Atmosphere delay phase correction accuracy quantitative evaluation method, system, device and medium

Abstract

The present application belongs to the technical field of environmental monitoring, and discloses a kind of atmospheric delay phase correction precision quantitative evaluation method, system, equipment and medium, including extracting time series correction differential interference phase diagram in each PS point Time series differential phase value, the Spearman rank correlation coefficient of time series differential phase value and time axis of each PS point is calculated, according to the Spearman rank correlation coefficient and the annual average displacement of each PS point, obtain the settlement point in all PS points and eliminate the settlement point from all PS points to obtain several target PS points, obtain the time series RMSE value of each target PS point and obtain the quantitative evaluation result of atmospheric delay phase correction accuracy according to the time series RMSE value of each target PS point.The method realizes the quantitative evaluation of atmospheric delay phase correction, without the assumption that the ground subsidence or surface deformation in the evaluation area is zero, greatly guarantees the accuracy of the quantitative evaluation result, and provides a basis for realizing the optimal correction of atmospheric delay phase.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring technology and relates to a method, system, equipment and medium for quantitatively evaluating the accuracy of atmospheric delayed phase correction. Background Technology

[0002] Land subsidence, also known as ground sinking or ground collapse, is a localized downward movement (or engineering geological phenomenon) caused by the consolidation and compression of loose underground strata under the influence of natural activities or human engineering and economic activities, resulting in a decrease in the elevation of the Earth's crust. Currently, InSAR (Synthetic Aperture Radar Interferometry) technology is commonly used for land subsidence monitoring. However, when using InSAR technology for land subsidence monitoring, changes in atmospheric water vapor content during satellite revisits, as well as spatial variations in water vapor content within the same radar image, lead to different radar signal propagation delays. This results in a significant additional phase change in the InSAR interferometric phase, namely the atmospheric delay phase. Therefore, the atmospheric delay phase is a major source of error in time-series InSAR processing. Thus, accurately removing the atmospheric delay phase is one of the most critical issues in achieving high-precision subsidence retrieval during time-series InSAR-based land subsidence monitoring.

[0003] Currently, numerous methods for correcting atmospheric delay phase in time-series InSAR processing have been proposed, which can be divided into three main categories. The first category is based on the assumption that atmospheric delay phase is randomly distributed in the time dimension, including pairwise analysis, stacking methods, and the widely used spatiotemporal filtering methods. The second category is based on empirical methods related to the correlation between atmospheric laminar flow and topography, including linear model methods, exponential model methods, and quadtree-assisted joint model methods. The third category is atmospheric correction methods based on external data, including using GPS station data, water vapor products from space radiometers such as MERIS and MODIS, and atmospheric model products to estimate atmospheric delay phase.

[0004] Based on the aforementioned various atmospheric delay phase correction methods, accurately evaluating the correction effect of these methods is crucial for correctly selecting the appropriate atmospheric delay phase correction method when performing time-series InSAR processing. There are two methods for quantitatively evaluating the accuracy of atmospheric delay phase correction. The first is to calculate the root mean square error or standard deviation of a single differential interferogram at different spatial scales or even globally. The second is to calculate the root mean square error or standard deviation of all points along the time series within the global scope of the interferogram. However, both of these evaluation methods are based on the same assumption: that there are no subsidence points within the calculation area, and the differential phase value of all points is theoretically zero. However, with urban expansion and development, human activities such as land reclamation, subway tunnel construction, and groundwater extraction over the years have caused varying degrees and extents of land subsidence. Most areas do not meet the above assumption, leading to the inability to effectively guarantee the accuracy of existing evaluation methods. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, device and medium for quantitative evaluation of atmospheric delay phase correction accuracy.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] In a first aspect, this invention provides a method for quantitatively assessing the accuracy of atmospheric delayed phase correction, comprising: acquiring a time-series corrected differential interferometric phase map after atmospheric delayed phase correction, and extracting the time-series differential phase values ​​of each PS point in the time-series corrected differential interferometric phase map; calculating the Spearman rank correlation coefficient between the time-series differential phase values ​​of each PS point and the time axis based on the time-series differential phase values ​​of each PS point; acquiring the annual average displacement of each PS point, and obtaining settlement points among all PS points and removing settlement points from all PS points based on the Spearman rank correlation coefficient between the time-series differential phase values ​​of each PS point and the time axis, and obtaining a number of target PS points; obtaining the time-series RMSE value of each target PS point based on the time-series differential phase values ​​of each target PS point, and obtaining a quantitative assessment result of the accuracy of atmospheric delayed phase correction based on the time-series RMSE values ​​of each target PS point.

