Surface step deformation extraction method and device based on time-series InSAR

Abstract

The present application provides a method and device for extracting surface step deformation based on time-series InSAR, which belongs to the field of step deformation extraction. The method includes: obtaining the image, time, terrain and location information of the observation point in the observation area; using the small baseline set time-series InSAR strategy to perform baseline screening and time series analysis to generate the corresponding time-series deformation data set; combining the initial moment of the step with the preset steepness parameter, using the hyperbolic tangent function to construct the step model of each observation point; solving all step models by the least squares method to obtain the linear deformation rate, phase reference parameter and step deformation value; finally, all step deformation values are graded and colored to generate a step deformation distribution map. The present application adopts the time-series InSAR strategy, and designs a step model based on the advantages of the hyperbolic tangent function that is globally continuous and differentiable and has obvious step characteristics, so as to accurately calculate the deformation value of each observation point and improve the accuracy and computational efficiency of step surface deformation extraction.

Description

Technical Field

[0001] The present application belongs to the field of step deformation extraction, and specifically relates to a method and device for extracting surface step deformation based on time-series InSAR. Background Art

[0002] Synthetic Aperture Radar (SAR) is an advanced radar remote sensing Earth observation technology. One of its key applications is to obtain surface deformation data through repeated observations of the same area. Surface deformation is a direct reflection of crustal stress release and is key information for analyzing the evolution of disasters. Interferometric Synthetic Aperture Radar (InSAR) technology is widely used in surface deformation monitoring. Compared to point-based observation methods such as the Global Navigation Satellite System, it provides more comprehensive deformation information, facilitating disaster research.

[0003] Among existing InSAR step deformation extraction methods, Differential InSAR (DInSAR) technology plays an important role in rapidly capturing step deformation fields for events such as earthquakes using a single pair of SAR images. However, because it relies solely on interferometry of a single pair of images to capture deformation, it is difficult to effectively remove interfering phases such as atmospheric delay and terrain residuals. Consequently, deformation monitoring accuracy is limited to the centimeter level, limiting its scope of application.

[0004] Traditional time-series InSAR methods effectively suppress atmospheric interference and achieve millimeter-level deformation monitoring accuracy by jointly analyzing multi-view SAR imagery. Persistent Scatterer (PS) InSAR and Small Baseline Subset (SBAS) InSAR, as mainstream methods, are commonly used for monitoring continuous, slow, landmark target deformations but cannot be directly applied to extract step deformations.

[0005] Image stacking technology improves deformation observation accuracy by overlaying multiple image observations to mitigate phase errors caused by atmospheric or topographic factors. However, in areas with significant temporal decoherence, such as densely vegetated areas, this technology struggles to obtain sufficiently effective interferometric pairs. Furthermore, decoherent regions from multiple images accumulate in the deformation map after stacking, resulting in information loss and limiting its application.

[0006] Therefore, there is an urgent need for a deformation extraction method that can obtain surface step deformation with high efficiency and high precision. Summary of the Invention

[0007] The purpose of the embodiments of the present application is to provide a method and device for extracting surface step deformation based on time-series InSAR, so as to alleviate the problem of low accuracy or extraction failure in extracting centimeter-level step surface deformation based on synthetic aperture radar interferometry InSAR technology in complex environments.

[0008] In order to solve the above technical problems, this application is implemented as follows:

[0009] In a first aspect, an embodiment of the present application provides a method for extracting surface step deformation based on time-series InSAR, the method comprising:

[0010] Acquiring relevant data of at least one observation point in an observation area, wherein the observation area includes an area with step deformation, and the relevant data includes image, time, terrain, and location information;

[0011] Based on the small baseline set time series InSAR strategy, baseline screening and time series analysis are performed on the relevant data of each observation point to generate the time series deformation dataset corresponding to each observation point;

[0012] According to the initial moment of step deformation and related data of each observation point, the corresponding step deformation time interval value is calculated;

[0013] Based on the time interval value, the corresponding time series deformation data set and the preset step steepness control parameter, the hyperbolic tangent function is used to construct the step model of each observation point;

[0014] The least squares method is used to solve all constructed step models to obtain the linear deformation rate, phase reference control parameter and step deformation value of each observation point;

[0015] The step deformation values of all observation points are graded and visually labeled with colors to generate a step deformation distribution map of the observation area.

