A method, device, equipment and storage medium for monitoring frozen soil deformation

Abstract

The present invention discloses a method, device, equipment and storage medium for monitoring the deformation of frozen soil. By obtaining a second quantity of high-coherence differential interferograms corresponding to the frozen soil area; and performing correction processing on each high-coherence differential interferogram based on the atmospheric product to obtain an optimized differential interferogram for calculating the time deformation sequence of the frozen soil area; performing interpolation processing on the preprocessed surface temperature product data to obtain and, according to the daily average temperature data of the frozen soil area, simultaneously calculating the daily average cumulative temperature corresponding to the thawing time interval and the freezing time interval to obtain a daily average cumulative temperature sequence; according to the time deformation sequence and the daily average cumulative temperature sequence, through a segmented elevation model, estimating and obtaining the long-term deformation rate and seasonal deformation amplitude of the frozen soil area, and obtaining the long-term deformation and seasonal deformation of the frozen soil area. Compared with the prior art, the technical solution of the present invention improves the accuracy of the deformation monitoring result of the frozen soil area and simultaneously improves the monitoring efficiency.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and particularly to a method, device, equipment and storage medium for monitoring frozen soil deformation. Background Art

[0002] In general, the natural environment in frozen soil areas is harsh and the terrain is complex. Traditional field measurement methods are inefficient, consuming a large amount of manpower, material resources and financial resources, and can only obtain the results of discrete points. With the development of time series InSAR technology, it is possible to separate the long-term deformation and seasonal deformation of frozen soil areas, provide deformation monitoring results with high spatio-temporal resolution, and provide data support for further analyzing the influence of relevant physical factors;

[0003] At present, time series InSAR is based on estimating the long-term deformation and seasonal deformation of frozen soil through a modeling model. Among them, pure mathematical models are difficult to reveal the correlation between frozen soil and the physical environment; models considering external environmental factors require surface temperature products as external auxiliary data to assist parameter estimation. However, meteorological stations in frozen soil areas are sparsely distributed and it is difficult to arrange observation points. When the research scope is large, there is a large deviation in using the average temperature of several stations to replace the overall temperature change. In addition, the deformation estimated based on the frozen soil deformation model generally assumes that the deformation between two images is a uniform deformation, which does not conform to the actual deformation of frozen soil and may lead to errors in the estimation of deformation rate or amplitude. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: to provide a method, device, equipment and storage medium for monitoring frozen soil deformation, improve the accuracy of the deformation monitoring results in frozen soil areas, and improve the monitoring efficiency at the same time.

[0005] To solve the above technical problem, the present invention provides a method for monitoring frozen soil deformation, including:

[0006] Obtain and obtain a first number of differential interferograms according to multiple SAR image maps of the frozen soil area, and select a second number of high-coherence differential interferograms based on the coherence corresponding to each differential interferogram;

[0007] Obtain the atmospheric products corresponding to the multiple SAR image maps, and perform correction processing on each high-coherence differential interferogram based on the atmospheric products to obtain optimized differential interferograms, and at the same time calculate the time deformation sequence of the frozen soil area through a preset formula according to the coefficient matrix of the optimized differential interferograms;

[0008] Obtain and interpolate the preprocessed surface temperature product data of the permafrost region to obtain the daily average temperature data of the permafrost region. Based on the daily average temperature data, obtain the thawing time interval and freezing time interval of the permafrost region, calculate the daily average cumulative temperature corresponding to the thawing time interval and the freezing time interval respectively, and normalize the daily average cumulative temperature to obtain the daily average cumulative temperature sequence;

[0009] According to the time deformation sequence and the daily average cumulative temperature sequence, through a segmented elevation model, estimate and obtain the long-term deformation and seasonal deformation of the permafrost region based on the long-term deformation rate and seasonal deformation amplitude of the permafrost region.

[0010] Furthermore, obtain the atmospheric products corresponding to the multiple SAR image maps, and perform correction processing on each high-coherence differential interferogram based on the atmospheric products. Specifically:

[0011] According to the acquisition time of the multiple SAR image maps, obtain the atmospheric products corresponding to the multiple SAR image maps, perform cropping and encoding processing on each atmospheric product to obtain the corresponding first atmospheric correction product, and based on a preset formula, obtain the first atmospheric correction phase of each first atmospheric correction product;

[0012] Obtain the second atmospheric correction product corresponding to each high-coherence differential interferogram, perform differential processing on the second atmospheric correction product to obtain the second atmospheric correction phase of each high-coherence differential interferogram, and perform atmospheric error correction processing on each high-coherence differential interferogram based on the second atmospheric correction phase;

[0013] At the same time, obtain the long-wave trend error corresponding to each high-coherence differential interferogram based on an iterative quadratic surface fitting model, and perform long-wave trend error correction processing on each high-coherence differential interferogram.