[0008] Optionally, calculating the Spearman rank correlation coefficient between the time series differential phase values ​​of each PS point and the time axis based on the time series differential phase values ​​of each PS point includes: arranging the differential phase values ​​in the time series differential phase values ​​of each PS point in ascending order, and replacing each differential phase value with its ranking position to obtain the rank phase sequence of each PS point; obtaining the time series of the time series corrected differential interferometric phase map, arranging each time in the time series in ascending order, and replacing each time with its ranking position to obtain the rank time series; and calculating the Spearman rank correlation coefficient between the time series differential phase values ​​of each PS point and the time axis using the following formula:

[0009]

[0010] Where, r si Let be the Spearman rank correlation coefficient between the time series differential phase value at the i-th PS point and the time axis, R(Xi) be the rank phase sequence at the i-th PS point, R(Y) be the rank time series, cov(R(Xi),R(Y)) be the covariance of R(Xi) and R(Y), and σ be the rank correlation coefficient between the time series differential phase value at the i-th PS point and the time axis. R(Xi) Let σ be the standard deviation of R(Xi). R(Y) Let R(Y) be the standard deviation.

[0011] Optionally, obtaining the annual average displacement of each PS point, and determining the settlement points among all PS points based on the Spearman rank correlation coefficient between the time series differential phase value of each PS point and the time axis, and the annual average displacement of each PS point, includes: taking the PS points whose annual average displacement is greater than a preset annual average displacement threshold and whose Spearman rank correlation coefficient between the time series differential phase value and the time axis is less than a preset correlation coefficient threshold as settlement points.

[0012] Optionally, the annual average displacement threshold ranges from 4 to 6 mm, and the correlation coefficient threshold ranges from -1 to -0.9.

[0013] Optionally, the annual average displacement threshold is 5 mm, and the correlation coefficient threshold is -0.9.

[0014] Optionally, obtaining the time series RMSE value of each target PS point based on the time series differential phase value of each target PS point includes: obtaining the differential phase value of each target PS point in each corrected differential interferometric phase diagram based on the time series differential phase value of each target PS point; and obtaining the time series RMSE value of each target PS point using the following formula:

[0015]

[0016]

[0017] Where Q is the time series RMSE value of the i-th target PS point, ph i,j Let be the transformed differential phase value of the i-th target PS point in the j-th corrected differential interferometric phase map, and N be the total number of corrected differential interferometric phase maps. Let λ be the differential phase value of the i-th target PS point in the j-th corrected differential interferometric phase diagram, and let λ be the radar wavelength of the corrected differential interferometric phase diagram.

[0018] Optionally, obtaining the quantitative assessment result of atmospheric delay phase correction accuracy based on the time series RMSE values ​​of each target PS point includes: calculating the mean or median of the time series RMSE values ​​of all target PS points as the quantitative assessment result of atmospheric delay phase correction accuracy.

[0019] In a second aspect, the present invention provides a quantitative assessment system for atmospheric delayed phase correction accuracy, comprising: an acquisition module for acquiring a time-series corrected differential interferometric phase map after atmospheric delayed phase correction, and extracting the time-series differential phase values ​​of each PS point in the time-series corrected differential interferometric phase map; a correlation analysis module for calculating the Spearman rank correlation coefficient between the time-series differential phase values ​​of each PS point and the time axis based on the time-series differential phase values ​​of each PS point; a settlement point removal module for acquiring the annual average displacement of each PS point, obtaining settlement points among all PS points based on the Spearman rank correlation coefficient between the time-series differential phase values ​​of each PS point and the time axis, and removing settlement points from all PS points to obtain a number of target PS points; and an assessment module for obtaining the time-series RMSE value of each target PS point based on the time-series differential phase values ​​of each target PS point, and obtaining a quantitative assessment result of the atmospheric delayed phase correction accuracy based on the time-series RMSE values ​​of each target PS point.

