An InSAR-based ground surface deformation monitoring method, device and system
By acquiring multi-source data in InSAR surface deformation monitoring, dividing sub-regions and quantifying the degree of terrain interference, and optimizing the unwrapping process, the problems of monitoring accuracy and reliability under complex terrain were solved, and high-precision surface deformation monitoring was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LUOYANG INST OF SCI & TECH
- Filing Date
- 2026-04-28
- Publication Date
- 2026-05-29
AI Technical Summary
In areas with complex terrain, InSAR surface deformation monitoring has insufficient accuracy and low reliability, mainly due to geometric distortions such as overlapping and shadows caused by complex terrain, which lead to interference with radar echo signals. Traditional phase unwrapping algorithms are unable to accurately recover the true phase.
By acquiring multi-source monitoring data, the target area is divided into sub-regions, the degree of terrain interference and comprehensive index are quantified, mapped to phase quality weights, and the phase unwrapping process is optimized to improve monitoring accuracy and reliability.
It significantly improves the accuracy and reliability of surface deformation monitoring in complex terrain environments, effectively suppresses the propagation and accumulation of phase errors, and enhances the accuracy and overall reliability of deformation inversion results.
Smart Images
Figure CN122110113A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of radar remote sensing technology, specifically to a method, device, and system for monitoring surface deformation based on InSAR. Background Technology
[0002] With the development of remote sensing technology, interferometric synthetic aperture radar (InSAR) technology has become an important means of monitoring surface deformation due to its all-weather, all-day coverage, wide coverage and millimeter-level high-precision monitoring capabilities. It is widely used in fields such as geological disaster early warning, urban subsidence assessment and infrastructure safety monitoring.
[0003] Currently, complex terrain causes geometric distortions such as overlay and shadows in synthetic aperture radar images, and the radar echo signal is interfered with by factors such as terrain orientation and surface roughness, resulting in a significant decrease in the quality of the interferometric phase. This makes it difficult for traditional phase unwrapping algorithms to accurately recover the true phase, and the accuracy and reliability of surface deformation monitoring in complex terrain areas are significantly reduced. Summary of the Invention
[0004] To address the technical problems of insufficient accuracy and low reliability in InSAR surface deformation monitoring in complex terrain areas, the present invention aims to provide an InSAR-based surface deformation monitoring method, equipment, and system. The specific technical solution adopted is as follows:
[0005] Firstly, an InSAR-based method for monitoring surface deformation is provided. This method includes: acquiring multi-source monitoring data covering a target area, including multi-temporal synthetic aperture radar (SAR) image data and digital elevation model (DEM) data spatially matched with the SAR image data; dividing the target area into multiple sub-regions; for each sub-region in the SAR image of each temporal phase, determining the instantaneous terrain interference level of the sub-region based on the multi-source monitoring data, whereby the instantaneous terrain interference level characterizes the instantaneous interference intensity caused by terrain factors on the interferometric phase quality of the sub-region under a single imaging temporal phase; for each sub-region, determining a comprehensive terrain interference index for each temporal phase based on the instantaneous terrain interference level of the sub-region under all temporal phases, whereby the comprehensive terrain interference index characterizes the comprehensive interference intensity of terrain factors on the SAR imaging quality; mapping the comprehensive terrain interference index of each sub-region to the phase quality weight of each sub-region, and performing phase unwrapping processing on the SAR image based on the phase quality weight of each sub-region to obtain the surface deformation monitoring results of the target area.
[0006] In one possible design, acquiring multi-source monitoring data covering the target area includes: acquiring multi-temporal synthetic aperture radar (SAR) images covering the target area and corresponding radar system parameters, including the incident angle and azimuth angle; acquiring digital elevation model (DEM) data that matches the spatial range of the SAR images; preprocessing the multi-temporal SAR images, including radiometric correction, geometric correction, and image registration; and spatially matching the preprocessed multi-temporal SAR images with the DEM data.
[0007] In one possible design, determining the instantaneous terrain interference level of a sub-region includes: identifying shadowed and overlayed areas based on the grayscale distribution of synthetic aperture radar (SAR) images, and defining all pixels in the sub-region other than those corresponding to the shadowed and overlayed areas as valid pixels; determining the surface roughness of the sub-region at a given time based on the grayscale values of the valid pixels; determining the terrain distortion direction index of the sub-region based on multi-source monitoring data, whereby the terrain distortion direction index characterizes the degree of distortion caused by terrain factors in the sub-region; and determining the instantaneous terrain interference level of the sub-region at a given time based on the surface roughness and the terrain distortion direction index.
[0008] In one possible design, determining the terrain distortion direction index of a sub-region includes: determining the degree of distortion influence based on the area of the shaded area, the area of the overlapping area, and the total area of the sub-region; determining the terrain aspect of the sub-region based on digital elevation model data; determining the degree of directional influence of the sub-region based on the terrain aspect and the radar azimuth angle corresponding to the synthetic aperture radar image; and determining the terrain distortion direction index of the sub-region based on the degree of distortion influence and the degree of directional influence.
[0009] In one possible design, the shadow region and the overlapping region are determined based on the grayscale distribution of the synthetic aperture radar image, including: determining the pixels with grayscale values lower than the preset shadow recognition threshold in the sub-region as shadow pixels, and clustering spatially adjacent shadow pixels to form shadow regions; determining the overall grayscale mean of the synthetic aperture radar image, determining the pixels with grayscale values higher than the overall grayscale mean in the sub-region as overlapping pixels, and clustering spatially adjacent overlapping pixels to form overlapping regions.
[0010] In one possible design, the surface roughness of a sub-region is determined based on the gray values of the effective pixels, including: determining the local gray mean of the sub-region based on the gray values of all effective pixels in the sub-region; and determining the surface roughness based on the difference between the gray value of each effective pixel and the local gray mean.