[0014] Furthermore, preprocess the surface temperature product data of the permafrost region. Specifically:

[0015] Obtain the surface temperature product data of the permafrost region, where the surface temperature product data includes daily surface temperature product data and 8-day surface temperature product data;

[0016] Respectively obtain the daytime temperature data and nighttime temperature data corresponding to the daily surface temperature product data and the 8-day surface temperature product data;

[0017] Extract the valid values from the daytime temperature data and the nighttime temperature data respectively to obtain the valid values of the daytime temperature data and the nighttime temperature data, and calculate the means of the valid values of the daytime temperature data and the nighttime temperature data respectively.

[0018] Further, the method for obtaining and based on multiple SAR image maps of the permafrost region to obtain a first number of differential interferograms, and selecting a second number of high-coherence differential interferograms based on the coherence corresponding to each differential interferogram is as follows:

[0019] Obtain multiple SAR image maps of the permafrost region, register the multiple SAR image maps in the same coordinate system, and set the spatio-temporal baseline to obtain a first number of differential interferograms and their corresponding coherence maps;

[0020] According to the coherence maps, obtain the coherence corresponding to each differential interferogram, and select a second number of high-coherence differential interferograms according to the coherence, where the second number is not greater than the first number.

[0021] Further, the present invention also provides a permafrost deformation monitoring device, including: a differential interferogram acquisition module, a time deformation sequence acquisition module, a daily average cumulative temperature sequence acquisition module, and an estimation module;

[0022] Among them, the differential interferogram acquisition module is used to obtain and based on multiple SAR image maps of the permafrost region to obtain a first number of differential interferograms, and select a second number of high-coherence differential interferograms based on the coherence corresponding to each differential interferogram;

[0023] The time deformation sequence acquisition module is used to obtain the atmospheric products corresponding to the multiple SAR image maps, and perform correction processing on each high-coherence differential interferogram based on the atmospheric products to obtain optimized differential interferograms, and at the same time calculate the time deformation sequence of the permafrost region according to the coefficient matrix of the optimized differential interferograms through a preset formula;

[0024] The daily average cumulative temperature sequence acquisition module is used to obtain and perform interpolation processing on the preprocessed surface temperature product data of the permafrost region to obtain the daily average temperature data of the permafrost region, and based on the daily average temperature data, obtain the thawing time interval and the freezing time interval of the permafrost region, calculate the daily average cumulative temperature corresponding to the thawing time interval and the freezing time interval respectively, and perform normalization processing on the daily average cumulative temperature to obtain a daily average cumulative temperature sequence;

[0025] The estimation module is configured to estimate, according to the time deformation sequence and the daily average cumulative temperature sequence, through a segmented elevation model, the long-term deformation rate and the seasonal deformation amplitude of the permafrost region, and obtain the long-term deformation and the seasonal deformation of the permafrost region.

[0026] Further, the time deformation sequence acquisition module is configured to acquire the atmospheric products corresponding to the multiple SAR image maps, and perform correction processing on each high-coherence differential interferogram based on the atmospheric products. Specifically:

[0027] The time deformation sequence acquisition module is configured to acquire the atmospheric products corresponding to the multiple SAR image maps according to the acquisition times of the multiple SAR image maps, perform cropping and encoding processing on each atmospheric product to obtain a corresponding first atmospheric correction product, and obtain the first atmospheric correction phase of each first atmospheric correction product based on a preset formula;

[0028] The time deformation sequence acquisition module is configured to acquire a second atmospheric correction product corresponding to each high-coherence differential interferogram, perform differential processing on the second atmospheric correction product to obtain the second atmospheric correction phase of each high-coherence differential interferogram, and perform atmospheric error correction processing on each high-coherence differential interferogram based on the second atmospheric correction phase;

[0029] The time deformation sequence acquisition module is configured to obtain the long-wave trend error corresponding to each high-coherence differential interferogram based on an iterative quadratic surface fitting model, and perform long-wave trend error correction processing on each high-coherence differential interferogram.

[0030] Further, the daily average cumulative temperature sequence acquisition module is configured to perform preprocessing on the surface temperature product data of the permafrost region. Specifically:

[0031] The daily average cumulative temperature sequence acquisition module is configured to acquire the surface temperature product data of the permafrost region, where the surface temperature product data includes daily surface temperature product data and 8-day surface temperature product data;

[0032] The daily average cumulative temperature sequence acquisition module is configured to respectively acquire the daytime temperature data and the nighttime temperature data corresponding to the daily surface temperature product data and the 8-day surface temperature product data;

[0033] The daily average cumulative temperature sequence acquisition module is configured to extract the effective values of the daytime temperature data and the nighttime temperature data, respectively obtain the effective values of the daytime temperature data and the nighttime temperature data, and respectively calculate the means of the effective values of the daytime temperature data and the nighttime temperature data.

[0034] Further, the differential interferogram acquisition module is configured to obtain and, based on multiple SAR image maps of the frozen soil area, obtain a first number of differential interferograms, and select a second number of high-coherence differential interferograms based on the coherence corresponding to each differential interferogram, specifically:

[0035] The differential interferogram acquisition module is configured to obtain multiple SAR image maps of the frozen soil area, register the multiple SAR image maps in the same coordinate system, and obtain a first number of differential interferograms and their corresponding coherence maps by setting the spatio-temporal baseline;

[0036] The differential interferogram acquisition module is configured to obtain the coherence corresponding to each differential interferogram according to the coherence map, and select a second number of high-coherence differential interferograms according to the coherence, where the second number is not greater than the first number.