[0016] Preferably, the specific steps of obtaining relevant data of at least one observation point in the observation area include:

[0017] Determine the vector range of the location where the step deformation exists at each observation point;

[0018] According to the vector range, the corresponding ascending or descending SAR image set, the exact time when the step change occurs, the digital elevation model data and the precise orbit data are obtained.

[0019] Preferably, based on the small baseline set time series InSAR strategy, baseline screening and time series analysis are performed on the relevant data of each observation point to generate a time series deformation dataset corresponding to each observation point. The specific steps include:

[0020] The small baseline set InSAR method is used to conduct baseline networking on the SAR image sets in the relevant data, and the SAR image data and InSAR processing baseline network are obtained.

[0021] Obtain unwrapped interferograms based on baseline network and SAR image data;

[0022] Perform time series analysis and deformation extraction on the disentangled interference pattern to obtain a time series deformation dataset.

[0023] Preferably, the small baseline set InSAR method is used to perform baseline networking on the SAR image sets in the relevant data. The specific steps of obtaining the SAR image data and the InSAR processing baseline network include:

[0024] Register all images in the SAR image set to obtain an aligned SAR image set;

[0025] Pair all images in the aligned SAR image set, establish a master-slave relationship, and form a baseline network; the baseline network contains multiple pairing combinations;

[0026] Calculate the temporal and spatial baselines between each pair combination;

[0027] Target pairs are selected from the baseline network according to the baseline threshold to form an interferometric image; wherein, the aligned SAR image set, the baseline network and the interferometric image constitute the SAR image data.

[0028] Preferably, all images in the SAR image set are registered to obtain an aligned SAR image set:

[0029] Taking the master image in the SAR image set as the reference, all slave images are registered with the master image to obtain registered slave images;

[0030] Resampling is performed on each registered slave image to obtain a resampled slave image; wherein the coordinate position of the resampled slave image is consistent with the coordinate position of the master image, and the resampled slave image and the master image form an aligned SAR image set.

[0031] Preferably, the specific steps of obtaining the unwrapped interferogram based on the baseline network and the SAR image data include:

[0032] Perform interferometric processing on SAR image data based on the baseline network to obtain multiple pairs of interferograms;

[0033] Remove the topographic phase in the interferogram and optimize the pixels to obtain a quality-controlled interferogram;

[0034] filtering the quality control interferogram to obtain a filtered interferogram;

[0035] The minimum cost flow method is used to unwrap the filtered interferogram to obtain the unwrapped interferogram.

[0036] Preferably, the step model is:

[0037]

[0038] in, is the hyperbolic tangent function; is the time interval of step deformation; is the cumulative deformation sequence in the temporal deformation dataset; parameter is the step steepness control parameter; is the step deformation value; is the linear deformation rate; is the phase reference control parameter.

[0039] Compared with the prior art, the above technical solution provided by this application has at least the following beneficial effects:

[0040] This application first obtains relevant data of observation points in the observation area; wherein, the observation area includes an area with step deformation, and the relevant data includes image, time, terrain and location information; based on the small baseline set time series InSAR strategy, baseline screening and time series analysis are performed on the relevant data of the observation points to generate a time series deformation data set corresponding to the observation points; based on the hyperbolic tangent function, a step model is constructed according to the time series deformation data set; the step model is solved using the least squares method to obtain the solution result to obtain a step deformation distribution map of the observation area. This application combines the excellent atmospheric interference suppression capability and deformation observation accuracy of time series InSAR with the advantages of the global continuous differentiability and obvious step characteristics of the hyperbolic tangent function to achieve high-efficiency and high-precision step surface deformation extraction.