[0011] In one possible design, determining the comprehensive topographic disturbance index of a sub-region in each time phase includes: determining the minimum instantaneous topographic disturbance level from the instantaneous topographic disturbance levels of the sub-region in all time phases; determining the offset of the instantaneous topographic disturbance level of each time phase relative to the minimum instantaneous topographic disturbance level, and determining the topographic morphology stability based on the offset, whereby topographic morphology stability characterizes the temporal stability of the topographic morphology of the sub-region; and determining the comprehensive topographic disturbance index of the sub-region in each time phase based on the instantaneous topographic disturbance level and topographic morphology stability of each time phase.
[0012] In one possible design, phase unwrapping is performed on multi-temporal synthetic aperture radar images based on the phase quality weight of each sub-region to obtain the surface deformation monitoring results of the target area. This includes: determining the phase quality weight of each sub-region as the confidence constraint of each sub-region in the phase unwrapping algorithm; performing phase unwrapping processing using the phase unwrapping algorithm to obtain the unwrapped phase field; and determining the surface deformation monitoring results of the target area based on the unwrapped phase field and radar system parameters.
[0013] Secondly, an InSAR-based surface deformation monitoring device is provided, comprising: a data acquisition unit for acquiring multi-source monitoring data covering a target area, the multi-source monitoring data including multi-temporal synthetic aperture radar (SAR) image data and digital elevation model (DEM) data spatially matched with the multi-temporal SAR image data; a data processing unit for dividing the target area into multiple sub-regions, and for each sub-region in the SAR image of each temporal phase, determining the instantaneous terrain interference level of the sub-region based on the multi-source monitoring data, the instantaneous terrain interference level being used to characterize the instantaneous interference intensity caused by terrain factors on the interferometric phase quality of the sub-region under a single imaging temporal phase; and the data processing unit further for determining, for each sub-region, a comprehensive terrain interference index for each temporal phase based on the instantaneous terrain interference levels of the sub-regions under all temporal phases, the comprehensive terrain interference index being used to characterize the comprehensive interference intensity of terrain factors on the SAR imaging quality. The deformation monitoring unit is used to map the comprehensive topographic interference index of each sub-region to the phase quality weight of each sub-region, and perform phase unwrapping processing on the multi-temporal synthetic aperture radar image based on the phase quality weight of each sub-region to obtain the surface deformation monitoring results of the target area.
[0014] Thirdly, an InSAR-based surface deformation monitoring system is provided, including a server and an InSAR-based surface deformation monitoring device as provided in the second aspect. The server stores multi-source monitoring data covering a target area, including multi-temporal synthetic aperture radar (MAP) image data and digital elevation model (DEM) data spatially matched with the MAP image data. The InSAR-based surface deformation monitoring device is communicatively connected to the server, used to acquire the multi-source monitoring data from the server, and execute the InSAR-based surface deformation monitoring method provided in any possible design of the first aspect to generate and output the surface deformation monitoring results for the target area.
[0015] The present invention has the following beneficial effects:
[0016] In the InSAR-based surface deformation monitoring method provided by this invention, firstly, by acquiring and fusing multi-temporal synthetic aperture radar images and digital elevation model data, a complete data foundation for refined analysis is provided. Then, the target area is divided into sub-regions, achieving spatial refinement from macro to micro levels. Based on this, a "terrain distortion direction index" quantifies the degree of distortion from the perspective of spatial geometry and directional relationships, and an "instantaneous terrain interference degree" quantifies the instantaneous impact from the perspective of single-temporal scattering characteristics, thus achieving a multi-dimensional and three-dimensional instantaneous mapping of terrain interference. Furthermore, by fusing instantaneous interference information from all temporal phases, a "comprehensive terrain interference index" is determined. This step expands the analysis perspective from a static single-temporal phase to a dynamic temporal series, ensuring that the final index not only includes the intensity information of the interference but also inherently contains its temporal stability characteristics. Finally, by inversely mapping this comprehensive index to the mass weight in the phase unwrapping process and optimizing the unwrapping accordingly, a complete technical chain is formed from "raw data" to "interference quantification" and then to "process optimization." This link enables the phase unwrapping process to adaptively distinguish and differentiate between high-confidence regions and low-confidence regions severely affected by terrain, effectively suppressing the propagation and accumulation of phase errors in space, thereby significantly improving the accuracy and overall reliability of deformation inversion results in complex terrain environments such as mountains and hills. Attached Figure Description
[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a schematic diagram of the structure of a surface deformation monitoring device based on InSAR provided in one embodiment of the present invention;
[0019] Figure 2 This is a flowchart illustrating an InSAR-based method for monitoring surface deformation, provided as an embodiment of the present invention.
[0020] Figure 3 This is a schematic diagram of the structure of an InSAR-based surface deformation monitoring system provided in one embodiment of the present invention. Detailed Implementation
[0021] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an InSAR-based surface deformation monitoring method, device, and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0022] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0023] In the description of this invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" and "more than one" refer to two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.
[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0025] The following description, in conjunction with the accompanying drawings, details a specific scheme for an InSAR-based method, equipment, and system for monitoring land deformation provided by this invention.
[0026] Please see Figure 1 The diagram illustrates a structural schematic of an InSAR-based surface deformation monitoring device according to an embodiment of the present invention. Figure 1The surface deformation monitoring device 10 based on InSAR shown includes a data acquisition unit 11, a data processing unit 12, and a deformation monitoring unit 13.
[0027] The data acquisition unit 11 is used to acquire multi-source monitoring data covering the target area, wherein the multi-source monitoring data includes multi-temporal synthetic aperture radar image data and digital elevation model data spatially matched with the multi-temporal synthetic aperture radar image data.
[0028] In some embodiments, the data acquisition unit 11 is used to retrieve multi-temporal synthetic aperture radar raw images and their corresponding radar system parameters (including incident angle and azimuth angle) from a local database or a remote satellite data server, and simultaneously acquire digital elevation model data consistent with the image coverage area; subsequently, the data acquisition unit 11 performs radiometric correction on the synthetic aperture radar images to eliminate sensor response differences and atmospheric attenuation effects, performs geometric coarse correction to complete geocoding, and uses an image registration algorithm to achieve sub-pixel level alignment of multi-temporal images, and finally performs spatial coordinate system matching and resolution matching between the preprocessed synthetic aperture radar images and the digital elevation model data to generate a spatially strictly aligned multi-source monitoring dataset.