[0037] Further, the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for monitoring frozen soil deformation as described in any one of the above is implemented.

[0038] Further, the present invention also provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, the device where the computer-readable storage medium is located is controlled to execute the method for monitoring frozen soil deformation as described in any one of the above.

[0039] A method, device, equipment, and storage medium for monitoring frozen soil deformation according to an embodiment of the present invention have the following beneficial effects compared with the prior art:

[0040] By performing differential processing on multiple SAR image maps of the frozen soil area obtained, a second quantity of high-coherence differential interferograms is obtained, and based on the atmospheric products, the high-coherence differential interferograms are corrected to obtain optimized differential interferograms. Based on the coefficient matrix of the optimized differential interferograms, the time deformation sequence of the frozen soil area is calculated according to a preset formula, avoiding the situation where the deformation estimated based on the frozen soil deformation model in the prior art does not conform to the actual deformation of the frozen soil, and providing the accuracy of the time deformation sequence of the obtained frozen soil area; at the same time, the surface temperature product data of the frozen soil area is obtained through the surface temperature product for preprocessing, and interpolation processing is performed on the surface temperature product data after preprocessing to obtain the daily average temperature data of the frozen soil area, effectively solving the problem that the meteorological stations in the frozen soil area are sparsely distributed and it is difficult to meet the spatial resolution. At the same time, based on the daily average temperature data, the daily average cumulative temperature corresponding to the thawing time interval and the freezing time interval is calculated respectively, and a normalized daily average cumulative temperature sequence is generated; according to the time deformation sequence and the daily average cumulative temperature sequence, through a segmented elevation model, the long-term deformation rate and seasonal deformation amplitude of the frozen soil area are estimated to obtain the long-term deformation and seasonal deformation of the frozen soil area. Compared with the prior art, the technical solution of the present invention does not need to estimate the deformation of the frozen soil through a frozen soil deformation model, improves the accuracy of the frozen soil deformation monitoring data, and the provided technical solution is highly operable and is beneficial to improving the monitoring efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic flowchart of an embodiment of a frozen soil deformation monitoring method provided by the present invention;

[0042] Figure 2 is a schematic structural diagram of an embodiment of a frozen soil deformation monitoring device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Embodiment 1

[0045] Refer to Figure 1 , Figure 1 which is a schematic flowchart of an embodiment of a frozen soil deformation monitoring method provided by the present invention. As Figure 1 shown, the method includes steps 101 - 104, specifically as follows:

[0046] Step 101: Obtain a first number of differential interferograms based on multiple SAR image maps of the permafrost region, and select a second number of high-coherence differential interferograms based on the coherence corresponding to each differential interferogram.

[0047] In this embodiment, multiple SAR image maps covering the same permafrost region are obtained, and the acquisition timings of the multiple SAR image data are all different; the multiple SAR image maps with different timings are registered to the same SAR image coordinate system through precise orbit data, and the spatio-temporal baselines are set to be less than a certain threshold, where the certain threshold can be set according to user requirements; by performing pairwise differential processing on the multiple SAR image maps, a first number of differential interference pairs are obtained, where each differential interference pair has a corresponding differential interferogram and a coherence map corresponding to the differential interferogram, and each differential interferogram is composed of two different SAR image maps.

[0048] In this embodiment, the first number of differential interferograms are optimally selected using each differential interferogram and its corresponding coherence map. By obtaining the coherence corresponding to each differential interferogram and based on the coherence, the differential interferograms with large-scale incoherence in the first number of differential interferograms are eliminated. After eliminating the differential interferograms with large-scale incoherence, a second number of high-coherence differential interferograms are selected, where the above second number is not greater than the first number, and the second number M1 and the first number M satisfy the following formula:

[0049]

[0050] where M is the first number, M1 is the second number, and N is the number of SAR image maps.

[0051] In this embodiment, after selecting the second number of high-coherence differential interferograms, each high-coherence differential interferogram is filtered using SAR data processing software to obtain the coherence map corresponding to each filtered high-coherence differential interferogram. At the same time, each high-coherence differential interferogram is also unwrapped using SAR data processing software to obtain the differential interference phase corresponding to each unwrapped high-coherence differential interferogram.

[0052] Step 102: Obtain the atmospheric products corresponding to the multiple SAR image maps, and perform correction processing on each high-coherence differential interferogram based on the atmospheric products to obtain optimized differential interferograms. At the same time, according to the coefficient matrix of the optimized differential interferograms, calculate the time deformation sequence of the permafrost region through a preset formula.