[0041] In a second aspect, an embodiment of the present application provides a surface step deformation extraction device based on time-series InSAR, comprising:

[0042] An observation module is used to obtain relevant data of at least one observation point in an observation area; wherein the observation area includes an area with step deformation, and the relevant data includes image, time, terrain and location information;

[0043] The image processing module is used to perform baseline screening and time series analysis on the relevant data of each observation point based on the small baseline set time series InSAR strategy, and generate the time series deformation dataset corresponding to each observation point;

[0044] The model building module is used to calculate the corresponding step deformation time interval value based on the initial time of the step deformation at each observation point and related data; based on the time interval value, the corresponding time series deformation data set and the preset step steepness control parameter, the hyperbolic tangent function is used to build the step model of each observation point;

[0045] The model solving module is used to solve all constructed step models using the least squares method to obtain the linear deformation rate, phase reference control parameters and step deformation value of each observation point; the step deformation values of all observation points are graded and visually marked with colors to generate a step deformation distribution map of the observation area.

[0046] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method of the first aspect.

[0047] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0048] It can be understood that the beneficial effects of the technical solutions provided in the second, third and fourth aspects can be found in the relevant description of the first aspect, and will not be repeated here.

[0049] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0051] Figure 1 This is a flow chart of a method for extracting surface step deformation based on time-series InSAR provided in some embodiments of the present application;

[0052] Figure 2 is a data flow chart of a surface step deformation extraction method based on time-series InSAR provided in some embodiments of the present application;

[0053] Figure 3 is a block diagram of a surface step deformation extraction device based on time-series InSAR shown in some embodiments of the present application;

[0054] Figure 4 is a block diagram of an electronic device according to some embodiments of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0056] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and are not used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of this application can be implemented in an order other than those illustrated or described herein. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0057] In the following, in conjunction with the accompanying drawings, a surface step deformation extraction method based on time-series InSAR provided by an embodiment of the present application is described in detail through specific embodiments and application scenarios.

[0058] The step deformations caused by events such as earthquakes and volcanic eruptions exhibit significant temporal characteristics. These deformation processes occur on the order of seconds, a timescale three orders of magnitude different from the SAR satellite revisit period (typically 10-30 days). This temporal abrupt change provides a key entry point for constructing physical constraint models. This example leverages this characteristic to design a step model based on a continuous function and incorporates it as a constraint into the time-series InSAR processing.

[0059] Figure 1 This is a flow chart of a method for extracting surface step deformation based on time-series InSAR according to the first embodiment of the present application. Figure 2 This is a data flow chart of a method for extracting surface step deformation based on time-series InSAR according to the first embodiment of this application. Figure 1 and Figure 2 , the method comprising:

[0060] Step S101: Acquire relevant data of at least one observation point in an observation area; wherein the observation area includes an area with step deformation, and the relevant data includes image, time, terrain and location information;

[0061] Specifically, it includes: defining the vector range of the location where the step deformation exists in the observation point; obtaining the ascending or descending SAR image set, the exact time when the step change occurs, the digital elevation model data and the precise orbit data based on the vector range.

[0062] In one possible implementation, relevant data covering the range where the step deformation occurs is obtained; the data includes SAR image data and auxiliary data, the SAR image data includes ascending SAR imagery or descending SAR imagery, and the auxiliary data includes the time when the step change occurs, digital elevation model data, and precise orbit data; the images are selected as SAR observation images taken 12 months before and 1 month after the step change occurs;

[0063] Furthermore, the vector range where step deformation exists can be the location where disasters such as ground collapse, earthquake, and volcanic eruption occur; the location information where step deformation exists can come from well-known official channels, the digital elevation model data is ALOS WORLD 3D-30m DEM data, and the precise orbit data is POD precise orbit determination ephemeris data.

[0064] Step S102: Based on the small baseline set time series InSAR strategy, baseline screening and time series analysis are performed on the relevant data of each observation point to generate a time series deformation dataset corresponding to each observation point;

[0065] Specifically, it includes: using the small baseline set InSAR method to baseline network the SAR image sets in the relevant data to obtain SAR image data and InSAR processing baseline network; obtaining the unwrapped interferogram based on the baseline network and SAR image data; performing time series analysis and deformation extraction on the unwrapped interferogram to obtain a time series deformation data set.

[0066] Among them, the small baseline set InSAR method is used to perform baseline networking on the SAR image sets in the relevant data to obtain SAR image data and InSAR processing baseline networks, specifically including: aligning all images in the SAR image set to obtain an aligned SAR image set; specifically, taking the master image in the SAR image set as the benchmark, aligning all slave images with the master image to obtain registered slave images; resampling is performed on each registered slave image to obtain a resampled slave image; wherein the coordinate position of the resampled slave image is consistent with the coordinate position of the master image, and the resampled slave image and the master image form an aligned SAR image set.

