Ground surface step deformation extraction method and device based on time sequence InSAR

Abstract

The invention provides a ground surface step deformation extraction method and device based on a time sequence InSAR, and belongs to the field of step deformation extraction. The method comprises the following steps: acquiring image, time, terrain and position information of observation points in an observation area; performing baseline screening and time sequence analysis by adopting a small baseline set time sequence InSAR strategy to generate a corresponding time sequence deformation data set; constructing a step model of each observation point by using a hyperbolic tangent function in combination with the step initial moment and a preset gradient parameter; solving all the step models through a least square method to obtain a linear deformation rate, a phase reference parameter and a step deformation value; and finally, coloring all the step deformation values in a grading manner to generate a step deformation distribution diagram. According to the method, the time sequence InSAR strategy is adopted, and the step model is designed based on the advantages that the hyperbolic tangent function is globally continuous and derivable and the step characteristics are obvious, so that the deformation value of each observation point is accurately calculated, and the precision and the operation efficiency of step surface deformation extraction are improved.

Description

Technical Field

[0001] This 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 technology for observing the Earth's surface. One of its important applications is to obtain surface deformation data by repeatedly observing the same area. Surface deformation is a direct manifestation of crustal stress release and is a key piece of information for analyzing the evolution law of disasters. Interferometric Synthetic Aperture Radar (InSAR) technology is widely used in the field of surface deformation monitoring. Compared with point-like observation means such as the Global Navigation Satellite System, it can provide more comprehensive deformation information and help with disaster research.

[0003] In the existing InSAR step deformation extraction methods, Differential InSAR (DInSAR) technology can quickly obtain the step deformation field of events such as earthquakes by using a single pair of SAR images and plays an important role. However, since it only relies on the interference of a single pair of images to obtain deformation, it is difficult to effectively remove interference phases such as atmospheric delay and topographic residuals, and the deformation monitoring accuracy is only centimeter-level, which limits the application scope. Traditional time-series InSAR methods can effectively suppress atmospheric interference and achieve millimeter-level deformation monitoring accuracy by jointly analyzing multiple SAR images. Among them, Persistent Scatterer (PS) InSAR and Small Baseline Subset (SBAS) InSAR, as mainstream methods, are often used for the deformation monitoring of continuous and slow landmark targets, but cannot be directly used for step deformation extraction. The Stacking technology weakens the error phase caused by factors such as the atmosphere or terrain by superimposing the observation results of multiple images, thereby improving the deformation observation accuracy. However, in areas where the influence of temporal decorrelation is large, such as densely vegetated areas, it is difficult to obtain sufficient effective interferometric pairs, and the decorrelated areas of multiple images will accumulate in the deformation map after stacking processing, resulting in information loss, which also limits its application.

[0004] Therefore, there is an urgent need for a deformation extraction method that can obtain surface step deformation with high efficiency and high accuracy. Summary of the Invention

[0005] The objective of the embodiments of the present application is to provide a method and device for extracting surface step deformations based on time-series InSAR, so as to alleviate the problems of low accuracy or extraction failure in extracting centimeter-level step surface deformations based on the Interferometric Synthetic Aperture Radar (InSAR) technology in complex environments.

[0006] To solve the above technical problems, the present application is implemented as follows: In a first aspect, the embodiments of the present application provide a method for extracting surface step deformations based on time-series InSAR, the method comprising: Obtaining relevant data of at least one observation point in the observation area; wherein, the observation area includes the area with step deformations, and the relevant data includes images, time, terrain, and position information; Based on the small baseline subset time-series InSAR strategy, performing baseline screening and time-series analysis on the relevant data of each observation point to generate a time-series deformation dataset corresponding to each observation point; Calculating the corresponding step deformation time interval value according to the initial moment when the step deformation occurs at each observation point and the relevant data; Based on the time interval value, the corresponding time-series deformation dataset, and a preset step steepness control parameter, constructing a step model for each observation point by using the hyperbolic tangent function; Using the least squares method to solve all the constructed step models to obtain the linear deformation rate, phase reference control parameter, and step deformation value of each observation point; Classifying the step deformation values of all observation points and visually annotating them with colors to generate a step deformation distribution map of the observation area.