[0053] In this embodiment, according to the acquisition time of the multiple SAR image maps, the corresponding atmospheric products of the multiple SAR image maps are obtained, and each atmospheric product is cropped and encoded to obtain the corresponding first atmospheric correction product. Based on a preset formula, the first atmospheric correction phase of each first atmospheric correction product is obtained. Specifically, the obtained atmospheric product is the GACOS atmospheric product. Since the time resolution of the GACOS atmospheric product is one minute, according to the acquisition time of the multiple SAR image maps, the GACOS atmospheric product corresponding to the acquisition moment of each SAR image map is downloaded. Since the coordinate system of the GACOS atmospheric product is the WGS84 coordinate system, in order to unify the coordinate system, the coordinate system of the GACOS atmospheric product needs to be converted to the SSAR image coordinate system. Among them, the conversion of the coordinate system of the GACOS atmospheric product is mainly achieved by arbitrarily selecting one of the multiple SAR image maps as the SAR main image data, cropping each obtained GACOS atmospheric product according to the longitude and latitude data of the SAR main image, and performing forward encoding on each GACOS atmospheric product based on the geocoding function in the GAMMA software, so as to project the GACOS atmospheric product originally in the WGS84 coordinate system onto the range - azimuth coordinate system of the SAR image, and obtain the first atmospheric correction product in the SAR image coordinate system. At the same time, according to the phase conversion formula, the distance unit of the first atmospheric correction product is converted into phase and projected onto the radar line - of - sight direction to obtain the first atmospheric correction phase φ of the first atmospheric correction product corresponding to each SAR image map. GACOS , and the phase conversion formula is as follows:

[0054]

[0055] where GACOS cut is the GACOS atmospheric product data after cropping, θ is the radar incidence angle, and λ is the radar center wavelength.

[0056] In this embodiment, by obtaining the second atmospheric correction product corresponding to each high-coherence differential interferogram, and performing differential processing on the second atmospheric correction product, the second atmospheric correction phase of each high-coherence differential interferogram is obtained, and based on the second atmospheric correction phase, atmospheric error correction processing is performed on each high-coherence differential interferogram; specifically, according to the second number of high-coherence differential interferograms obtained in step 101, the acquisition times of two SAR image maps in each high-coherence differential interferogram are obtained, and according to the acquisition times, two corresponding first atmospheric correction products are obtained and used as the second atmospheric correction product corresponding to each high-coherence differential interferogram. Differential processing is performed on the second atmospheric correction product, that is, the atmospheric phase data of the two second atmospheric correction products corresponding to each high-coherence differential interferogram are subtracted to obtain the second atmospheric correction phase of each high-coherence differential interferogram, and based on the second atmospheric correction phase, the differential interferogram phase corresponding to each high-coherence differential interferogram is subtracted from the corresponding second atmospheric correction phase, that is, the atmospheric error correction of each high-coherence differential interferogram is completed.

[0057] In this embodiment, the long-wave trend error corresponding to each high-coherence differential interferogram is also obtained based on the iterative quadratic surface fitting model, and long-wave trend error correction processing is performed on each high-coherence differential interferogram. Specifically, the iterative quadratic surface fitting model is as follows:

[0058] R orbit = b0 + b1x + b2y + b3xy + b4x 2 + b5y 2 ;

[0059] where R orbit is the phase value uniformly extracted from the high-coherence differential interferogram, x and y are the coordinate values in the SAR image coordinate system, and b0 to b5 are the iterative quadratic surface fitting model parameters.

[0060] In this embodiment, the long-wavelength trend error of all pixels in each high-coherence differential interferogram is estimated through the model parameters, and the long-wavelength trend error of all pixels is subtracted from the high-coherence differential interferogram to complete the long-wave trend error correction processing of each high-coherence differential interferogram.

[0061] In this embodiment, the high-coherence differential interferogram that has undergone atmospheric error correction and long-wave trend error correction processing is used as the optimized differential interferogram.

[0062] In this embodiment, according to the coefficient matrix of the optimized differential interferogram, the time deformation sequence of the frozen soil area is calculated through a preset formula. Specifically: Obtain the coefficient matrix corresponding to the optimized differential interferogram. When the coefficient matrix is not rank-deficient, directly solve the time deformation sequence D = [d2 d3 … d N of the frozen soil area through a hypothesis-free method according to the preset formula. The preset formula is as follows:

[0063]

[0064] where φ i.j is the optimized differential interferogram.

[0065] In this embodiment, based on the above preset formula, the time deformation sequence of the frozen soil area can be directly solved, avoiding the situation in the prior art where the deformation of the frozen soil area estimated based on the frozen soil deformation model does not match the actual deformation of the frozen soil area, making the obtained time deformation sequence of the frozen soil area more accurate.

[0066] Step 103: Obtain and perform interpolation processing on the preprocessed surface temperature product data of the frozen soil area to obtain the daily average temperature data of the frozen soil area. Based on the daily average temperature data, obtain the thawing time interval and freezing time interval of the frozen soil area, calculate the daily average cumulative temperature corresponding to the thawing time interval and the freezing time interval respectively, and perform normalization processing on the daily average cumulative temperature to obtain the daily average cumulative temperature sequence.