[0067] It should be noted that in synthetic aperture radar interferometry, the master image refers to the SAR image selected as the reference image in the interferometric pair. It typically exhibits good geometric quality and a moderate temporal and spatial baseline. In subsequent processing, all other images (slave images) are registered and interferometrically processed with the master image. The slave image is the SAR image paired with the master image for interferometric processing. It differs from the master image in time and orbital position, resulting in a phase difference that can be used to infer surface deformation information.

[0068] Then, all images in the aligned SAR image set are paired, a master-slave relationship is established, and a baseline network is formed. The baseline network contains multiple pairing combinations. The temporal baseline and spatial baseline between each pairing combination are calculated. The target pairings are screened from the baseline network according to the baseline threshold to form an interferometric image. The aligned SAR image set, the baseline network, and the interferometric image constitute the SAR image data.

[0069] Among them, the unwrapped interferogram is obtained according to the baseline network and SAR image data, which specifically includes: interferometric processing of the SAR image data based on the baseline network to obtain multiple pairs of interferograms; removing the terrain phase in the interferogram and optimizing the pixels to obtain a quality control interferogram; filtering the quality control interferogram to obtain a filtered interferogram; and unwrapping the filtered interferogram using the minimum cost flow method to obtain an unwrapped interferogram.

[0070] In one possible implementation, the ALOS WORLD 3D-30m DEM data can be used to remove the terrain phase in the interferogram. The average coherence and amplitude deviation values of multiple pairs of interferograms are used to extract low-quality pixels and retain high-quality pixels to obtain quality-controlled interferograms, where the coherence threshold is generally higher than 0.3 and the amplitude deviation threshold is generally lower than 0.4. Then, all quality-controlled interferograms are filtered using the Goldstein method.

[0071] Furthermore, in one possible implementation, the unwrapped interferogram can be input into MintPy or other similar data processing platforms that support time-series InSAR methods to obtain a satellite line-of-sight (LOS) time-series deformation dataset at locations where step deformation occurs. The temporal baseline threshold is typically less than 36 days, and the spatial baseline threshold is typically less than 150 meters.

[0072] Specifically, the unwrapped interferogram and 30m DEM data are imported as input into MintPy (other similar time-series InSAR methods are also acceptable). Following the standard MintPy steps, time-series InSAR processing is performed to obtain a satellite line-of-sight (LOS) time-series deformation dataset (M rows × N columns × K dimensions, where K represents the number of observations) at locations with step deformations. Note that filtering in the time dimension is not permitted in this step.

[0073] Step S103: Calculate the corresponding step deformation time interval value based on the initial time of the step deformation at each observation point and related data; Based on the time interval value, the corresponding time series deformation data set and the preset step steepness control parameter, use the hyperbolic tangent function to construct a step model for each observation point;

[0074] The step model is:

[0075]

[0076] in, is the hyperbolic tangent function; is the time interval of step deformation; is the cumulative deformation sequence in the temporal deformation dataset; parameter is the step steepness control parameter; is the step deformation value; is the linear deformation rate; is the phase reference control parameter.

[0077] In one possible implementation, based on the deformation characteristics and the requirement for model simplicity, the model needs to have the following characteristics:

[0078] (1) The independent variable of this model is time, and the dependent variable is surface deformation;

[0079] (2) The time-deformation curve of the model changes in steps before and after the deformation occurs, and does not change or changes linearly before or after the deformation occurs;

[0080] (3) The model must be composed of continuous functions;

[0081] (4) The model can be combined with time-series InSAR processing.

[0082] Based on the above characteristics, the model is constructed based on the hyperbolic tangent function, which is one of the commonly used hyperbolic functions and is usually denoted as tanh. The function expression is as follows:

[0083]

[0084] The original tanh function is an odd function whose graph passes through the origin and is monotonically increasing. Because it is differentiable throughout its entire domain and its graph is approximately linear near the origin, it is widely used as an activation function in deep learning. When an appropriate constant term is added, the slope of its graph approaches infinity near the origin, allowing it to be used as a step function.