[0020] In a third aspect, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for quantitatively evaluating the accuracy of atmospheric delay phase correction.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for quantitatively evaluating the accuracy of atmospheric delay phase correction.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] This invention presents a quantitative assessment method for atmospheric delayed phase correction accuracy. It obtains the time-series differential phase values ​​of each PS point in a time-series corrected differential interferometric phase diagram, then calculates the Spearman rank correlation coefficient between the time-series differential phase value of each PS point and the time axis. Based on this Spearman rank correlation coefficient and the annual average displacement of each PS point, a subsidence point mask is used to obtain several target PS points. Finally, the time-series RMSE value of each target PS point is obtained, and a quantitative assessment result of the atmospheric delayed phase correction accuracy is derived based on this value. This method achieves a quantitative assessment of atmospheric delayed phase correction accuracy without requiring the assumption of zero ground subsidence or surface deformation within the assessment area. It can be widely applied to areas with slow surface deformation, avoiding the misinterpretation of surface deformation as residual error in atmospheric correction due to the zero deformation assumption, thus greatly ensuring the accuracy of the quantitative assessment results and providing a foundation for achieving optimal atmospheric delayed phase correction. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method for quantitatively evaluating the accuracy of atmospheric delay phase correction according to an embodiment of the present invention;

[0025] Figure 2 This is a flowchart of the atmospheric delay phase correction method according to an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram illustrating the quantitative evaluation results of atmospheric delay phase correction accuracy in an embodiment of the present invention.

[0027] Figure 4 This is a block diagram of the atmospheric delay phase correction accuracy quantitative evaluation system according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] As introduced in the background section, atmospheric phase delay is a major source of error in time-series InSAR processing, particularly prominent in regions with persistently cloudy and rainy weather. Studies show that the delay caused by atmospheric water vapor on one-way electromagnetic wave propagation varies by approximately 0–30 cm between the poles and the equator, and by several centimeters to 20 cm annually in mid-latitude regions. This effect is on the order of magnitude of slow deformations such as land subsidence. Therefore, in the process of land subsidence monitoring based on time-series InSAR, accurately removing the atmospheric phase delay is one of the most critical issues for achieving high-precision subsidence inversion.

[0031] Based on this, the present invention provides a quantitative evaluation method for atmospheric delayed phase correction accuracy. It uses the time series differential phase values ​​of PS points to perform time series rank correlation analysis, and combines annual deformation variables to perform settling point masking, thereby calculating the time series root mean square error statistical index of the phase value as a quantitative evaluation index, so as to realize the quantitative evaluation of atmospheric delayed phase correction performance and to achieve the optimal selection of different atmospheric delayed phase correction methods.

[0032] The present invention will now be described in further detail with reference to the accompanying drawings:

[0033] See Figure 1 In one embodiment of the present invention, a method for quantitatively evaluating the accuracy of atmospheric delay phase correction is provided, which specifically includes the following steps:

[0034] S1: Obtain the time-series corrected differential interferometric phase map after atmospheric delay phase correction, and extract the time-series differential phase value of each PS point in the time-series corrected differential interferometric phase map.

[0035] S2: Based on the time series differential phase values ​​of each PS point, calculate the Spearman rank correlation coefficient between the time series differential phase values ​​of each PS point and the time axis.

[0036] S3: Obtain the annual average displacement of each PS point. Based on the time series differential phase value of each PS point and the Spearman rank correlation coefficient of the time axis, as well as the annual average displacement of each PS point, obtain the settlement points among all PS points and remove the settlement points from all PS points to obtain several target PS points.

[0037] S4: Based on the time series differential phase values ​​of each target PS point, obtain the time series RMSE value of each target PS point, and based on the time series RMSE value of each target PS point, obtain the quantitative evaluation result of the atmospheric delay phase correction accuracy.

