CN112666070A - Soil erosion calculation method - Google Patents
Soil erosion calculation method Download PDFInfo
- Publication number
- CN112666070A CN112666070A CN202011595305.3A CN202011595305A CN112666070A CN 112666070 A CN112666070 A CN 112666070A CN 202011595305 A CN202011595305 A CN 202011595305A CN 112666070 A CN112666070 A CN 112666070A
- Authority
- CN
- China
- Prior art keywords
- soil erosion
- deformation
- inversion
- data
- unwrapping
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention provides a soil erosion calculation method which is characterized by comprising the following steps of S1, obtaining SAR original image data, and cutting the SAR original image data; s2 monitoring the deformation of the earth surface; according to the invention, the microwave image is adopted, soil erosion is inverted by calculating micro earth surface deformation, microwave data is introduced, the research range of the remote sensing technology is greatly expanded, and particularly in winter. Meanwhile, the defects of the existing soil erosion model are considered, the soil erosion research means can be effectively expanded, and the application field of microwave remote sensing is deepened.
Description
Background
Soil erosion is an important component of the regional ecological environment and is also a major manifestation of deterioration of the ecological environment. The reservoir area of the dam belongs to a water and soil loss strength erosion area, and as one of the most main influence factors of the fragility of the ecological environment, soil erosion becomes a prominent problem in the ecological environment construction of the reservoir area of the dam. Soil erosion leads to direct warehousing of a large amount of silt, effective reservoir capacity of a reservoir is reduced to a certain extent, storage regulation capacity is reduced, a river channel water passing section is atrophied, flood drainage is not smooth, the service life of a dam reservoir area is seriously influenced, and the flood control power generation function is exerted. In addition, a large amount of pollutants carried by the deposited silt can cause secondary pollution of the water body.
The quantitative evaluation of the soil erosion of the reservoir area at the present stage mainly comprises two types: (1) quantitative simulation is carried out by using different soil erosion models. Soil erosion models can be divided into physical models, distribution models and empirical models according to the purpose of the established method and the simulated erosion process. Representative physical models are the European soil erosion model (EUROSEM), the non-point source region basin environmental reaction model (ANSWERS); the distribution model is most typical of the SHE model; empirical models include the soil loss equation (USLE), modified Universal soil loss equation (RUSLE) and the Chinese Soil Loss Equation (CSLE). (2) On the basis of field investigation, a soil erosion model suitable for a reservoir area is created through runoff plot observation or artificial rainfall simulation. Although most soil erosion models become important means for studying soil erosion of reservoir areas of dams due to concise calculation formulas and lower data requirements, seasonal dynamics of vegetation coverage and seasonal nonuniformity of rainfall are often ignored in the using process of the models, rainfall erosion force factors calculated by total annual rainfall and single vegetation coverage factors are often used for operation, the soil erosion models only reflect the soil erosion experience relationship of the areas, and the models are not applicable to other areas or smaller scales and have certain limitations. Besides the two approaches, close-range photogrammetry technology and optical remote sensing data are also applied to monitoring the deformation of the micro landform in the hydro-fluctuation area of the reservoir area of the dam, and the wave erosion process of the hydro-fluctuation area is revealed. Although the close-range photogrammetry technology and the optical remote sensing data play an important role in the research on the soil erosion of the hydro-fluctuation area in the reservoir area of the dam, the reservoir area has complex topography, so that the photogrammetry error is large for the shade-covered valley area, the surface detail information cannot be well reflected, and the reservoir area of the dam has the characteristics of more cloud, less fog and less sunshine, so that the optical remote sensing data are greatly limited in the research on the soil erosion of the hydro-fluctuation area. Therefore, it is particularly critical to propose a research method suitable for the soil erosion in the hydro-fluctuation area.
Disclosure of Invention
The invention aims to solve the technical problems that the photogrammetry error of a close-range photogrammetry technology and optical remote sensing data is larger for a shade-covered valley area in the soil erosion research of a hydro-fluctuation area of a dam reservoir, the earth surface detail information cannot be well reflected, and the optical remote sensing data is greatly limited in the soil erosion research of the hydro-fluctuation area.
The invention provides a soil erosion calculation method, which comprises the following steps,
s1, acquiring SAR original image data, and cutting the SAR original image data;
synthetic Aperture radar (sar), an active earth observation system, can be installed on flight platforms such as airplanes, satellites, spacecraft, etc., and can perform earth observation all day long and all day long, and has a certain ground surface penetration capability.
S2 monitoring the deformation of the earth surface;
further, the step S2 includes,
s21 generating a connection graph, performing pairing processing on the input data by an interference image pair, and setting a time baseline to 365d (365 days) and a space baseline to 2 m;
s22, interfering the workflow, carrying out interference processing on the paired interference image pairs, carrying out registration on the paired relative pairs according to DEM data, removing terrain phase, obtaining a texture interference image, and setting the multi-view vision as 1: 4;
a Digital Elevation Model (DEM), which is a solid ground Model that uses a group of ordered numerical arrays to represent ground Elevation, is a branch of a Digital Terrain Model (DTM), from which various other Terrain feature values can be derived.