[0085] In one possible implementation, the temporal deformation dataset with step change regions is preprocessed, and the time interval between the observation of each landscape image and the step change is calculated based on the known time of the step change. , earlier than the change time is a negative value, and is a positive value if it is later than. The original time series deformation dataset is straightened into (M×N) rows×K columns; the straightened dataset is used as .

[0086] This embodiment introduces the hyperbolic tangent function to construct a continuous differentiable step model, as shown below:

[0087]

[0088] in, is the cumulative deformation sequence of any observation point on the surface; is a step deformation; is the linear deformation rate; The number of days between the image acquisition time and the step event; parameter Controlling step steepness, This model is used for phase benchmark unification. This model is built based on continuous functions, making the model's expression more concise and easier to solve. Preliminary tests show that the solution time for a single example can reach seconds.

[0089] It should be noted that compared with traditional technologies such as DInSAR and image stacking, this embodiment has a In the process of , it has strong resistance to atmospheric phase error and decoherence error (through the optimization of the step model), The accuracy is higher than that of traditional methods.

[0090] Step S104: Use the least squares method to solve all constructed step models to obtain the linear deformation rate, phase reference control parameter and step deformation value of each observation point; grade the step deformation values of all observation points and visually mark them with colors to generate a step deformation distribution map of the observation area.

[0091] The step model is solved using the least squares algorithm to obtain the step deformation after straightening. ((M×N) rows×1 column), linear deformation rate after straightening ((M × N) rows × 1 column);

[0092] Will and Restore to M rows × N columns, where is the step deformation in the observation area, is the linear deformation rate of the observation area during the observation period.

[0093] The above-mentioned embodiment provides a method for extracting surface step deformation based on time-series InSAR. First, relevant data of observation points in the observation area are obtained. The observation area includes areas where step deformation exists, and the relevant data include image, time, terrain, and location information. Based on the small baseline set time-series InSAR strategy, baseline screening and time series analysis are performed on the relevant data of the observation points to generate a time-series deformation data set corresponding to the observation points. Based on the hyperbolic tangent function, a step model is constructed according to the time-series deformation data set. The step model is solved using the least squares method to obtain a solution result to obtain a step deformation distribution map of the observation area. This embodiment combines the excellent atmospheric interference suppression capability and deformation observation accuracy of time-series InSAR with the advantages of the hyperbolic tangent function's global continuous differentiability and obvious step characteristics to achieve high-efficiency and high-precision step surface deformation extraction.

[0094] It should be noted that the execution entity of the time-series InSAR-based surface step deformation extraction method provided in the embodiments of the present application can be a time-series InSAR-based surface step deformation extraction device, or a control module in the time-series InSAR-based surface step deformation extraction device that is used to execute the loading of the time-series InSAR-based surface step deformation extraction method. In the embodiments of the present application, the time-series InSAR-based surface step deformation extraction device executing the loading of the time-series InSAR-based surface step deformation extraction method is used as an example to illustrate the time-series InSAR-based surface step deformation extraction device provided in the embodiments of the present application.

[0095] Figure 3 This is a schematic diagram of a surface step deformation extraction device based on time-series InSAR according to the second embodiment of the present application. Figure 3 The surface step deformation extraction device 200 based on time-series InSAR includes:

[0096] Observation module 201: Acquire relevant data of at least one observation point in an observation area; wherein the observation area includes an area with step deformation, and the relevant data includes image, time, terrain and location information;

[0097] Specifically, it includes: defining the vector range of the location where the step deformation exists in the observation point; obtaining the ascending or descending SAR image set, the exact time when the step change occurs, the digital elevation model data and the precise orbit data based on the vector range.

[0098] Image processing module 202: Based on the small baseline set time series InSAR strategy, baseline screening and time series analysis are performed on the relevant data of each observation point to generate a time series deformation dataset corresponding to each observation point;

[0099] Specifically, it includes: using the small baseline set InSAR method to baseline network the SAR image sets in the relevant data to obtain SAR image data and InSAR processing baseline network; obtaining the unwrapped interferogram based on the baseline network and SAR image data; performing time series analysis and deformation extraction on the unwrapped interferogram to obtain a time series deformation data set.