[0007] Preferably, the specific steps of obtaining relevant data of at least one observation point in the observation area include: Defining the vector range of the position with step deformations in each observation point; Obtaining the corresponding ascending or descending orbit SAR image set, the accurate moment when the step change occurs, digital elevation model data, and precise orbit data according to the vector range.

[0008] Preferably, the specific steps of performing baseline screening and time-series analysis on the relevant data of each observation point based on the small baseline subset time-series InSAR strategy to generate a time-series deformation dataset corresponding to each observation point include: Using the small baseline subset InSAR method to respectively form a baseline network for the SAR image set in the relevant data to obtain SAR image data and an InSAR processing baseline network; Obtaining an unwrapped interferogram according to the baseline network and the SAR image data; Performing time-series analysis and deformation extraction on the unwrapped interferogram to obtain a time-series deformation dataset.

[0009] Preferably, the specific steps of using the small baseline set InSAR method to respectively perform baseline networking on the SAR image set in the relevant data to obtain SAR image data and the InSAR processing baseline network include: Register all the images in the SAR image set to obtain an aligned SAR image set; Pair up all the images in the aligned SAR image set, establish a master-slave relationship and form a baseline network; among them, the baseline network contains multiple paired combinations; Calculate the temporal baseline and spatial baseline between each paired combination; Select target pairs from the baseline network according to the baseline threshold to form interferometric images; among them, the aligned SAR image set, the baseline network and the interferometric images constitute SAR image data.

[0010] Preferably, register all the images in the SAR image set to obtain an aligned SAR image set: Taking the master image in the SAR image set as a reference, register all the slave images with the master image to obtain registered slave images; Perform resampling on each registered slave image to obtain resampled slave images; among them, the coordinate positions of the resampled slave images are the same as those of the master image, and the resampled slave images and the master image form an aligned SAR image set.

[0011] Preferably, the specific steps of obtaining an unwrapped interferogram according to the baseline network and SAR image data include: Perform interferometric processing on the SAR image data based on the baseline network to obtain multiple pairs of interferograms; Remove the topographic phase in the interferogram and optimize the pixels to obtain a quality-controlled interferogram; Filter the quality-controlled interferogram to obtain a filtered interferogram; Use the minimum cost flow method to unwrap the filtered interferogram to obtain an unwrapped interferogram.

[0012] Preferably, the step model is: Among them, is the hyperbolic tangent function; is the step deformation time interval value; is the cumulative deformation sequence in the time series deformation data set; the parameter is the step steepness control parameter; is the step deformation value; is the linear deformation rate; is the phase reference control parameter.

[0013] Compared with the prior art, the above technical solutions provided by the present application at least include the following beneficial effects: This application first obtains the relevant data of the observation points in the observation area; among them, the observation area includes the area with step deformation, and the relevant data includes images, time, terrain, and position information; based on the small baseline set time series InSAR strategy, the relevant data of the observation points are subjected to baseline screening and time series analysis to generate the 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 least squares method is used to solve the step model to obtain the solution result, so as to obtain the step deformation distribution map of the observation area. This application combines the excellent atmospheric interference suppression ability and deformation observation accuracy of time series InSAR, and the advantages of the hyperbolic tangent function being globally continuously differentiable and having obvious step characteristics to achieve high-efficiency and high-precision extraction of step-like surface deformation.