[0029] The data processing unit 12 is used to divide the target area into multiple sub-regions. For each sub-region in the synthetic aperture radar image of each time phase, it determines the terrain distortion direction index and the instantaneous terrain interference level of the sub-region based on multi-source monitoring data. It is also used to determine the comprehensive terrain interference index of each sub-region at each time phase based on the instantaneous terrain interference levels of the sub-regions across all time phases. Specifically, the terrain distortion direction index characterizes the degree of distortion caused by terrain factors in the sub-region, the terrain morphology stability characterizes the temporal stability of the terrain morphology of the sub-region, and the comprehensive terrain interference index characterizes the overall interference intensity of terrain factors on the synthetic aperture radar imaging quality.
[0030] In some embodiments, firstly, the data processing unit 12 divides the target area into multiple sub-regions; for each sub-region in the synthetic aperture radar image of each time phase, the data processing unit 12 identifies shadow areas and overlapping areas based on the image grayscale distribution (for example, clustering pixels with grayscale values below a preset shadow identification threshold as shadow areas, and clustering pixels with grayscale values above the overall grayscale average of the entire image as overlapping areas), and calculates the degree of distortion influence by combining the total area of the sub-region; at the same time, the data processing unit 12 extracts the terrain slope aspect of the sub-region based on digital elevation model data, and calculates the angular relationship between the slope aspect and the radar azimuth angle to obtain the degree of directional influence by combining the degree of distortion influence and the degree of directional influence, and finally fuses the degree of distortion influence and the degree of directional influence to determine the terrain distortion direction index of each sub-region. In addition, the data processing unit 12 removes invalid pixels corresponding to shadows and overlapping areas, calculates surface roughness using the gray-level discreteness of the remaining valid pixels, and determines the instantaneous terrain interference level under a single time phase by combining the terrain distortion direction index. Based on this, by analyzing the instantaneous terrain interference level under all time phases, the comprehensive terrain interference index of each sub-region under each time phase is determined. The comprehensive terrain interference index of each sub-region under each time phase is output to the deformation monitoring unit 13 as a key intermediate result to guide the subsequent phase unwrapping process.
[0031] The deformation monitoring unit 13 is used to map the comprehensive index of terrain interference in each sub-region to the phase quality weight of each sub-region, and to perform phase unwrapping processing on the multi-temporal synthetic aperture radar image based on the phase quality weight of each sub-region to obtain the surface deformation monitoring results of the target area.
[0032] In some embodiments, the deformation monitoring unit 13 receives the comprehensive terrain interference index of each sub-region at each time phase output by the data processing unit 12, and maps it to a phase quality weight, i.e., the stronger the terrain interference in a sub-region, the lower its phase quality weight. The deformation monitoring unit 13 introduces this phase quality weight as a confidence constraint into the phase unwrapping algorithm. When constructing the unwrapped network, the reliability parameters of nodes or connecting edges are dynamically adjusted according to the phase quality weight, so that the unwrapping path extends preferentially along the high-weight (i.e., low terrain interference) region, thereby effectively suppressing the propagation of phase error caused by complex terrain and generating a high-quality unwrapped phase field. Subsequently, the deformation monitoring unit 13 combines the unwrapped phase field and radar system parameters (such as wavelength, incident angle, etc.) to invert and calculate the surface deformation relative to the reference time phase at each monitoring time, and finally outputs the surface deformation monitoring results of the target area.
[0033] Please see Figure 2 The diagram illustrates a flowchart of an InSAR-based surface deformation monitoring method according to an embodiment of the present invention, including the following steps S201-S204.
[0034] S201. Obtain multi-source monitoring data covering the target area.
[0035] The multi-source monitoring data includes multi-temporal synthetic aperture radar (SAR) image data and digital elevation model (DEM) data spatially matched with the SAR image data.
[0036] As one possible approach, multi-temporal synthetic aperture radar (SAR) images covering the target area are first obtained from an international satellite data sharing platform, along with the radar system parameters corresponding to each SAR image.
[0037] It should be noted that the acquired multi-temporal synthetic aperture radar (SAR) images refer to a sequence of multiple images of the same geographic area collected by SAR satellites at different observation times. The radar system parameters include at least the incident angle and the azimuth angle. The incident angle is the angle between the radar beam centerline and the local surface normal, used to characterize the radar observation geometry. The azimuth angle is the projection angle of the radar satellite's flight direction onto the horizontal plane, used to determine the horizontal direction of the radar's line of sight.
[0038] Furthermore, digital elevation model (DEM) data matching the spatial range of the synthetic aperture radar (SAR) image is acquired. The DEM data consists of raster data representing the surface elevation distribution of the target area, and its spatial resolution and geographic coordinate system must be consistent with the SAR image to support subsequent terrain feature extraction and geometric correction.
[0039] Optionally, the data source for digital elevation model data can be publicly available data from the Shuttle Radar Topography Mission (SRTM) or data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTERGDEM).
[0040] Furthermore, the multi-temporal synthetic aperture radar images are preprocessed.
[0041] In some embodiments, preprocessing includes radiometric correction, geometric correction, and image registration. Radiometric correction is used to eliminate radiation distortion caused by differences in sensor gain, atmospheric attenuation, and topographic undulations, making the backscattering intensity comparable between different images. Geometric correction, based on satellite orbital parameters and the Earth ellipsoid model, transforms the original image from the slant-range-Doppler coordinate system to the geographic coordinate system, achieving preliminary geocoding. Image registration involves selecting one scene from all temporal synthetic aperture radar images as the master image, and aligning the remaining secondary images with the master image with high precision using a preset feature matching algorithm (such as a matching algorithm based on scale-invariant feature transformation or phase gradient autocorrelation method), ensuring the consistency of the position of the same ground point in images of different temporal phases and avoiding the introduction of false phase signals due to registration errors.