[0067] In this embodiment, after obtaining the surface temperature product data of the frozen soil area, it is necessary to preprocess the surface temperature product data of the frozen soil area. Among them, the surface temperature product data is the MODIS surface temperature product. The MODIS surface temperature product uses daily surface temperature product data and 8-day surface temperature product data, and both the daily surface temperature product data and the 8-day surface temperature product data include daytime temperature data, nighttime temperature data, quality data of daytime temperature, and quality data of nighttime temperature.

[0068] In this embodiment, the daytime temperature data and the nighttime temperature data corresponding to the daily land surface temperature product data and the 8-day land surface temperature product data are obtained respectively. Since both the daytime temperature data and the nighttime temperature data are DN values, and the valid range of their data is from 7500 to 65535, with the remaining values being invalid values, the valid values of the daytime temperature data and the nighttime temperature data are extracted respectively to obtain the valid value data of the daytime temperature data and the nighttime temperature data. At the same time, the quality data of the daytime temperature and the quality data of the nighttime temperature corresponding to the daily land surface temperature product data and the 8-day land surface temperature product data are obtained respectively. Since the quality data of the daytime temperature and the quality data of the nighttime temperature are identified by 8-bit binary numbers, in this embodiment, the points where the 0-3 bits in the quality data of the daytime temperature and the quality data of the nighttime temperature are not 0 are taken as error points, and the corresponding daytime temperature data or nighttime temperature data of the error point data are removed. The mean values of the valid values of the daytime temperature data and the nighttime temperature data corresponding to the daily land surface temperature product data and the 8-day land surface temperature product data after removing the invalid value data and the error point data are calculated respectively. Since this mean value is a DN value, in this embodiment, the mean values of the valid values of the daytime temperature data and the nighttime temperature data are also converted into the corresponding Celsius temperature LST. The Celsius temperature conversion formula is as follows:

[0069] LST = DN * 0.02 - 273.15.

[0070] In this embodiment, after preprocessing the MODIS land surface temperature product, it is also necessary to perform interpolation processing on the land surface temperature product data of the frozen soil area after preprocessing to obtain the daily average temperature data of the frozen soil area. Specifically, the daily land surface temperature data is interpolated according to the 8-day land surface temperature products MYD11A1 and MOD11A2 in terms of time, that is, the mean value of the valid values of the daytime temperature data and the nighttime temperature data in the 8-day land surface temperature product data calculated above is used as the temperature mean value of the 8 days before the date of obtaining the daily land surface temperature data, and 8 times of this temperature mean value is obtained as the total temperature of the 8 days before the date of obtaining the daily land surface temperature data. And if there are temperature data measured in the daily land surface temperature products for 4 days or more within these 8 days, the sum of the temperature data of the daily land surface temperature products corresponding to the number of days with temperature data is obtained, where 4 days or more is a preset quantity, and this quantity can be adjusted accordingly according to user needs.

[0071] In this embodiment, the total temperature of the 8 days before the date of obtaining the daily land surface temperature data is subtracted by the temperature data of the daily land surface temperature products corresponding to the number of days with temperature data, and the difference is obtained. Then the difference is divided by the number of days without temperature data within 8 days, and the calculated value is used as the daily average land surface temperature of the daily land surface temperature products without temperature time data within these 8 days.

[0072] In this embodiment, for the average daily surface temperature of the daily surface temperature product without temperature-time data, based on the spatial analysis tool under ArcGIS software, the Kriging interpolation method is selected to perform spatial interpolation processing on the average daily surface temperature data without temperature-time data, and the surface temperature data of all times at a point in the spatial position is used as a one-dimensional sequence, and time interpolation processing is performed through the cubic spline function provided by Matlab to obtain continuous average daily temperature data in the permafrost region, solving the problem that the distribution of meteorological stations in the permafrost region is sparse and it is difficult to meet the spatial distribution rate in the prior art.

[0073] In this embodiment, the thawing time interval and the freezing time interval of the permafrost region are obtained, the average daily cumulative temperature corresponding to the thawing time interval and the freezing time interval is calculated respectively, and a normalized average daily cumulative temperature sequence is generated. Specifically, according to the above-obtained average daily temperature data, the start time when the temperature is greater than 0 for 8 consecutive days is set as the thawing node T t , and the start time when the temperature is less than 0 for 8 consecutive days is set as the freezing node T f , when T t ≤T<T f , it is in the thawing interval, and when T f ≤T<T t , it is in the freezing interval. According to the freezing node and the thawing node, the freezing interval and the thawing interval of the permafrost region are obtained respectively every year, the average daily cumulative temperature ADDT in the freezing interval and the average daily cumulative temperature ADDF in the thawing interval are calculated segment by segment, and the average daily cumulative temperature ADDT in the freezing interval and the average daily cumulative temperature ADDF in the thawing interval are normalized to obtain the normalized average daily cumulative temperature sequence of the permafrost region, where the average daily cumulative temperature sequence includes the normalized average daily cumulative temperature ADDT in the freezing interval and the average daily cumulative temperature ADDF in the thawing interval.

[0074] Step 104: According to the time deformation sequence and the average daily cumulative temperature sequence, through a segmented elevation model, estimate and obtain the long-term deformation and seasonal deformation of the permafrost region according to the long-term deformation rate and seasonal deformation amplitude of the permafrost region.