[0014] In a second aspect, an embodiment of this application provides a device for extracting surface step deformation based on time series InSAR, including: An observation module, configured to obtain the relevant data of at least one observation point in the observation area; among them, the observation area includes the area with step deformation, and the relevant data includes images, time, terrain, and position information; An image processing module, configured 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 to generate the time series deformation data set corresponding to each observation point; A model construction module, configured to calculate the corresponding step deformation time interval value according to the initial time when each observation point undergoes step deformation and the relevant data; based on the time interval value, the corresponding time series deformation data set, and a preset step steepness control parameter, a step model of each observation point is constructed using the hyperbolic tangent function; A model solution module, configured to solve all the 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; classify the step deformation values of all observation points and visually annotate them with colors to generate the step deformation distribution map of the observation area.

[0015] In a third aspect, an embodiment of this application provides an electronic device, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method in the first aspect are implemented.

[0016] In a fourth aspect, an embodiment of this application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0017] It can be understood that the beneficial effects of the technical solutions provided in the above second aspect, third aspect, and fourth aspect can refer to the relevant descriptions in the above first aspect, and will not be repeated here.

[0018] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Brief Description of the Drawings

[0019] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the description of the embodiments in conjunction with the following drawings, where: Figure 1 is a schematic flowchart of a method for extracting surface step deformation based on time series InSAR provided by some embodiments of the present application; Figure 2 is a data flowchart of a method for extracting surface step deformation based on time series InSAR provided by some embodiments of the present application; Figure 3 is a block diagram of a device for extracting surface step deformation based on time series InSAR shown by some embodiments of the present application; Figure 4 is a block diagram of an electronic device shown by some embodiments of the present application. Detailed Description of the Embodiments

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.

[0021] The terms "first", "second", etc. in the description and claims of the present application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data used may be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order different from those illustrated or described herein. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally means an "or" relationship between the associated objects before and after.

[0022] A method for extracting surface step deformation based on time series InSAR provided by the embodiments of the present application will be described in detail below in conjunction with the drawings, through specific embodiments and their application scenarios.

[0023] Step-like deformations formed by events such as earthquakes and volcanic eruptions exhibit significant step characteristics in the time dimension. Their deformation processes are concentrated in the order of seconds, forming a time-scale difference of three orders of magnitude with the SAR satellite revisit period (usually 10 - 30 days). This time-domain mutation feature provides a key entry point for constructing a physical constraint model. In this embodiment, a step model constructed based on continuous functions is designed according to this feature and incorporated as a constraint term into the time-series InSAR processing.

[0024] Figure 1 FIG. is a schematic flow chart of a method for extracting surface step-like deformations based on time-series InSAR according to the first embodiment of the present application. Figure 2 FIG. is a data flow chart of a method for extracting surface step-like deformations based on time-series InSAR according to the first embodiment of the present application. Please refer to Figure 1 and Figure 2 , the method includes: Step S101: Obtain relevant data of at least one observation point in the observation area; wherein, the observation area includes the area with step-like deformations, and the relevant data includes images, time, terrain, and position information; Specifically include: demarcate the vector range of the position with step-like deformations among the observation points; obtain the ascending-orbit or descending-orbit SAR image set, the accurate time when the step change occurs, digital elevation model data, and precise orbit data according to the vector range.

[0025] In a possible implementation manner, obtain relevant data covering the range with step-like deformations; including SAR image data and auxiliary data. The SAR image data includes ascending-orbit SAR images or descending-orbit SAR images, and the auxiliary data includes the time when the step change occurs, digital elevation model data, and precise orbit data; select the SAR observation images in the 12 months before and 1 month after the step change for the images; Furthermore, the demarcated vector range with step-like deformations can be the position where disasters such as ground collapses, earthquakes, and volcanic eruptions occur; the position information with step-like deformations 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 ephemeris data.

[0026] Step S102: Based on the small baseline set time-series InSAR strategy, perform baseline screening and time-series analysis on the relevant data of each observation point to generate a time-series deformation data set corresponding to each observation point; Specifically include: use the small baseline set InSAR method to respectively form a baseline network for the SAR image set in the relevant data to obtain SAR image data and an InSAR processing baseline network; obtain the unwrapped interferogram according to the baseline network and the SAR image data; perform time-series analysis and deformation extraction on the unwrapped interferogram to obtain the time-series deformation data set.