[0100] Among them, the small baseline set InSAR method is used to perform baseline networking on the SAR image sets in the relevant data to obtain SAR image data and InSAR processing baseline networks, specifically including: aligning all images in the SAR image set to obtain an aligned SAR image set; specifically, taking the master image in the SAR image set as the benchmark, aligning all slave images with the master image to obtain registered slave images; resampling is performed on each registered slave image to obtain a resampled slave image; wherein the coordinate position of the resampled slave image is consistent with the coordinate position of the master image, and the resampled slave image and the master image form an aligned SAR image set.

[0101] Then, all images in the aligned SAR image set are paired, a master-slave relationship is established, and a baseline network is formed. The baseline network contains multiple pairing combinations. The temporal baseline and spatial baseline between each pairing combination are calculated. The target pairings are screened from the baseline network according to the baseline threshold to form an interferometric image. The aligned SAR image set, the baseline network, and the interferometric image constitute the SAR image data.

[0102] Among them, the unwrapped interferogram is obtained according to the baseline network and SAR image data, which specifically includes: interferometric processing of the SAR image data based on the baseline network to obtain multiple pairs of interferograms; removing the terrain phase in the interferogram and optimizing the pixels to obtain a quality control interferogram; filtering the quality control interferogram to obtain a filtered interferogram; and unwrapping the filtered interferogram using the minimum cost flow method to obtain an unwrapped interferogram.

[0103] Model construction module 203: Calculate the corresponding step deformation time interval value based on the initial time of the step deformation at each observation point and related data; Based on the time interval value, the corresponding time series deformation data set and the preset step steepness control parameter, use the hyperbolic tangent function to construct the step model for each observation point;

[0104] The step model is:

[0105]

[0106] in, is the hyperbolic tangent function; is the time interval of step deformation; is the cumulative deformation sequence in the temporal deformation dataset; parameter is the step steepness control parameter; is the step deformation value; is the linear deformation rate; is the phase reference control parameter.

[0107] Model solving module 204: uses the least squares method to solve all constructed step models to obtain the linear deformation rate, phase reference control parameter and step deformation value of each observation point; the step deformation values of all observation points are graded and visually marked with colors to generate a step deformation distribution map of the observation area.

[0108] The surface step deformation extraction device based on time-series InSAR in the embodiments of the present application can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, the mobile electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. The non-mobile electronic device can be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc., which are not specifically limited in the embodiments of the present application.

[0109] The surface step deformation extraction device based on time-series InSAR in the embodiments of the present application can be a device having an operating system. The operating system can be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiments of the present application.

[0110] The surface step deformation extraction device based on time-series InSAR provided in the embodiment of the present application can achieve Figures 1 to 2 In order to avoid repetition, the various processes implemented by the surface step deformation extraction device based on time-series InSAR in the method embodiment are not described here.

[0111] Optionally, see Figure 4 The embodiment of the present application further provides an electronic device 300, including a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the computer program 303 is executed by the processor 301, each process of the above-mentioned embodiment of the method for extracting surface step deformation based on time-series InSAR is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0112] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned embodiment of the method for extracting surface step deformation based on time-series InSAR is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0113] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk.

[0114] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned embodiment of the surface step deformation extraction method based on time-series InSAR, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0115] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0116] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0117] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of this application.

[0118] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A method for extracting surface step deformation based on time series InSAR, characterized in that: include: Acquiring relevant data of at least one observation point in an observation area; wherein the observation area includes an area with step deformation, and the relevant data includes image, time, terrain, and location information; Based on the small baseline set time series InSAR strategy, baseline screening and time series analysis are performed on the relevant data of each observation point to generate a time series deformation dataset corresponding to each observation point; Calculate the corresponding step deformation time interval value according to the initial time when the step deformation occurs at each observation point and the relevant data; Based on the time interval value, the corresponding time series deformation data set and the preset step steepness control parameter, a step model of each observation point is constructed using a hyperbolic tangent function; The least squares method is used to solve all constructed step models to obtain the linear deformation rate, phase reference control parameter and step deformation value of each observation point; The step deformation values of all observation points are graded and visually labeled with colors to generate a step deformation distribution map of the observation area.