[0075] In this embodiment, according to the time deformation sequence of the permafrost region obtained in step 102 and the normalized average daily cumulative temperature sequence of the permafrost region obtained in step 103, its long-term deformation rate and seasonal deformation amplitude are estimated through a segmented elevation model, where the segmented elevation model is shown as follows:

[0076]

[0077] Among them, D(t) is the deformation of the optimized differential interferogram at time t, V is the long-term deformation rate, S is the amplitude of the seasonal deformation, and c is a constant.

[0078] In this embodiment, the obtained long-term deformation rate is integrated with time to obtain the long-term deformation of the frozen soil area; the amplitude of the obtained seasonal deformation is multiplied by the normalized daily average cumulative temperature sequence to obtain the seasonal deformation of the frozen soil area.

[0079] In this embodiment, the time deformation sequence of the frozen soil area is directly solved by a hypothesis-free method, and then combined with the normalized daily average cumulative temperature sequence to estimate the long-term deformation rate and seasonal amplitude of the frozen soil. Compared with the prior art, there is no need to make assumptions through a frozen soil deformation model, making the estimated long-term deformation rate and seasonal deformation amplitude of the frozen soil area more accurate, and the estimation process in this embodiment is more operable and convenient to apply.

[0080] See Figure 2 , Figure 2 is a schematic structural diagram of an embodiment of a frozen soil deformation monitoring device provided by the present invention. As Figure 2 shown, the device includes a differential interferogram acquisition module 201, a time deformation sequence acquisition module 202, a daily average cumulative temperature sequence acquisition module 203, and an estimation module 204, specifically as follows:

[0081] The differential interferogram acquisition module 201 is configured to acquire and obtain a first number of differential interferograms based on multiple SAR image maps of the frozen soil area, and select a second number of high-coherence differential interferograms based on the coherence corresponding to each differential interferogram.

[0082] The time deformation sequence acquisition module 202 is configured to acquire the atmospheric products corresponding to the multiple SAR image maps, perform correction processing on each high-coherence differential interferogram based on the atmospheric products to obtain an optimized differential interferogram, and calculate the time deformation sequence of the frozen soil area through a preset formula according to the coefficient matrix of the optimized differential interferogram;

[0083] The daily average cumulative temperature sequence acquisition module 203 is configured to acquire and perform interpolation processing on the preprocessed surface temperature product data of the frozen soil area to obtain the daily average temperature data of the frozen soil area, and based on the daily average temperature data, acquire the thawing time interval and freezing time interval of the frozen soil area, respectively calculate the daily average cumulative temperature corresponding to the thawing time interval and the freezing time interval, and perform normalization processing on the daily average cumulative temperature to obtain a daily average cumulative temperature sequence;

[0084] An estimation module 204, configured to estimate, according to the time deformation sequence and the daily average cumulative temperature sequence, through a segmented elevation model, the long-term deformation rate and seasonal deformation amplitude of the frozen soil area, and obtain the long-term deformation and seasonal deformation of the frozen soil area.

[0085] In this embodiment, the time deformation sequence acquisition module 202 is configured to acquire the atmospheric products corresponding to the multiple SAR image maps, and perform correction processing on each high-coherence differential interferogram based on the atmospheric products. Specifically: the time deformation sequence acquisition module 202 is configured to acquire the atmospheric products corresponding to the multiple SAR image maps according to the acquisition times of the multiple SAR image maps, perform cropping and encoding processing on each atmospheric product to obtain a corresponding first atmospheric correction product, and obtain the first atmospheric correction phase of each first atmospheric correction product based on a preset formula; the time deformation sequence acquisition module 202 is configured to acquire the second atmospheric correction product corresponding to each high-coherence differential interferogram, perform differential processing on the second atmospheric correction product to obtain the second atmospheric correction phase of each high-coherence differential interferogram, and perform atmospheric error correction processing on each high-coherence differential interferogram based on the second atmospheric correction phase; the time deformation sequence acquisition module 202 is configured to obtain the long-wave trend error corresponding to each high-coherence differential interferogram based on an iterative quadratic surface fitting model, and perform long-wave trend error correction processing on each high-coherence differential interferogram.

[0086] In this embodiment, the daily average cumulative temperature sequence acquisition module 203 is configured to perform preprocessing on the surface temperature product data of the frozen soil area. Specifically: the daily average cumulative temperature sequence acquisition module 203 is configured to acquire the surface temperature product data of the frozen soil area, where the surface temperature product data includes daily surface temperature product data and 8-day surface temperature product data; the daily average cumulative temperature sequence acquisition module 203 is configured to respectively acquire the daytime temperature data and nighttime temperature data corresponding to the daily surface temperature product data and the 8-day surface temperature product data; the daily average cumulative temperature sequence acquisition module 203 is configured to extract the valid values of the daytime temperature data and the nighttime temperature data, respectively obtain the valid values of the daytime temperature data and the nighttime temperature data, and respectively calculate the means of the valid values of the daytime temperature data and the nighttime temperature data.