[0027] Among them, the small baseline set InSAR method is used to respectively network the baselines of the SAR image sets in the relevant data, and SAR image data and an InSAR processing baseline network are obtained, specifically including: registering all the images in the SAR image set to obtain an aligned SAR image set; specifically, taking the master image in the SAR image set as a reference, registering all the slave images with the master image to obtain registered slave images; performing resampling on each registered slave image to obtain resampled slave images; among them, the coordinate positions of the resampled slave images are the same as those of the master image, and the resampled slave images and the master image form an aligned SAR image set.

[0028] 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 usually has good geometric quality and moderate temporal and spatial baselines. In subsequent processing, all other images (slave images) will be registered and interfered with the master image. The slave image refers to another SAR image paired with the master image for interferometric processing. It is different from the master image in terms of time and orbital position, and this difference leads to a phase difference, which can be used to invert surface deformation information.

[0029] Next, all the images in the aligned SAR image set are paired pairwise, a master-slave relationship is established and a baseline network is formed; among them, the baseline network contains multiple paired combinations; the temporal baseline and spatial baseline between each paired combination are calculated; target pairs are selected from the baseline network according to the baseline threshold to form interferometric images; among them, the aligned SAR image set, the baseline network, and the interferometric images constitute the SAR image data.

[0030] Among them, an unwrapped interferogram is obtained according to the baseline network and the SAR image data, specifically including: performing interferometric processing on the SAR image data based on the baseline network to obtain multiple pairs of interferograms; removing the topographic phase in the interferograms and optimizing the pixels to obtain a quality-controlled interferogram; filtering the quality-controlled interferogram to obtain a filtered interferogram; using the minimum cost flow method to unwrap the filtered interferogram to obtain an unwrapped interferogram.

[0031] In a possible implementation manner, ALOS WORLD 3D-30m DEM data can be used to remove the topographic phase in the interferograms, and low-quality pixels are proposed using the average coherence and amplitude deviation values of multiple pairs of interferograms, and high-quality pixels are retained to obtain a quality-controlled interferogram, where the coherence threshold is generally higher than 0.3, and the amplitude deviation threshold is generally lower than 0.4; then, the Goldstein method is used to filter all the quality-controlled interferograms.

[0032] Further, in a 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 the locations with step deformations. The time baseline threshold is generally less than 36 days, and the spatial baseline threshold is generally less than 150 meters.

[0033] Specifically, the unwrapped interferogram and 30m DEM data are imported as input data into MintPy (other similar time-series InSAR methods are also applicable); time-series InSAR processing is completed according to the conventional steps of MintPy to obtain a satellite line-of-sight (LOS) time-series deformation dataset (M rows × N columns × K dimensions, where K represents the number of observed images). It should be noted that in this step, filtering should not be performed in the time dimension.

[0034] Step S103: Calculate the corresponding step deformation time interval value based on the initial time and relevant data of the step deformation at each observation point; based on the time interval value, the corresponding time-series deformation dataset, and a preset step steepness control parameter, use the hyperbolic tangent function to construct a step model for each observation point; The step model is: where, is the hyperbolic tangent function; is the step deformation time interval value; is the cumulative deformation sequence in the time-series deformation dataset; the parameter is the step steepness control parameter; is the step deformation value; is the linear deformation rate; is the phase reference control parameter.

[0035] In a possible implementation, according to the deformation characteristics and the requirement for model simplicity, the model should have the following characteristics: (1) The independent variable of this model is time, and the dependent variable is surface deformation; (2) The time-deformation curve of this model has a step change before and after the deformation occurs, and does not change or shows a linear change before or after this moment; (3) This model needs to be composed of continuous functions; (4) This model can be combined with time-series InSAR processing.

