image.

3. The InSAR atmospheric vertical stratification delay correction method according to claim 1, characterized in that, The time-domain low-pass filter is a dual-scale time-domain low-pass filter, which includes two stages: small-scale filtering and large-scale filtering. First, small-scale filtering is performed to suppress atmospheric turbulence delay and random noise in the time-series unwrapped phase, resulting in a first low-frequency phase and a first high-frequency phase. Then, large-scale filtering is performed to smooth the first high-frequency phase and extract a small amount of high-frequency deformation signal to obtain a second low-frequency phase. The first low-frequency phase and the second low-frequency phase are superimposed to obtain the time-series low-frequency phase.

4. The InSAR atmospheric vertical stratification delay correction method according to claim 3, characterized in that, Both the small-scale filtering and the large-scale filtering are implemented using a Gaussian weighted average method. The low-frequency phase of the time sequence is obtained according to equations (1) to (6): （1）， （2）， （3）， （4）， （5）， （6）， In the formula, , They are The imaging time of the SAR image acquisition; Indicates the imaging date of the filtered data; This refers to the size of a small-scale time window; This refers to the size of a large-scale time window; Represents the Gaussian function; This represents the first low-frequency phase after small-scale filtering; This represents the first high-frequency phase after small-scale filtering; This represents the second low-frequency phase after large-scale filtering; This is the low-frequency phase of the timing sequence.

5. The InSAR atmospheric vertical stratification delay correction method according to claim 1, characterized in that, The local window segmentation adopts a quadtree segmentation method, which includes: setting a minimum window side length and a maximum elevation difference threshold, recursively segmenting the digital elevation model until the elevation difference within each window is less than the maximum elevation difference threshold or the window side length reaches the minimum window side length.

6. The InSAR atmospheric vertical stratification delay correction method according to claim 1, characterized in that, The method of dividing elevation differences into levels based on the statistical distribution characteristics of elevation data includes: Calculate the average elevation data of all highly coherent points within a local window. and standard deviation ; Calculate the standard score of the elevation of each high coherence point according to formula (7). z : （7）， in, h The elevation value of the high coherence point; Elevation is divided into several elevation difference levels using integer multiples of standard scores as grading nodes.

7. The InSAR atmospheric vertical stratification delay correction method according to claim 1, characterized in that, The phase-elevation model is a linear model, expressed as follows: （8）， （9）， （10）， （11）， （12）， In the formula, Indicates the atmospheric vertical stratification delay phase. This represents the atmospheric vertical stratification delay phase vector corresponding to the i-th elevation difference level. Indicates the coefficient to be determined; Indicates the elevation value of the high coherence point; Represents the Kronecker tensor product; The matrix representing the correspondence between the SAR image and the differential interferogram within each local elevation difference level; This represents the design matrix constructed based on the elevation of highly coherent points within each elevation difference level; This represents the parameter matrix that needs to be solved.

8. An InSAR atmospheric vertical stratification delay correction device, characterized in that, include: The interferogram processing module is used to acquire time-series SAR image data of the monitoring area, perform differential interferometry, phase unwrapping, and least squares calculations to obtain the time-series unwrapped phase corresponding to each SAR image; The time-domain filtering module is used to perform time-domain low-pass filtering on the time-series unwrapped phase to separate the time-domain high-frequency components and obtain the time-series low-frequency phase. The window segmentation module is used to segment the monitoring area into several local windows based on the digital elevation model of the monitoring area. The elevation difference layering module is used to divide the elevation difference into several elevation difference layers within each local window based on the statistical distribution characteristics of the elevation data. The model building module is used to build a phase-elevation model within each elevation difference level, estimate the atmospheric vertical stratification delay phase of each elevation difference level, and remove the estimated atmospheric vertical stratification delay phase from the time-series low-frequency phase to obtain the atmospheric vertical stratification delay-corrected phase.

9. A computer device, characterized in that, include: The computer device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to implement the InSAR atmospheric vertical stratification delay correction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the InSAR atmospheric vertical stratification delay correction method as described in any one of claims 1 to 7.

Images