[0042] Finally, the preprocessed multi-temporal synthetic aperture radar imagery is spatially matched with digital elevation model data.
[0043] In some embodiments, the digital elevation model data is resampled to the same spatial resolution as the synthetic aperture radar imagery, and a rigorous coordinate system transformation is performed to ensure complete consistency between the two in terms of projection, pixel size, and geographical extent. Simultaneously, a small amount of ground control point data is introduced as an auxiliary method to jointly construct a unified and accurate spatial reference system, thereby ensuring accurate overlay and joint analysis of terrain information and radar imagery information in subsequent steps.
[0044] S202. Divide the target area into multiple sub-regions. For each sub-region in the synthetic aperture radar image of each time phase, determine the instantaneous terrain interference level of the sub-region based on multi-source monitoring data.
[0045] Among them, the instantaneous terrain interference level is used to characterize the instantaneous interference intensity caused by terrain factors on the interferometric phase quality of a sub-region under a single imaging phase.
[0046] As one possible approach, the target region in each phase of the preprocessed synthetic aperture radar image is first divided into multiple uniformly sized sub-regions according to a pre-defined fixed-size grid. Each sub-region contains a fixed number of pixels, the size of which is pre-set according to the spatial resolution of the synthetic aperture radar image to ensure that each sub-region contains a sufficient number of pixels to support statistical analysis. A unique spatial index is assigned to each sub-region to maintain the spatial correspondence in subsequent processing.
[0047] Optionally, the target region in the synthetic aperture radar image can be divided into several regions with a side length of A square grid of pixels, in which The value is a preset positive integer (e.g., 64) to ensure the total area of each sub-region. For a fixed value (e.g.) (each pixel).
[0048] Furthermore, for each sub-region in the synthetic aperture radar image of each time phase, the shadow region and the overlapping region are determined based on the grayscale distribution of the synthetic aperture radar image.
[0049] In some embodiments, such as for the currently processed first The first phase of synthetic aperture radar imagery In each sub-region, two main types of terrain geometric distortions—shadowed areas and overlapping areas—are identified based on the grayscale distribution characteristics of synthetic aperture radar images.
[0050] Because radar waves cannot reach the back slope, they will create shadow areas with extremely low grayscale in the image. For these shadow areas, a low grayscale threshold is set, denoted as the preset shadow recognition threshold. (For example, a fixed value near the lower limit of the image's dynamic range can be used, such as 5). All gray values within the sub-region that are lower than... Pixels were identified as shadow pixels. Subsequently, connected component analysis was performed on these shadow pixels to cluster spatially adjacent shadow pixels into continuous shadow regions. The total number of pixels in all shadow regions within each region was counted and recorded as the area of the shadow region. .
[0051] On a steep slope facing the radar, the echo from the top of the slope arrives before the bottom, causing signal superposition, which appears as an abnormally bright overlapping area in the image. For the overlapping area, calculate the current... The overall grayscale mean of the synthetic aperture radar image at each time phase is denoted as . This serves as a reference for identifying overlays. All grayscale values within the sub-region that are higher than [a certain value] are [selected]. The pixels were identified as overlapping pixels. Connectivity analysis was then performed to cluster adjacent overlapping pixels into continuous overlapping regions. The total number of pixels in all overlapping regions within each region was counted and recorded as the area of the overlapping region. .
[0052] It should be noted that since shadowed and overlayed areas are two types of terrain distortions that can severely damage or distort the geometric relationships and phase information of SAR imaging, the larger the area on the image, the more severe the influence of terrain distortion on the image, and the lower the reliability of deformation monitoring. Therefore, the degree of influence of terrain distortion on InSAR phase can be reflected by the relative area ratio of shadowed and overlayed areas. The larger the area, the more severe the influence of terrain distortion on the area, and the lower the reliability of InSAR deformation monitoring.
[0053] Furthermore, for each sub-region in the synthetic aperture radar image of each time phase, based on the identified shadow and overlay regions within the sub-region, the pixels corresponding to the shadow regions and the pixels corresponding to the overlay regions are removed, and all remaining pixels within the sub-region are determined as valid pixels.
[0054] Furthermore, extract the first The first phase of synthetic aperture radar imagery The gray values of all valid pixels within each sub-region form a gray value set. ,in, For the first The first phase of synthetic aperture radar imagery Within each sub-region The gray value of each effective pixel. For the first The first phase of synthetic aperture radar imagery The total number of valid pixels within each sub-region. Then, the geometric mean of this grayscale value is calculated, denoted as the local grayscale mean corresponding to the sub-region. .
[0055] It should be noted that differences in surface roughness directly affect the coherence of radar echoes. Rough surfaces (such as areas with exposed bedrock) have stable scattering mechanisms, which helps maintain high coherence; while smooth surfaces (such as flat rammed earth) are more susceptible to subtle environmental changes, leading to a rapid decay in coherence. To quantify this characteristic, surface roughness characterization values are calculated. Its formula is expressed as: , For the first The first phase of synthetic aperture radar imagery The surface roughness of the sub-region is calculated by... The first phase of synthetic aperture radar imagery The gray values of all valid pixels within a sub-region relative to its local gray mean The sum of absolute deviations is obtained by normalization (e.g., by using the maximum-minimum normalization method) to map the value to the [0,1] interval. The larger the value, the more significant the difference in pixel grayscale within the sub-region, i.e., the rougher the surface, the higher the expected temporal coherence, and the more favorable it is for deformation inversion.
[0056] The area of the shaded region is obtained. and the area of the overlapping region Then, based on the area of the shaded region Area of overlapping regions and sub-regions The total area determines the degree of distortion, and its formula is expressed as follows: In the formula, That is, the first The first phase of synthetic aperture radar imagery The degree of distortion in each sub-region The larger the value, the more severe the influence of terrain geometric distortion on the sub-region, and the lower the InSAR phase reliability.