[0087] In this embodiment, the differential interferogram acquisition module 201 is configured to obtain and, based on multiple SAR image maps of the frozen soil area, obtain a first number of differential interferograms, and select a second number of high-coherence differential interferograms based on the coherence corresponding to each differential interferogram. Specifically, the differential interferogram acquisition module 201 is configured to obtain multiple SAR image maps of the frozen soil area, register the multiple SAR image maps in the same coordinate system, and set the spatio-temporal baseline to obtain a first number of differential interferograms and their corresponding coherence maps; the differential interferogram acquisition module 201 is configured to obtain the coherence corresponding to each differential interferogram according to the coherence map, and select a second number of high-coherence differential interferograms according to the coherence, where the second number is not greater than the first number.

[0088] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described device can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated herein.

[0089] It should be noted that the embodiments of the above-described frozen soil deformation monitoring device are merely illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] Based on the above embodiment of the frozen soil deformation monitoring method, another embodiment of the present invention provides a frozen soil deformation monitoring terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the frozen soil deformation monitoring method of any embodiment of the present invention is implemented.

[0091] Exemplarily, in this embodiment, the computer program can be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the frozen soil deformation monitoring terminal device.

[0092] The frozen soil deformation monitoring terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The frozen soil deformation monitoring terminal device may include, but is not limited to, a processor and a memory.

[0093] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the frozen soil deformation monitoring terminal device, and connects various parts of the entire frozen soil deformation monitoring terminal device through various interfaces and lines.

[0094] The memory can be used to store the computer program and / or module. The processor realizes various functions of the frozen soil deformation monitoring terminal device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0095] Based on the embodiments of the above-mentioned frozen soil deformation monitoring method, another embodiment of the present invention provides a storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute the frozen soil deformation monitoring method of any embodiment of the present invention.

[0096] In this embodiment, the above storage medium is a computer-readable storage medium, and the computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0097] In summary, a method, device, equipment and storage medium for monitoring frozen soil deformation provided by the present invention obtain a first number of differential interferograms based on multiple SAR image maps of a frozen soil area, and select a second number of high-coherence differential interferograms based on the coherence corresponding to each differential interferogram; obtain the atmospheric products corresponding to the multiple SAR image maps, and perform correction processing on each high-coherence differential interferogram based on the atmospheric products to obtain optimized differential interferograms, and at the same time calculate the time deformation sequence of the frozen soil area through a preset formula according to the coefficient matrix of the optimized differential interferograms; obtain and perform interpolation processing on the preprocessed surface temperature product data of the frozen soil area to obtain the daily average temperature data of the frozen soil area, and based on the daily average temperature data, obtain the thawing time interval and freezing time interval of the frozen soil area, respectively calculate the daily average cumulative temperature corresponding to the thawing time interval and the freezing time interval, and perform normalization processing on the daily average cumulative temperature to obtain a daily average cumulative temperature sequence; according to the time deformation sequence and the daily average cumulative temperature sequence, estimate through a segmented elevation model and obtain the long-term deformation rate and seasonal deformation amplitude of the frozen soil area according to the long-term deformation and seasonal deformation of the frozen soil area. Compared with the prior art, the technical solution provided by the present application improves the accuracy of the frozen soil area deformation monitoring result and at the same time improves the monitoring efficiency.

[0098] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and replacements can still be made, and these improvements and replacements should also be regarded as the protection scope of the present invention.

Claims

1. A method for monitoring frozen soil deformation, characterized in that, Including: Obtaining a first quantity of differential interferograms based on multiple SAR image maps of a permafrost region, and selecting a second quantity of high-coherence differential interferograms based on the coherence corresponding to each differential interferogram, including: Obtaining multiple SAR image maps of the permafrost region, registering the multiple SAR image maps in the same coordinate system, and obtaining a first quantity of differential interferograms and their corresponding coherence maps by setting the spatio-temporal baseline; Obtaining the coherence corresponding to each differential interferogram according to the coherence map, and selecting a second quantity of high-coherence differential interferograms according to the coherence, where the second quantity is not greater than the first quantity; Obtaining the atmospheric products corresponding to the multiple SAR image maps, and performing correction processing on each high-coherence differential interferogram based on the atmospheric products to obtain optimized differential interferograms. At the same time, according to the coefficient matrix of the optimized differential interferograms, calculating the time deformation sequence of the permafrost region through a preset formula, including: According to the acquisition time of the multiple SAR image maps, obtaining the atmospheric products corresponding to the multiple SAR image maps, performing cropping and encoding processing on each atmospheric product to obtain corresponding first atmospheric correction products, and obtaining the first atmospheric correction phases of each first atmospheric correction product based on a preset formula; Obtaining the second atmospheric correction products corresponding to each high-coherence differential interferogram, performing differential processing on the second atmospheric correction products to obtain the second atmospheric correction phases of each high-coherence differential interferogram, and performing atmospheric error correction processing on each high-coherence differential interferogram based on the second atmospheric correction phases; Obtaining the long-wave trend errors corresponding to each high-coherence differential interferogram based on an iterative quadratic surface fitting model, and performing long-wave trend error correction processing on each high-coherence differential interferogram; Obtaining and interpolating the preprocessed surface temperature product data of the permafrost region to obtain the daily average temperature data of the permafrost region, and based on the daily average temperature data, obtaining the thawing time interval and freezing time interval of the permafrost region, respectively calculating the daily average cumulative temperature corresponding to the thawing time interval and the freezing time interval, and normalizing the daily average cumulative temperature to obtain a daily average cumulative temperature sequence; According to the time deformation sequence and the daily average cumulative temperature sequence, estimating and obtaining the long-term deformation and seasonal deformation of the permafrost region based on the long-term deformation rate and seasonal deformation amplitude of the permafrost region through a segmented elevation model.