[0036] According to the above characteristics, a model is constructed based on the hyperbolic tangent function. The hyperbolic tangent function is one of the common hyperbolic functions, usually denoted as tanh. The function expression is as follows: The original tanh function is an odd function. Its function graph passes through the origin and is monotonically increasing. Since this function is differentiable throughout its domain and its graph is approximately linear near the origin, it is widely used as an activation function in the field of deep learning. When an appropriate constant term is added, the slope of the function graph approaches infinity near the origin and can be used as a step function.

[0037] In a possible implementation, preprocess the time deformation data set with step change regions. Calculate the number of days between each observed image and the step change based on the known time of step change as , earlier than the change time is negative and positive when later. Straighten the original time series deformation data set into (M×N) rows × K columns; use the straightened data set as .

[0038] This embodiment introduces the hyperbolic tangent function to construct a continuously differentiable step model as follows: where is the cumulative deformation sequence of any observation point on the ground surface; is the step deformation; is the linear deformation rate; is the number of days between the image acquisition time and the step event; the parameter controls the step steepness, is used for phase reference unification. This model is constructed based on continuous functions, and the expression of the model is more concise and easier to solve. The solution time of a single example can reach the second level in preliminary tests.

[0039] It should be noted that, compared with traditional technologies such as DInSAR and image stacking (Stacking), this embodiment has a stronger ability to resist atmospheric phase errors and decoherence errors (through the optimization of the step model) in the process of obtaining , and has higher accuracy than traditional methods.

[0040] Step S104: Use the least squares method to solve all the constructed step models to obtain the linear deformation rate, phase reference control parameter, and step deformation value of each observation point; classify the step deformation values of all observation points and visually annotate them with colors to generate a step deformation distribution map of the observation area.

[0041] Use the least squares algorithm to solve the step model to obtain the straightened step deformation ((M×N) rows × 1 column), the straightened linear deformation rate ((M×N) rows × 1 column); Take and is restored to M rows × N columns, where is the step deformation within the observation area, is the linear deformation rate of the observation area during the observation period.

[0042] The method for extracting surface step deformation based on time-series InSAR provided by the above embodiment first obtains the relevant data of the observation points in the observation area; wherein, the observation area includes the area with step deformation, and the relevant data includes images, time, terrain and position information; based on the small baseline set time-series InSAR strategy, the relevant data of the observation points are screened for baselines and time-series analyzed to generate a time-series deformation dataset corresponding to the observation points; based on the hyperbolic tangent function, a step model is constructed according to the time-series deformation dataset; the least squares method is used to solve the step model to obtain the solution result, so as to obtain the step deformation distribution map of the observation area. This embodiment combines the excellent atmospheric interference suppression ability and deformation observation accuracy of time-series InSAR, and the advantages of the hyperbolic tangent function being globally continuously differentiable and having obvious step characteristics, to achieve high-efficiency and high-precision extraction of step-like surface deformation.

[0043] It should be noted that the execution subject of the method for extracting surface step deformation based on time-series InSAR provided by the embodiments of the present application can be a device for extracting surface step deformation based on time-series InSAR, or a control module in the device for extracting surface step deformation based on time-series InSAR for executing the method for loading the extraction of surface step deformation based on time-series InSAR. In the embodiments of the present application, taking the device for extracting surface step deformation based on time-series InSAR as an example to execute the method for loading the extraction of surface step deformation based on time-series InSAR, the device for extracting surface step deformation based on time-series InSAR provided by the embodiments of the present application is described.