[0038] In this context, PS (Persistent Scatter) refers to various ground features that strongly backscatter radar waves and are relatively stable over time. PS points are the pixels representing these ground features in the radar image. The Spearman rank correlation coefficient is a non-parametric measure of the statistical correlation between the ranks of two variables, designed to assess a monotonic relationship between them, even if that relationship is non-linear. Furthermore, the Spearman rank correlation coefficient is more robust to outliers than the traditionally used Pearson correlation coefficient. The RMSE value is the root mean square error.

[0039] Specifically, the time-series corrected differential interferometric phase map after atmospheric delay phase correction is obtained by preprocessing the time-series synthetic aperture radar image using the PS InSAR algorithm, and then processing it using various preset atmospheric delay phase correction methods.

[0040] The preprocessing for the PS InSAR algorithm can employ conventional methods, including temporal and spatial baseline analysis of the entire time-series synthetic aperture radar (SAR) imagery. The image with the shortest spatial and temporal baselines to other images is selected as the master image. Then, using an orbital parameter-based reference digital elevation model (DEM), other images are precisely registered with the master image. Following the PS-InSAR algorithm's pairing criteria, all images in the sequence except the master image form interferometric pairs with it, resulting in differential interferometric phase maps. These are then removed using the reference DEM to remove terrain and flat phases, yielding a time-series differential interferometric phase map for atmospheric delay phase correction. This time-series differential interferometric phase map is then used for processing, employing various preset atmospheric delay phase correction methods to obtain time-series corrected differential interferometric phase maps corrected using different atmospheric delay phase correction methods.

[0041] Among them, see Figure 2When using atmospheric delay phase correction methods, atmospheric delay phase correction can be performed using spatiotemporal filtering methods alone, or by combining spatiotemporal filtering methods with atmospheric delay phase correction methods from atmospheric model products.

[0042] Specifically, the process for atmospheric delay phase correction using a spatiotemporal filtering method alone includes: taking the phase-unwrapped time-series differential interferometric phase map as input, performing preliminary atmospheric delay phase correction; then forming a Delaunay network based on spatial relationships and subtracting the phases of adjacent PS points; next, performing time-dimensional low-pass filtering and least-squares inversion on the main image to obtain atmospheric delay phase and orbital error estimates for the main image; and performing time-dimensional high-pass filtering and spatial low-pass filtering on the sub-image to obtain atmospheric delay phase and orbital error estimates for the sub-image, as well as the spatial correlation component of the reference digital elevation model (DEM) error. Finally, based on the atmospheric delay phase and orbital error estimates of the main image and the sub-image, and the spatial correlation component of the reference DEM error, a time-series corrected differential interferometric phase map with atmospheric delay phase correction is obtained.

[0043] When performing atmospheric delay phase correction using a combination of spatiotemporal filtering and atmospheric delay phase correction from atmospheric model products, the time-series differential interferometric phase map after phase unwrapping and the estimated atmospheric delay phase from atmospheric model products are used as inputs, in addition to using spatiotemporal filtering alone.

[0044] It should be noted that the specific processing details of the above-mentioned atmospheric delay phase correction method can be found in the current conventional atmospheric delay phase correction processing flow, and will not be elaborated here.

[0045] It should be noted that the present invention provides a quantitative evaluation method for atmospheric delay phase correction accuracy, which is used to quantitatively evaluate the correction accuracy of atmospheric delay phase correction methods. Therefore, in practical applications, when comparing the quantitative evaluation results of atmospheric delay phase correction accuracy of different atmospheric delay phase correction methods, the processing remains the same except for the differences in the atmospheric delay phase correction methods.

[0046] In summary, the present invention provides a quantitative assessment method for atmospheric delayed phase correction accuracy. This method obtains the time-series differential phase values ​​of each PS point in the time-series corrected differential interferometric phase diagram, calculates the Spearman rank correlation coefficient between the time-series differential phase values ​​of each PS point and the time axis, and uses this Spearman rank correlation coefficient combined with the annual average displacement of each PS point to perform a subsidence point masking to obtain several target PS points. Finally, it obtains the time-series RMSE value of each target PS point and uses this value to obtain a quantitative assessment result of the atmospheric delayed phase correction accuracy. This method achieves a quantitative assessment of atmospheric delayed phase correction accuracy without requiring the assumption of zero ground subsidence or surface deformation within the assessment area. It can be widely applied to areas with slow surface deformation, avoiding the misinterpretation of surface deformation as residual error in atmospheric correction due to the zero deformation assumption, thus greatly ensuring the accuracy of the quantitative assessment results and providing a foundation for achieving optimal atmospheric delayed phase correction.