[0057] Furthermore, deformation in sloping terrain (such as landslides and glaciers) often exhibits strong directionality, typically occurring downwards along the slope. Under gravity, the projection direction of the slope normal onto the horizontal plane, i.e., the slope aspect, determines the dominant direction of deformation. When the slope aspect is parallel to the radar azimuth, the slope extends along the radar's flight direction, and its slope component in the radar range direction (line of sight) is minimal, resulting in weak terrain distortion (especially overlay). Conversely, if the slope aspect is perpendicular to the radar azimuth, the range slope component increases, the radar's sensitivity to deformation increases, the range slope is maximized, range compression is severe, and terrain distortion is most significant. Therefore, to more accurately assess the impact of terrain factors on the geometric distortion of a sub-region in the current time phase, the degree of directional influence of the sub-region should also be analyzed, and the degree of distortion influence of the sub-region should be compensated to obtain the terrain distortion direction index of the sub-region in the current time phase.
[0058] First, based on high-precision digital elevation model data, the terrain analysis tools in Geographic Information System (GIS) are used to calculate the slope aspect of each pixel (slope aspect north of 0 degrees, measured clockwise to 360 degrees), and the results are statistically analyzed. The average topographic slope aspect of all pixels within a sub-region is denoted as the topographic slope aspect of the sub-region. At the same time, obtain the current number. The radar azimuth angle corresponding to the synthetic aperture radar image of each time phase is denoted as . That is, the angle between the radar satellite's flight direction and due north.
[0059] Furthermore, according to the first The topographic slope of each sub-region Radar azimuth The angular difference between the terrain slope aspect and the radar azimuth angle is determined. To ensure that the angular difference is between 0 and 180 degrees, the formula for calculating the angular difference is: In the formula, That is, the first The first phase of synthetic aperture radar imagery The angular differences between sub-regions are then converted into the degree of directional influence, used to quantify the impact of the aspect-azimuth geometry on distortion. For example, a sine function can be used for mapping, expressed by the following formula: ,in, For the first The first phase of synthetic aperture radar imagery The degree of directional influence of each sub-region The larger the value, the closer the slope aspect is to the radar azimuth angle being perpendicular, the stronger the influence of directional distortion caused by terrain, and the stronger the impact on the InSAR phase.
[0060] Finally, considering the overall degree of distortion impact and the degree of influence of direction Determine the topographic distortion direction index of the sub-region.
[0061] In some embodiments, the formula for calculating the terrain distortion direction index of a sub-region is as follows:
[0062]
[0063] In the formula, For the first The first phase of synthetic aperture radar imagery The topographic distortion direction index of each sub-region For the first The first phase of synthetic aperture radar imagery The degree of distortion in each sub-region For the first The first phase of synthetic aperture radar imagery The degree of directional influence of each sub-region. These are preset weighting coefficients, which can be flexibly configured according to the needs of the monitoring scenario, for example... . The larger the value, the more severe the influence of directional geometric distortion caused by topographic factors on the sub-region in the current time phase.
[0064] Finally, based on the surface roughness and topographic distortion direction index, the first... The first phase of synthetic aperture radar imagery The degree of real-time terrain interference in each sub-region.
[0065] In some embodiments, the first The first phase of synthetic aperture radar imagery The formula for calculating the instantaneous terrain disturbance level of each sub-region is as follows:
[0066]
[0067] In the formula, For the first The first phase of synthetic aperture radar imagery The degree of real-time terrain interference in each sub-region For the first The first phase of synthetic aperture radar imagery The topographic distortion direction index of each sub-region For the first The first phase of synthetic aperture radar imagery Surface roughness of each sub-region This reflects the Earth's surface's ability to "suppress" disturbances (the larger the value, the stronger the suppression ability). Therefore, using... As a correction factor Modulation is performed when the surface roughness is high ( When (large), the correction factor A smaller surface roughness reduces the contribution of the geometric distortion index because a rough surface helps stabilize the echo; conversely, a smooth surface (…) reduces the contribution of the geometric distortion index. When the time is small, the correction factor is... The large size of the geometric distortion fully reflects its effects. The larger the value, the more significant the effect. The first phase of synthetic aperture radar imagery The higher the overall instantaneous intensity of terrain disturbance in a sub-region, the better.
[0068] Understandably, this embodiment of the invention determines surface roughness by analyzing the grayscale distribution of effective pixels, essentially quantifying the modulation effect of surface micro-undulations on the radar wave scattering mechanism. Rough surfaces typically correspond to more stable and stronger backscattering and higher temporal coherence, possessing an inherent ability to suppress phase errors caused by geometric distortion; smooth surfaces, on the other hand, exhibit the opposite. By integrating this quantified surface roughness with the terrain distortion direction index, which reflects the intensity of macroscopic geometric distortion, the determined instantaneous terrain interference level is no longer a rough estimate based solely on image appearance, but a comprehensive measure integrating both "geometric distortion intensity" and "surface scattering stability" physical information. This measure can more realistically reflect the actual net attenuation effect of terrain factors on the interferometric phase signal at a specific imaging moment. Its direct effect is that subsequent time-series stability analysis and final weight mapping based on this parameter sequence are based on more accurate and reliable intermediate data, fundamentally avoiding weight misallocation caused by underestimating surface scattering stability or overestimating interference from pure regions. This ensures that the unwrapping process can more accurately focus on high-confidence phase information and effectively suppress systematic phase errors caused by complex terrain.
[0069] S203. For each sub-region, determine the comprehensive terrain interference index for each sub-region in each time phase based on the real-time terrain interference level of the sub-region in all time phases.
[0070] Among them, the terrain interference comprehensive index is used to characterize the comprehensive interference intensity of terrain factors on the imaging quality of synthetic aperture radar.