[0044] Figure 3 is a schematic diagram of a device for extracting surface step deformation based on time-series InSAR shown in the second embodiment of the present application. Please refer to Figure 3 , the device 200 for extracting surface step deformation based on time-series InSAR includes: Observation module 201: Obtain the relevant data of at least one observation point in the observation area; wherein, the observation area includes the area with step deformation, and the relevant data includes images, time, terrain and position information; Specifically include: delimiting the vector range of the position with step deformation among the observation points; obtaining the ascending or descending orbit SAR image set, the accurate time when the step change occurs, the digital elevation model data and the precise orbit data according to the vector range.

[0045] Image processing module 202: Based on the small baseline set time-series InSAR strategy, screen the baselines and perform time-series analysis on the relevant data of each observation point to generate a time-series deformation dataset corresponding to each observation point; Specifically, it includes: using the small baseline set InSAR method 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 unwrapped interferogram according to the baseline network and the SAR image data; performing time series analysis and deformation extraction on the unwrapped interferogram to obtain a time series deformation data set.

[0046] Among them, using the small baseline set InSAR method 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 specifically includes: registering all the images in the SAR image set to obtain an aligned SAR image set; specifically, taking the master image in the SAR image set as a reference, registering all the slave images with the master image to obtain registered slave images; performing resampling on each registered slave image to obtain resampled slave images; among them, the coordinate positions of the resampled slave images are the same as those of the master image, and the resampled slave images and the master image form an aligned SAR image set.

[0047] Next, pair up all the images in the aligned SAR image set two by two, establish a master-slave relationship and form a baseline network; among them, the baseline network contains multiple paired combinations; calculate the temporal baseline and spatial baseline between each paired combination; screen out target pairs from the baseline network according to the baseline threshold to form interference images; among them, the aligned SAR image set, the baseline network and the interference images constitute the SAR image data.

[0048] Among them, obtaining a unwrapped interferogram according to the baseline network and the SAR image data specifically includes: performing interference processing on the SAR image data based on the baseline network to obtain multiple pairs of interferograms; removing the topographic phase in the interferograms and optimizing the pixels to obtain quality control interferograms; filtering the quality control interferograms to obtain filtered interferograms; using the minimum cost flow method to unwrap the filtered interferograms to obtain unwrapped interferograms.

[0049] Model construction module 203: Calculate the corresponding step deformation time interval value according to the initial moment when 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, use the hyperbolic tangent function to construct a step model for each observation point; The step model is: Among them, is the hyperbolic tangent function; is the step deformation time interval value; is the cumulative deformation sequence in the time series deformation data set; the parameter is the step steepness control parameter; is the step deformation value; is the linear deformation rate; is the phase reference control parameter.

[0050] Model calculation module 204: Solve all the constructed step models by using the least squares method to obtain the linear deformation rate, phase reference control parameter, and step deformation value of each observation point; classify the step deformation values of all observation points and visually annotate them with colors to generate a step deformation distribution map of the observation area.

[0051] The device for extracting surface step deformation 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. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a handheld computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., and the non-mobile electronic device can be a server, a Network Attached Storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc. The embodiments of the present application do not make specific limitations.

[0052] The device for extracting surface step deformation based on time-series InSAR in the embodiments of the present application can be a device with an operating system. The operating system can be an Android operating system, an iOS operating system, or other possible operating systems. The embodiments of the present application do not make specific limitations.

[0053] The device for extracting surface step deformation based on time-series InSAR provided in the embodiments of the present application can implement Figures 1 to 2 each process implemented by the device for extracting surface step deformation based on time-series InSAR in the method embodiments. To avoid repetition, it will not be elaborated here.

[0054] Optionally, please refer to Figure 4 , the embodiments of the present application further provide an electronic device 300, including a processor 301, a memory 302, and a computer program 303 stored on the memory 302 and executable on the processor 301. When the computer program 303 is executed by the processor 301, it implements each process of the above-mentioned method embodiment for extracting surface step deformation based on time-series InSAR and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0055] An embodiment of the present application further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements each process of the above-mentioned embodiment of the method for extracting surface step deformation based on temporal InSAR and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0056] Wherein, the processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk or optical disc, etc.