[0047] In one possible implementation, calculating the Spearman rank correlation coefficient between the time series differential phase values ​​of each PS point and the time axis based on the time series differential phase values ​​of each PS point includes: arranging the differential phase values ​​in the time series differential phase values ​​of each PS point in ascending order, and replacing each differential phase value with its ranking to obtain the order sequence of each PS point; obtaining the time series of the time series corrected differential interferometric phase map, arranging each time in the time series in ascending order, and replacing each time with its ranking to obtain the rank time series; and calculating the Spearman rank correlation coefficient between the time series differential phase values ​​of each PS point and the time axis using the following formula:

[0048]

[0049] Where, r si Let be the Spearman rank correlation coefficient between the time series differential phase value of the i-th PS point and the time axis, R(Xi) be the order column of the i-th PS point, R(Y) be the rank time series, cov(R(Xi),R(Y)) be the covariance of R(Xi) and R(Y), and σ be the rank of the time series. R(Xi) Let σ be the standard deviation of R(Xi). R(Y) Let R(Y) be the standard deviation.

[0050] In one possible implementation, obtaining the annual average displacement of each PS point, and determining the settlement points among all PS points based on the Spearman rank correlation coefficient between the time series differential phase value of each PS point and the time axis, and the annual average displacement of each PS point, includes: taking all PS points whose annual average displacement is greater than a preset annual average displacement threshold and whose Spearman rank correlation coefficient between the time series differential phase value and the time axis is less than a preset correlation coefficient threshold as settlement points.

[0051] Specifically, based on the Spearman rank correlation coefficient between the time series differential phase value of each PS point and the time axis, as well as the annual average displacement of each PS point, it is determined whether each PS point has a settling trend, so as to accurately remove settling points and avoid the impact of settling points on the assessment of atmospheric delay phase correction.

[0052] Optionally, the annual average displacement threshold ranges from 4 to 6 mm, and the correlation coefficient threshold ranges from -1 to -0.9. Specifically, the selection of the annual average displacement threshold and the correlation coefficient threshold can be based on the actual application area and is an adaptive value. In this embodiment, based on historical data and experience, preferred values ​​are provided, namely, an annual average displacement threshold of 5 mm and a correlation coefficient threshold of -0.9.

[0053] In one possible implementation, obtaining the time series RMSE value of each target PS point based on the time series differential phase value of each target PS point includes: obtaining the differential phase value of each target PS point in each corrected differential interferometric phase diagram based on the time series differential phase value of each target PS point; and obtaining the time series RMSE value of each target PS point using the following formula:

[0054]

[0055]

[0056] Where Q is the time series RMSE value of the i-th target PS point, ph i,j Let be the transformed differential phase value of the i-th target PS point in the j-th corrected differential interferometric phase map, and N be the total number of corrected differential interferometric phase maps. Let λ be the differential phase value of the i-th target PS point in the j-th corrected differential interferometric phase diagram, and let λ be the radar wavelength of the corrected differential interferometric phase diagram.

[0057] Specifically, the time series RMSE value of each target PS point can be understood as the global RMSE value of each target PS point within the range of all differential interferometric phase maps, thereby reflecting the accuracy of atmospheric delay phase correction.

[0058] Optionally, obtaining the quantitative assessment result of atmospheric delay phase correction accuracy based on the time series RMSE values ​​of each target PS point includes: calculating the mean or median of the time series RMSE values ​​of all target PS points as the quantitative assessment result of atmospheric delay phase correction accuracy.