2. The method for monitoring frozen soil deformation according to claim 1, wherein Performing preprocessing on the surface temperature product data of the permafrost region, specifically: Obtaining the surface temperature product data of the permafrost region, where the surface temperature product data includes daily surface temperature product data and 8-day surface temperature product data; Respectively obtaining the daytime temperature data and nighttime temperature data corresponding to the daily surface temperature product data and the 8-day surface temperature product data; Extract the valid values from the daytime temperature data and the nighttime temperature data respectively to obtain the valid values of the daytime temperature data and the nighttime temperature data, and calculate the means of the valid values of the daytime temperature data and the nighttime temperature data respectively.

3. A permafrost deformation monitoring device, characterized in that, Including: Differential interferogram acquisition module, time deformation sequence acquisition module, daily average cumulative temperature sequence acquisition module, and estimation module; Among them, the differential interferogram acquisition module is used to obtain and, based on multiple SAR image maps of the frozen soil area, obtain a first quantity of differential interferograms, and select a second quantity of high-coherence differential interferograms based on the coherence corresponding to each differential interferogram, including: Obtain multiple SAR image maps of the frozen soil area, register the multiple SAR image maps in the same coordinate system, and obtain a first quantity of differential interferograms and their corresponding coherence maps by setting the spatio-temporal baseline; According to the coherence maps, obtain the coherence corresponding to each differential interferogram, and select a second quantity of high-coherence differential interferograms according to the coherence, where the second quantity is not greater than the first quantity; The time deformation sequence acquisition module is used to obtain the atmospheric products corresponding to the multiple SAR image maps, and perform correction processing on each high-coherence differential interferogram based on the atmospheric products to obtain optimized differential interferograms. At the same time, according to the coefficient matrix of the optimized differential interferograms, calculate the time deformation sequence of the frozen soil area through a preset formula, including: According to the acquisition time of the multiple SAR image maps, obtain the atmospheric products corresponding to the multiple SAR image maps, perform cropping and coding processing on each atmospheric product to obtain corresponding first atmospheric correction products, and obtain the first atmospheric correction phases of each first atmospheric correction product based on a preset formula; Obtain the second atmospheric correction products corresponding to each high-coherence differential interferogram, perform differential processing on the second atmospheric correction products to obtain the second atmospheric correction phases of each high-coherence differential interferogram, and perform atmospheric error correction processing on each high-coherence differential interferogram based on the second atmospheric correction phases; Obtain the long-wave trend errors corresponding to each high-coherence differential interferogram based on the iterative quadratic surface fitting model, and perform long-wave trend error correction processing on each high-coherence differential interferogram; The daily average cumulative temperature sequence acquisition module is used to obtain and perform interpolation processing on the preprocessed surface temperature product data of the frozen soil area to obtain the daily average temperature data of the frozen soil area, and based on the daily average temperature data, obtain the thawing time interval and freezing time interval of the frozen soil area, calculate the daily average cumulative temperature corresponding to the thawing time interval and the freezing time interval respectively, and perform normalization processing on the daily average cumulative temperature to obtain the daily average cumulative temperature sequence; The estimation module is used to estimate and, according to the long-term deformation rate and seasonal deformation amplitude of the frozen soil area, obtain the long-term deformation and seasonal deformation of the frozen soil area based on the time deformation sequence and the daily average cumulative temperature sequence through a segmented elevation model.

4. The permafrost deformation monitoring device according to claim 3, characterized in that, The daily average cumulative temperature sequence acquisition module is used to preprocess the surface temperature product data of the permafrost region, specifically: The daily average cumulative temperature sequence acquisition module is used to acquire the surface temperature product data of the permafrost region, wherein the surface temperature product data includes daily surface temperature product data and 8-day surface temperature product data; The daily average cumulative temperature sequence acquisition module is used to respectively acquire the daytime temperature data and the nighttime temperature data corresponding to the daily surface temperature product data and the 8-day surface temperature product data; The daily average cumulative temperature sequence acquisition module is used to extract the effective values of the daytime temperature data and the nighttime temperature data, respectively obtain the effective values of the daytime temperature data and the nighttime temperature data, and respectively calculate the means of the effective values of the daytime temperature data and the nighttime temperature data.

5. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the permafrost deformation monitoring method according to any one of claims 1 to 2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the permafrost deformation monitoring method according to any one of claims 1 to 2.

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