[0057] Another embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run a program or instruction to implement each process of the above-mentioned embodiment of the method for extracting surface step deformation based on temporal InSAR and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0058] It should be understood that the chip mentioned in the embodiment of the present application may also be referred to as a system-on-chip, system chip, chip system or system-on-chip, etc.

[0059] It should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including that element. In addition, it should be pointed out that the scope of the method and device in the embodiment 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 a reverse 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 be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0060] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, 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 several instructions for causing a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0061] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

Claims

1. A method for extracting surface step deformations based on time series InSAR, characterized in that Comprising: Obtaining relevant data of at least one observation point in the observation area; wherein, the observation area includes an area with step deformation, and the relevant data includes images, time, terrain, and position information; Based on the small baseline set time series InSAR strategy, performing baseline screening and time series analysis on the relevant data of each observation point to generate a time series deformation data set corresponding to each observation point; Calculating the corresponding step deformation time interval value according to the initial moment when 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 a preset step steepness control parameter, constructing a step model for each observation point using the hyperbolic tangent function; Solving all the 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; Classifying the step deformation values of all observation points and visually annotating them with colors to generate a step deformation distribution map of the observation area.

2. The method for extracting surface step deformation based on temporal InSAR according to claim 1, wherein, The specific steps of obtaining relevant data of at least one observation point in the observation area include: Defining the vector range of the position where step deformation exists in each observation point; Obtaining the corresponding ascending or descending orbit SAR image set, the accurate moment when step change occurs, digital elevation model data, and precise orbit data according to the vector range to form relevant data.

3. The method for extracting surface step deformation based on temporal 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: Using the small baseline set InSAR method 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 an unwrapped interferogram according to the baseline network and the SAR image data; Performing time series analysis and deformation extraction on the unwrapped interferogram to obtain a time series deformation data set.

4. The method for extracting surface step deformation based on temporal InSAR according to claim 3, wherein, The specific steps of using the small baseline set InSAR method 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 include: Registering all the images in the SAR image set to obtain an aligned SAR image set; Pairing all the images in the aligned SAR image set pairwise, establishing a master-slave relationship and forming a baseline network; wherein, the baseline network includes multiple pairing combinations; Calculating the time baseline and spatial baseline between each pairing combination; Selecting target pairings from the baseline network according to a baseline threshold to form interferometric images; wherein, the aligned SAR image set, the baseline network, and the interferometric images constitute the SAR image data.

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

6. The method for extracting surface step deformation based on temporal 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 the topographic phase in the interferogram and optimizing pixels to obtain a quality-controlled interferogram; Filtering the quality-controlled interferogram to obtain a filtered interferogram; Unwrapping the filtered interferogram using the minimum cost flow method to obtain an unwrapped interferogram.

7. The method for extracting surface step deformation based on temporal InSAR according to claim 1, characterized in that The step model is: wherein, is the hyperbolic tangent function; is the step deformation time interval value; is the cumulative deformation sequence in the time series deformation data set; the 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 device for extracting surface step deformation based on time-series InSAR, which is used to execute the method for extracting surface step deformation based on time-series InSAR according to any one of claims 1-7, characterized in that, Including: An observation module for obtaining relevant data of at least one observation point in the observation area; wherein, the observation area includes an area with step deformation, and the relevant data includes images, time, topography, and position information; An image processing module for 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; A model construction module for calculating the corresponding step deformation time interval value according to the initial moment when 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 a preset step steepness control parameter, using the hyperbolic tangent function to construct a step model for each observation point; A model solution module for solving all the 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; classifying the step deformation values of all observation points and visually annotating them with colors to generate a step deformation distribution map of the observation area.

9. An electronic device, characterized in that, Including: A memory, a processor, and a program or instruction stored on the memory and executable on the processor, and 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 1-7 are implemented.

10. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium, and 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 1-7 are implemented.

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