[0059] Specifically, by calculating the mean or median of the time series RMSE values ​​of all target PS points, a quantitative assessment result of the atmospheric delay phase correction accuracy is obtained. This avoids the significant impact of a single target PS point's time series RMSE value with large or incorrect errors on the quantitative assessment result, thus ensuring the stability of the quantitative assessment result.

[0060] In one possible implementation, a certain region is used as a case study area to verify the quantitative evaluation method for atmospheric delay phase correction accuracy of the present invention.

[0061] This implementation uses Sentinel-1TOPS mode single-view complex data, which includes 68 images from June 2015 to March 2018. The acquisition date interval is mostly 12 days. The data parameters are shown in Table 1.

[0062] Table 1 Data Parameter Table

[0063]

[0064] The data acquired on March 12, 2017, was used as the main image, and other data in the time series were used as secondary images. The time baseline ranged from 12 to 600 days, and the spatial baseline did not exceed 121.1 meters. The reference digital elevation model adopted AW3D DEM data with a resolution of 30 meters.

[0065] In this embodiment, atmospheric delay phase correction methods such as GACOS (Generic Atmospheric Correction Online Service), ERA-I (ERA-Interim, a global atmospheric reanalysis data source), ERA5 (a comprehensive global atmospheric reanalysis data source), Filter (spatiotemporal filtering), GACOS&Filter (GACOS atmospheric products combined with spatiotemporal filtering), ERA-I&Filter (ERA-I atmospheric reanalysis data combined with spatiotemporal filtering), and ERA5&Filter (ERA5 atmospheric reanalysis data combined with spatiotemporal filtering) are used to perform atmospheric delay phase correction on the time series differential interferometric phase map. Then, the atmospheric delay phase correction accuracy quantitative evaluation method of this invention is used to quantitatively evaluate the correction of various atmospheric delay phase correction methods. See [link to relevant documentation]. Figure 3The data shows the 10% to 90% percentiles of the time series RMSE values ​​of the target PS point. It can be seen that the original time series differential interferometric phase map has the most noise, while the residual noise after correction using spatiotemporal filtering combined with atmospheric model products is the least, within 10 mm. Among them, ERA5 & Filter has the highest correction accuracy.

[0066] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0067] See Figure 4 In another embodiment of the present invention, an atmospheric delay phase correction accuracy quantitative evaluation system is provided, which can be used to implement the above-mentioned atmospheric delay phase correction accuracy quantitative evaluation method. Specifically, the atmospheric delay phase correction accuracy quantitative evaluation system includes an acquisition module, a correlation analysis module, a settling point elimination module, and an evaluation module.

[0068] The system comprises the following modules: an acquisition module for acquiring a time-series corrected differential interferometric phase map after atmospheric delay phase correction, and extracting the time-series differential phase values ​​of each PS point in the map; a correlation analysis module for calculating the Spearman rank correlation coefficient between the time-series differential phase values ​​of each PS point and the time axis; a settlement point removal module for acquiring the annual average displacement of each PS point, obtaining the settlement points among all PS points based on the Spearman rank correlation coefficient between the time-series differential phase values ​​of each PS point and the time axis, and removing the settlement points to obtain several target PS points; and an evaluation module for obtaining the time-series RMSE value of each target PS point based on the time-series differential phase values, and obtaining a quantitative evaluation result of the atmospheric delay phase correction accuracy based on the time-series RMSE values ​​of each target PS point.

[0069] In one possible implementation, calculating the Spearman rank correlation coefficient between the time series differential phase values ​​of each PS point and the time axis based on the time series differential phase values ​​of each PS point includes: arranging the differential phase values ​​in the time series differential phase values ​​of each PS point in ascending order, and replacing each differential phase value with its ranking to obtain the order sequence of each PS point; obtaining the time series of the time series corrected differential interferometric phase map, arranging each time in the time series in ascending order, and replacing each time with its ranking to obtain the rank time series; and calculating the Spearman rank correlation coefficient between the time series differential phase values ​​of each PS point and the time axis using the following formula:

[0070]

[0071] Where, r si Let be the Spearman rank correlation coefficient between the time series differential phase value of the i-th PS point and the time axis, R(Xi) be the order column of the i-th PS point, R(Y) be the rank time series, cov(R(Xi),R(Y)) be the covariance of R(Xi) and R(Y), and σ be the rank of the time series. R(Xi) Let σ be the standard deviation of R(Xi). R(Y) Let R(Y) be the standard deviation.