[0071] As one possible implementation, firstly from the... Each sub-region in all The minimum instantaneous terrain disturbance level is determined from the instantaneous terrain disturbance levels at each time phase, and denoted as . Then, the offset of the instantaneous terrain disturbance level relative to the minimum instantaneous terrain disturbance level at each time phase is determined, and the terrain morphology stability is determined based on the offset. The formula is expressed as follows: In the formula, To minimize immediate terrain disturbance, For the first The first phase of synthetic aperture radar imagery The degree of real-time terrain interference in each sub-region That is, the first The first phase of synthetic aperture radar imagery The offset of the instantaneous terrain disturbance level of each sub-region relative to the minimum instantaneous terrain disturbance level; For the number of all time phases, To sum the offsets of all time phases, we obtain the cumulative offset, which directly measures the intensity of terrain disturbance fluctuations over time. The greater the fluctuation, the further each time phase value deviates from the minimum value, and the larger the cumulative offset. For the first The stability of the terrain morphology of each sub-region is affected by the smaller the cumulative offset (i.e., the smaller the time-series fluctuation), and the closer the denominator is to 1. A larger value indicates a more stable terrain in the area; conversely, a smaller value indicates a larger cumulative offset. The smaller the value, the worse the stability.
[0072] Furthermore, based on the instantaneous degree of terrain disturbance and the stability of terrain morphology in each time phase, a comprehensive terrain disturbance index for each sub-region in each time phase is determined.
[0073] In some embodiments, the formula for calculating the comprehensive terrain disturbance index of each sub-region in each time phase is as follows:
[0074]
[0075] In the formula, For the first The first phase of time The comprehensive index of terrain disturbance in each sub-region For the first The first phase of synthetic aperture radar imagery The degree of real-time terrain interference in each sub-region For the first Topographic stability of individual regions, local topographic stability When the value is relatively high (i.e., the time series fluctuation is small), the denominator A larger value will suppress the final comprehensive index of terrain disturbance. Even if a momentary disturbance is strong at a certain time phase, if the topographic impact pattern in the area is temporally stable and predictable, then its "comprehensive" negative impact on the reliability of deformation monitoring should be relatively underestimated, because stable disturbances are more easily partially modeled or tolerated in time-series processing. Conversely, if the topographic morphology is stable... If the value is very low (with drastic fluctuations), the denominator approaches 1, indicating the instantaneous disturbance intensity. It will be almost entirely reflected as This indicates that the overall interference level is assessed as very high due to the unpredictable interference patterns. The larger the value, the higher the value. The first phase, the The higher the overall level of interference from topographic factors in a sub-region, the worse the expected quality of its interference phase, and the lower the reliability of the deformation monitoring results.
[0076] Finally, after completing the first The first phase of time Comprehensive index of terrain disturbance in each sub-region After the initial calculation, following the same calculation method described above, the comprehensive terrain disturbance index is calculated for all sub-regions under that time phase, and then the entire... In each sub-region of the synthetic aperture radar (SAR) image at each time phase, the terrain interference index is calculated one by one. Finally, the set of terrain interference indices for all sub-regions within the target area at each time phase is obtained. The larger the terrain interference index value of each sub-region, the stronger the comprehensive interference from terrain factors in the corresponding time phase, the greater the influence of terrain on the SAR imaging quality, and the lower the reliability of the corresponding surface deformation monitoring results. Conversely, the smaller the terrain interference index value, the weaker the comprehensive interference from terrain factors in the corresponding time phase, the less the influence of terrain on the SAR imaging quality, and the higher the reliability of the corresponding surface deformation monitoring results.
[0077] Understandably, this embodiment of the invention extracts the minimum instantaneous terrain interference level from the multi-temporal instantaneous terrain interference levels as the benchmark for stability analysis, and calculates the offset of each time relative to this benchmark to quantify its volatility. This step essentially reveals and measures the predictability of terrain influence in the time dimension. Subsequently, the quantified terrain morphology stability is used as a modulation factor applied to the instantaneous interference intensity of each time phase, so that the finally determined comprehensive index not only reflects the instantaneous amplitude of the interference, but also inherently contains the robustness information of the interference mode in the time series. This enables the system to intelligently distinguish two types of key areas: one type is areas where the instantaneous interference may be strong but the mode is stable and the phase noise is relatively constant, and the comprehensive interference evaluation of these areas will be appropriately lowered, thus preserving their usable phase information value; the other type is areas where the interference intensity fluctuates drastically and irregularly over time, and even if the instantaneous interference of some time phases seems low, they will be given a high comprehensive interference evaluation due to their poor stability, and thus be treated cautiously during untangling. This mechanism significantly enhances the entire monitoring system's ability to adapt to and suppress time-varying disturbances (such as seasonal scattering characteristics changes and intermittent micro-topographical changes) under complex terrain, fundamentally reducing the risk of unwrapping error propagation caused by temporal phase decoherence, and ensuring that the final deformation inversion results have higher consistency and credibility in both time and space dimensions.
[0078] S204. Map the terrain interference comprehensive index of each sub-region to the phase quality weight of each sub-region, and perform phase unwrapping processing on the multi-temporal synthetic aperture radar image based on the phase quality weight of each sub-region to obtain the surface deformation monitoring results of the target area.
[0079] As one possible implementation, the terrain disturbance comprehensive index of each sub-region is first mapped to the phase quality weight of each sub-region.
[0080] In some embodiments, the design principle of this mapping relationship is that areas with larger terrain disturbance comprehensive index values indicate that their phase quality is more severely affected by terrain and have lower reliability, and therefore should be assigned lower weights; conversely, areas with less disturbance should receive higher weights. The formula for this mapping relationship is expressed as follows: In the formula, For the first The first phase of time The comprehensive index of terrain disturbance in each sub-region For the first The first phase of time Phase quality weights for each sub-region The larger the value, the better. The smaller the value, The relative reliability of the phase of the corresponding sub-region is directly quantified.