2. The method for extracting surface step deformation based on time series InSAR according to claim 1, characterized in that: The specific steps of obtaining relevant data of at least one observation point in the observation area include: Determine the vector range of the location where the step deformation exists at each observation point; According to the vector range, the corresponding ascending or descending SAR image set, the exact time when the step change occurs, the digital elevation model data and the precise orbit data are obtained to form relevant data.

3. The method for extracting surface step deformation based on time series InSAR according to claim 2, characterized in that: The specific steps of performing baseline screening and time series analysis on the relevant data of each observation point based on the small baseline set time series InSAR strategy to generate a time series deformation data set corresponding to each observation point include: A small baseline set InSAR method is used to respectively perform baseline networking on the SAR image sets in the relevant data to obtain SAR image data and an InSAR processing baseline network; Obtaining a disentangled interferogram based on the baseline network and the SAR image data; Performing time series analysis and deformation extraction on the unwrapped interference pattern to obtain a time series deformation data set.

4. The method for extracting surface step deformation based on time series InSAR according to claim 3, characterized in that: The specific steps of using the small baseline set InSAR method to perform baseline networking on the SAR image sets in the relevant data to obtain SAR image data and InSAR processing baseline networks include: registering all images in the SAR image set to obtain an aligned SAR image set; Pairing all images in the aligned SAR image set, establishing a master-slave relationship and forming a baseline network; wherein the baseline network includes multiple pairing combinations; Calculating the time baseline and the space baseline between each pair combination; Target pairs are screened out from the baseline network according to a baseline threshold to form an interferometric image; wherein the aligned SAR image set, the baseline network and the interferometric image constitute the SAR image data.

5. The method for extracting surface step deformation based on time series InSAR according to claim 4, characterized in that: The specific steps of registering all images in the SAR image set to obtain the aligned SAR image set include: Taking the master image in the SAR image set as a reference, registering all slave images with the master image to obtain registered slave images; Resampling is performed on each of the registered slave images to obtain a resampled slave image; wherein the coordinate position of the resampled slave image is consistent with the coordinate position of the master image, and the resampled slave image and the master image form an aligned SAR image set.

6. The method for extracting surface step deformation based on time series InSAR according to claim 3, characterized in that: The specific steps of obtaining the unwrapped interferogram according to the baseline network and the SAR image data include: performing interferometric processing on the SAR image data based on the baseline network to obtain multiple pairs of interferograms; removing a topographic phase from the interferogram and optimizing pixels to obtain a quality-controlled interferogram; filtering the quality control interferogram to obtain a filtered interferogram; The filtered interference graph is unwrapped using a minimum cost flow method to obtain an unwrapped interference graph.

7. The method for extracting surface step deformation based on time series InSAR according to claim 1, characterized in that: The step model is: in, is the hyperbolic tangent function; is the time interval of step deformation; is the cumulative deformation sequence in the temporal deformation dataset; parameter is the step steepness control parameter; is the step deformation value; is the linear deformation rate; is the phase reference control parameter.

8. A surface step deformation extraction device based on time series InSAR, used to execute the surface step deformation extraction method based on time series InSAR according to any one of claims 1 to 7, characterized in that: include: An observation module, configured to obtain relevant data of at least one observation point in an observation area; wherein the observation area includes an area with step deformation, and the relevant data includes image, time, terrain, and location information; An image processing module is used to perform baseline screening and time series analysis on the relevant data of each observation point based on a small baseline set time series InSAR strategy, and generate a time series deformation data set corresponding to each observation point; A model building module is used to calculate the corresponding step deformation time interval value based on the initial time of the step deformation at each observation point and the relevant data; based on the time interval value, the corresponding time series deformation data set and the preset step steepness control parameter, a hyperbolic tangent function is used to build a step model for each observation point; The model solving module is used to solve all constructed step models using the least squares method to obtain the linear deformation rate, phase reference control parameter and step deformation value of each observation point; the step deformation values of all observation points are graded and visually annotated with colors to generate a step deformation distribution map of the observation area.

9. An electronic device, characterized in that: include: A memory, a processor, and a program or instruction stored in the memory and executable on the processor, wherein when the program or instruction is executed by the processor, the steps of the method for extracting surface step deformation based on time-series InSAR according to any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the surface step deformation extraction method based on time-series InSAR according to any one of claims 1 to 7 are implemented.

Images