[0072] In one possible implementation, obtaining the annual average displacement of each PS point, and determining the settlement points among all PS points based on the Spearman rank correlation coefficient between the time series differential phase value of each PS point and the time axis, and the annual average displacement of each PS point, includes: taking all PS points whose annual average displacement is greater than a preset annual average displacement threshold and whose Spearman rank correlation coefficient between the time series differential phase value and the time axis is less than a preset correlation coefficient threshold as settlement points.

[0073] In one possible implementation, the annual average displacement threshold ranges from 4 to 6 mm, and the correlation coefficient threshold ranges from -1 to -0.9.

[0074] Preferably, the annual average displacement threshold is 5 mm, and the correlation coefficient threshold is -0.9.

[0075] In one possible implementation, obtaining the time series RMSE value of each target PS point based on the time series differential phase value of each target PS point includes: obtaining the differential phase value of each target PS point in each corrected differential interferometric phase diagram based on the time series differential phase value of each target PS point; and obtaining the time series RMSE value of each target PS point using the following formula:

[0076]

[0077]

[0078] Where Q is the time series RMSE value of the i-th target PS point, ph i,j Let be the transformed differential phase value of the i-th target PS point in the j-th corrected differential interferometric phase map, and N be the total number of corrected differential interferometric phase maps. Let λ be the differential phase value of the i-th target PS point in the j-th corrected differential interferometric phase diagram, and let λ be the radar wavelength of the corrected differential interferometric phase diagram.

[0079] In one possible implementation, obtaining a quantitative assessment result of the atmospheric delay phase correction accuracy based on the time series RMSE values ​​of each target PS point includes: calculating the mean or median of the time series RMSE values ​​of all target PS points as the quantitative assessment result of the atmospheric delay phase correction accuracy.

[0080] All relevant content of each step involved in the aforementioned embodiments of the quantitative evaluation method for atmospheric delay phase correction accuracy can be referenced from the functional description of the corresponding functional module in the quantitative evaluation system for atmospheric delay phase correction accuracy in the embodiments of the present invention, and will not be repeated here. The module division in the embodiments of the present invention is illustrative and is only a logical functional division. In actual implementation, there may be other division methods. In addition, the functional modules in the various embodiments of the present invention can be integrated into a processor, exist separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module.

[0081] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a quantitative evaluation method for atmospheric delay phase correction accuracy.

[0082] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the atmospheric delay phase correction accuracy quantitative evaluation method in the above embodiments.

[0083] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.

[0084] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] 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.

[0086] 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.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention 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 of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for quantitatively evaluating the accuracy of atmospheric delayed phase correction, characterized in that, include: Obtain the time-series corrected differential interferometric phase map after atmospheric delay phase correction, and extract the time-series differential phase value of each PS point in the time-series corrected differential interferometric phase map; Based on the time series differential phase values ​​of each PS point, calculate the Spearman rank correlation coefficient between the time series differential phase values ​​of each PS point and the time axis; The annual average displacement of each PS point is obtained. Based on the Spearman rank correlation coefficient between the time series differential phase value of each PS point and the time axis, and the annual average displacement of each PS point, the settlement points in all PS points are obtained and the settlement points are removed from all PS points to obtain a number of target PS points. Based on the time series differential phase values ​​of each target PS point, the time series RMSE value of each target PS point is obtained, and based on the time series RMSE value of each target PS point, a quantitative assessment result of the atmospheric delay phase correction accuracy is obtained.