[0081] Furthermore, the phase quality weights for each sub-region of each phase are obtained. Then, it is introduced into the phase unwrapping process to optimize the unwrapping path and suppress error propagation in low-quality phase regions.
[0082] In some embodiments, the phase quality weight of each sub-region (or each cell within a sub-region) is... This serves as a confidence constraint for the region in phase unwrapping algorithms. For example, when using phase unwrapping algorithms based on graph theory or least squares principles, it will... The weights (or cost coefficients) of the corresponding nodes or edges in the unwrapped network graph are set. During iteration or optimization, the phase unwrapping algorithm tends to prioritize establishing phase unwrapping paths through high-weight (high-confidence) regions and automatically avoids or reduces dependence on low-weight (low-confidence) regions, thereby effectively constraining the spatial spread of errors. Based on this weighted unwrapping process, a more accurate unwrapped phase field is finally obtained after optimization.
[0083] It should be noted that in temporal InSAR processing, a reference time phase needs to be selected (usually the earliest or most stable image). All other time phases (i.e., the...) Each phase, ,and The unwrapped phase (not equal to the reference phase number) represents the phase difference between the surface state at that time phase and the reference phase state in the radar line-of-sight direction.
[0084] Furthermore, based on the unwrapped phase field and combined with radar system parameters, deformation inversion calculations are performed to determine the surface deformation monitoring results of the target area.
[0085] In some embodiments, for the first The first phase of time The cumulative surface deformation of each sub-region along the radar line of sight. It can be determined according to the following calculation formula.
[0086]
[0087] In the formula, For from the first Extracted from the unwrapping phase field of the first time phase, corresponding to the first... The untangling phase value (in radians) of each sub-region. The value is essentially the first The interference phase difference between the current phase and the reference phase is eliminated after unwrapping. Obtained after blurring; This refers to the wavelength of the electromagnetic waves emitted by the radar, which is a parameter in the radar system. For the first The radar incident angle corresponding to each sub-region For the first The first phase of time Each sub-region from the reference phase to the [number]th During each time phase, the cumulative surface deformation along the radar line of sight is represented by positive values, which usually indicate displacement away from the satellite, and negative values, which indicate displacement towards the satellite.
[0088] Finally, by repeating the above calculations for all sub-regions and all time phases within the target area, a complete monitoring result of surface deformation in the target area can be generated.
[0089] In some embodiments, after obtaining the surface deformation monitoring results, the surface deformation amount in the surface deformation monitoring results is compared with a preset warning threshold. If it exceeds the preset warning threshold, a warning signal is generated to issue a warning.
[0090] Understandably, in the InSAR-based surface deformation monitoring method provided in this invention embodiment, firstly, by acquiring and fusing multi-temporal synthetic aperture radar images and digital elevation model data, a complete data foundation is provided for refined analysis; then, the target area is divided into sub-regions, achieving spatial refinement from macro to micro. Based on this, a "terrain distortion direction index" quantifies the degree of distortion from the perspective of spatial geometry and directional relationships, and an "instantaneous terrain interference degree" quantifies the instantaneous impact from the perspective of single-temporal scattering characteristics, thereby achieving a multi-dimensional and three-dimensional instantaneous mapping of terrain interference. Further, by fusing instantaneous interference information from all temporal phases, a "comprehensive terrain interference index" is determined. This step expands the analysis perspective from a static single-temporal phase to a dynamic temporal series, ensuring that the final index not only includes the intensity information of the interference but also inherently contains its temporal stability characteristics. Finally, by inversely mapping this comprehensive index to the mass weight in the phase unwrapping process and optimizing the unwrapping accordingly, a complete technical link is formed from "raw data" to "interference quantification" and then to "process optimization." This link enables the phase unwrapping process to adaptively distinguish and differentiate between high-confidence regions and low-confidence regions severely affected by terrain, effectively suppressing the propagation and accumulation of phase errors in space, thereby significantly improving the accuracy and overall reliability of deformation inversion results in complex terrain environments such as mountains and hills.
[0091] Please see Figure 3 This illustrates a schematic diagram of the structure of an InSAR-based surface deformation monitoring system provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the InSAR-based surface deformation monitoring system 30 includes a server 31 and an InSAR-based surface deformation monitoring device 10, and the server 31 and the InSAR-based surface deformation monitoring device 10 are connected in communication.
[0092] Server 31 is used to store multi-source monitoring data covering the target area. The InSAR-based surface deformation monitoring device 10 is used to obtain multi-source monitoring data from server 31 through a communication connection with server 31, and execute the InSAR-based surface deformation monitoring method shown in steps S201-S205 above to generate and output the surface deformation monitoring results of the target area, which will not be described in detail here.
[0093] In some embodiments, the InSAR-based surface deformation monitoring device 10 is also used to upload the surface deformation monitoring results of the identified target area to the server 31, whereby the server 31 stores the surface deformation monitoring results.
[0094] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0095] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
Claims
1. A method for monitoring surface deformation based on InSAR, characterized in that, The method includes: Acquire multi-source monitoring data covering the target area, the multi-source monitoring data including multi-temporal synthetic aperture radar image data and digital elevation model data spatially matched with the multi-temporal synthetic aperture radar image data; The target area is divided into multiple sub-regions. For each sub-region in the synthetic aperture radar image of each time phase, the instantaneous terrain interference level of the sub-region is determined based on the multi-source monitoring data. The instantaneous terrain interference level is used to characterize the instantaneous interference intensity caused by terrain factors on the interferometric phase quality of the sub-region under a single imaging time phase. For each sub-region, a comprehensive terrain interference index for each sub-region is determined based on the instantaneous terrain interference level of the sub-region in all time phases. The comprehensive terrain interference index is used to characterize the comprehensive interference intensity of terrain factors on the synthetic aperture radar imaging quality. The terrain interference comprehensive index of each sub-region is mapped to the phase quality weight of each sub-region, and the multi-temporal synthetic aperture radar image is phase unwrapped based on the phase quality weight of each sub-region to obtain the surface deformation monitoring results of the target area.