2. The method for quantitatively evaluating the accuracy of atmospheric delay phase correction according to claim 1, characterized in that, The step of calculating the Spearman rank correlation coefficient between the time series difference phase value of each PS point and the time axis based on the time series difference phase value of each PS point includes: Arrange the differential phase values ​​in the time series differential phase values ​​of each PS point in order of magnitude, and replace each differential phase value with the order of the differential phase values ​​to obtain the rank phase sequence of each PS point; The time series of the time series corrected differential interferometric phase map is obtained, and the time in the time series is arranged in order of magnitude. The time is replaced by the order of the time in the arrangement to obtain the rank time series. The Spearman rank correlation coefficient between the time series differential phase value and the time axis at each PS point is calculated using the following formula: Where, r si Let be the Spearman rank correlation coefficient between the time series differential phase value at the i-th PS point and the time axis, R(Xi) be the rank phase sequence at the i-th PS point, R(Y) be the rank time series, cov(R(Xi), R(Y)) be the covariance of R(Xi) and R(Y), and σ be the rank correlation coefficient between the time series differential phase value at the i-th PS point and the time axis. R(Xi) Let σ be the standard deviation of R(Xi). R(Y) Let R(Y) be the standard deviation.

3. The method for quantitatively evaluating the accuracy of atmospheric delay phase correction according to claim 1, characterized in that, The method for obtaining the annual average displacement of each PS point involves using the Spearman rank correlation coefficient between the time series differential phase value of each PS point and the time axis, along with the annual average displacement of each PS point, to determine the settlement points among all PS points, including: Settlement points are defined as PS points whose annual average displacement is greater than a preset annual average displacement threshold and whose Spearman rank correlation coefficient between the time series differential phase value and the time axis is less than a preset correlation coefficient threshold.

4. The method for quantitatively evaluating the accuracy of atmospheric delay phase correction according to claim 1, characterized in that, The threshold value for the annual average displacement is 4 to 6 mm, and the threshold value for the correlation coefficient is -1 to -0.

9.

5. The method for quantitatively evaluating the accuracy of atmospheric delay phase correction according to claim 4, characterized in that, The threshold for the average annual displacement is 5 mm, and the threshold for the correlation coefficient is -0.

9.

6. The method for quantitatively evaluating the accuracy of atmospheric delay phase correction according to claim 1, characterized in that, The process of obtaining the time series RMSE value of each target PS point based on the time series differential phase value of each target PS point includes: Based on the time series differential phase values ​​of each target PS point, the differential phase values ​​of each target PS point in each corrected differential interferometric phase diagram are obtained; The time series RMSE values ​​of each target PS point are obtained using the following formula: Where Q is the time series RMSE value of the i-th target PS point, ph i,j Let be the transformed differential phase value of the i-th target PS point in the j-th corrected differential interferometric phase map, and N be the total number of corrected differential interferometric phase maps. Let λ be the differential phase value of the i-th target PS point in the j-th corrected differential interferometric phase diagram, and let λ be the radar wavelength of the corrected differential interferometric phase diagram.

7. The method for quantitatively evaluating the accuracy of atmospheric delay phase correction according to claim 1, characterized in that, The quantitative assessment results of atmospheric delay phase correction accuracy obtained based on the time series RMSE values ​​of each target PS point include: The mean or median of the time series RMSE values ​​for all target PS points is calculated as a quantitative assessment of the accuracy of atmospheric delay phase correction.

8. A quantitative evaluation system for atmospheric delayed phase correction accuracy, characterized in that, include: The acquisition module is used to acquire the time-series corrected differential interferometric phase map after atmospheric delay phase correction, and extract the time-series differential phase value of each PS point in the time-series corrected differential interferometric phase map; The correlation analysis module is used to calculate the Spearman rank correlation coefficient between the time series differential phase value of each PS point and the time axis based on the time series differential phase value of each PS point. The settlement point removal module is used to obtain the annual average displacement of each PS point. Based on the time series differential phase value of each PS point and the Spearman rank correlation coefficient of the time axis, as well as the annual average displacement of each PS point, the settlement points in all PS points are obtained and the settlement points are removed from all PS points to obtain a number of target PS points. The evaluation module is used to obtain the time series RMSE value of each target PS point based on the time series differential phase value of each target PS point, and to obtain a quantitative evaluation result of the atmospheric delay phase correction accuracy based on the time series RMSE value of each target PS point.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for quantitatively evaluating the accuracy of atmospheric delay phase correction as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for quantitatively evaluating the accuracy of atmospheric delay phase correction as described in any one of claims 1 to 7.

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