2. The InSAR-based surface deformation monitoring method according to claim 1, characterized in that, Acquire multi-source monitoring data covering the target area, including: Acquire multi-temporal synthetic aperture radar images covering the target area and corresponding radar system parameters, including the incident angle and azimuth angle; Acquire digital elevation model data that matches the spatial range of the synthetic aperture radar image; The multi-temporal synthetic aperture radar image is preprocessed, including radiometric correction, geometric correction and image registration. The preprocessed multi-temporal synthetic aperture radar imagery is spatially matched with the digital elevation model data.
3. The InSAR-based surface deformation monitoring method according to claim 1, characterized in that, Determining the instantaneous terrain disturbance level of the sub-region includes: The shadow region and the overlapping region are determined based on the grayscale distribution of the synthetic aperture radar image, and the pixels in the sub-region other than the pixels corresponding to the shadow region and the pixels corresponding to the overlapping region are determined as valid pixels; Based on the gray values of the effective pixels, the surface roughness of the sub-region under the given time phase is determined; Based on the multi-source monitoring data, the topographic distortion direction index of the sub-region is determined. The topographic distortion direction index is used to characterize the degree of distortion caused by topographic factors in the sub-region. The instantaneous terrain disturbance level of the sub-region at the given time phase is determined based on the surface roughness and the terrain distortion direction index.
4. The InSAR-based surface deformation monitoring method according to claim 3, characterized in that, Determining the terrain distortion direction index of the sub-region includes: The degree of distortion impact is determined based on the area of the shadowed region, the area of the overlapping region, and the total area of the sub-regions; Determine the topographic slope aspect of the sub-region based on the digital elevation model data; The degree of directional influence of the sub-region is determined based on the terrain slope aspect and the radar azimuth angle corresponding to the synthetic aperture radar image. The terrain distortion direction index of the sub-region is determined based on the degree of distortion influence and the degree of directional influence.
5. The InSAR-based surface deformation monitoring method according to claim 3, characterized in that, The shadow region and overlapping region are determined based on the grayscale distribution of the synthetic aperture radar image, including: According to a preset shadow recognition threshold, pixels with gray values lower than the preset shadow recognition threshold within the sub-region are identified as shadow pixels, and spatially adjacent shadow pixels are clustered to form the shadow region. The overall grayscale mean of the synthetic aperture radar image is determined, and pixels in the sub-region with grayscale values higher than the overall grayscale mean are identified as overlay pixels. Spatially adjacent overlay pixels are clustered to form the overlay region.
6. The InSAR-based surface deformation monitoring method according to claim 3, characterized in that, Determining the surface roughness of the sub-region in the given time phase based on the grayscale values of the effective pixels includes: Based on the gray values of all valid pixels within the sub-region, determine the local gray mean value corresponding to the sub-region; The surface roughness is determined based on the difference between the gray value of each effective pixel and the local gray mean.
7. The InSAR-based surface deformation monitoring method according to claim 1, characterized in that, Determine the comprehensive terrain disturbance index for the sub-region at each time phase, including: Determine the minimum instantaneous terrain disturbance level from the instantaneous terrain disturbance levels of the sub-region across all temporal phases; The offset of the instantaneous terrain disturbance level of each time phase relative to the minimum instantaneous terrain disturbance level is determined, and the terrain morphology stability is determined based on the offset. The terrain morphology stability is used to characterize the temporal stability of the terrain morphology of the sub-region. Based on the instantaneous terrain disturbance level and terrain morphology stability of each time phase, the comprehensive terrain disturbance index of the sub-region under each time phase is determined.
8. The InSAR-based surface deformation monitoring method according to claim 1, characterized in that, Phase unwrapping is performed on the multi-temporal synthetic aperture radar imagery based on the phase quality weight of each sub-region to obtain the surface deformation monitoring results of the target region, including: The phase quality weight of each sub-region is determined as the confidence constraint of each sub-region in the phase unwrapping algorithm. The phase unwrapping algorithm is then used to perform phase unwrapping processing to obtain the unwrapped phase field. Based on the unwrapped phase field and radar system parameters, the surface deformation monitoring results of the target area are determined.
9. A surface deformation monitoring device based on InSAR, characterized in that, include: The data acquisition unit is used to acquire multi-source monitoring data covering the target area. The multi-source monitoring data includes multi-temporal synthetic aperture radar image data and digital elevation model data that spatially matches the multi-temporal synthetic aperture radar image data. The data processing unit is used to divide the target area into multiple sub-regions. For each sub-region in the synthetic aperture radar image of each time phase, the instantaneous terrain interference level of the sub-region is determined according to the multi-source monitoring data. The instantaneous terrain interference level is used to characterize the instantaneous interference intensity caused by terrain factors on the interferometric phase quality of the sub-region under a single imaging time phase. The data processing unit is also used to determine the comprehensive terrain interference index of each sub-region in each time phase based on the real-time terrain interference level of the sub-region in all time phases. The comprehensive terrain interference index is used to characterize the comprehensive interference intensity of terrain factors on the imaging quality of synthetic aperture radar. The deformation monitoring unit is used to map the comprehensive topographic interference index of each sub-region to the phase quality weight of each sub-region, and to perform phase unwrapping processing on the multi-temporal synthetic aperture radar image based on the phase quality weight of each sub-region to obtain the surface deformation monitoring results of the target area.
10. A surface deformation monitoring system based on InSAR, characterized in that, Includes a server and the InSAR-based surface deformation monitoring device as described in claim 9; The server is used to store multi-source monitoring data covering the target area. The multi-source monitoring data includes multi-temporal synthetic aperture radar image data and digital elevation model data that spatially matches the multi-temporal synthetic aperture radar image data. The InSAR-based surface deformation monitoring device is communicatively connected to the server, and is used to obtain the multi-source monitoring data from the server, and execute the InSAR-based surface deformation monitoring method as described in any one of claims 1 to 8, to generate and output the surface deformation monitoring results of the target area.