And S23, performing phase unwrapping, wherein the scale factors of the azimuth direction and the distance direction are 3 by using a 3D unwrapping method, and the unwrapping coherence threshold is 0.3. After all images are accurately registered to the super main image, filtering, flattening, unwrapping and the like are carried out to generate all matched flattening interferograms, coherence coefficient maps and unwrapping maps;
s24, refining and leveling the track, wherein the refining and re-leveling the track are mainly to select a scene to be interfered relatively after interference workflow processing, add a reference DEM, select a control point and correct track errors;
s25, carrying out first inversion to estimate the deformation rate and the residual terrain;
s26, performing second inversion, and removing the atmospheric phase by using a filtering method on the basis of the deformation rate estimated for the first time to obtain the final time sequence deformation quantity;
s27, geocoding, namely geocoding the result obtained by the second inversion, converting the inversion result into a geographic coordinate unified with the reference DEM, and correcting the deformation influence of the terrain change on the image;
and S28, soil erosion inversion, namely obtaining regional landmark deformation data according to the steps, and dividing the regional landmark deformation data into an erosion region and a stacking region according to the calculation result and the landmark deformation characteristics to obtain soil erosion inversion data.
The beneficial effect of the invention is that,
according to the method, the microwave image is adopted, soil erosion is inverted by calculating micro earth surface deformation, microwave data is introduced, the research range of the remote sensing technology is greatly expanded, and particularly in winter. Meanwhile, the method is introduced into the research on the soil erosion of the reservoir area of the dam by considering the defects of the existing soil erosion model, so that the research means of the soil erosion can be effectively expanded, and the application field of the microwave remote sensing is deepened.
The invention well solves the quantitative research of the deposition part in the soil erosion process, sets the precision threshold from the generation and the editing of the connection diagram to the geocoding, can adjust corresponding parameters through repeated tests, improves the inversion precision and has simple and convenient operation.
In general, a new idea and a new way can be provided for the research of soil erosion in small watersheds.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a connection diagram with space-time baselines of 365d and 2m, respectively.
FIG. 3 flattens the interferogram.
FIG. 4 is a schematic diagram of a network flow algorithm model.
FIG. 5 is a diagram of a surface deformation result according to an embodiment of the present invention.
FIG. 6 is a diagram illustrating the results of surface deformation according to one embodiment of the present invention.
FIG. 7 is a diagram illustrating the results of surface deformation according to one embodiment of the present invention.
FIG. 8 is a diagram of a surface deformation result according to an embodiment of the present invention.
Detailed Description
The inventive idea of the present invention is that,
1. the introduction of microwave data into the study on soil erosion in reservoir areas of dams is one of the innovative points of the invention. With the rise and rapid development of remote sensing technology, the research on soil erosion enters a new stage. The remote sensing technology has the characteristics of high acquisition speed, large information amount, low cost, less limited conditions, repeatability, time saving, labor saving and the like, and is widely applied to the field of soil erosion. In addition, image data of global multiband, multi-temporal, different resolutions and all-weather angles acquired by the remote sensing technology provides favorable conditions for researching typical landform space distribution patterns of regions with severe natural conditions, although optical remote sensing plays an important role in soil erosion research, the optical remote sensing is easily influenced by factors such as meteorological conditions and the like, the all-weather working capability is poor, the application of the optical remote sensing in soil erosion research of reservoir areas of dams is limited to a certain extent, and the defects can be exactly made up by microwave remote sensing.
2. From the perspective of surface deformation, the method starts with two aspects of an erosion area and a stacking area, and comprehensive inversion of soil erosion is another innovative point of the method. The traditional soil erosion model generally focuses on considering the erosion process and neglecting the deposition part, and the soil erosion is divided into two parts of erosion and accumulation according to the inverted surface deformation result, so that the regional soil erosion is comprehensively calculated.
3. By freely combining the image pairs with shorter baselines, a series of time sequence interferogram subsets based on different main images are generated, and then a singular value decomposition method of a matrix is utilized to combine a plurality of short baselines to solve, so that the problem of time discontinuity caused by overlong baselines among data sets is effectively solved, the monitoring time resolution is improved, and a deformation sequence and an average sedimentation rate covering the whole observation time are obtained.
The terms used in the present invention will be explained below.
Interpretation of terms:
radar remote sensing: the microwave remote sensing radar remote sensing device is one of microwave remote sensing, the wavelength of electromagnetic waves emitted by a radar remote sensing platform is generally longer (1mm-1m), and the microwave remote sensing radar remote sensing platform is mainly used for detecting the backscattering coefficient and the dielectric constant of a target body.
Soil erosion: the soil or other ground constituent materials are degraded, destroyed, separated, transported and deposited under the action of natural forces or under the combined action of natural forces and human activities. According to the kind of the foreign forces, the soil erosion can be divided into hydraulic erosion, wind erosion, freeze-thaw erosion, gravity erosion, leaching erosion, torrential flood erosion, debris flow erosion, soil collapse and the like.
Example 1
As shown in FIG. 1, the present invention provides a soil erosion calculation method
Step 1: and (4) preprocessing data. Considering that the SAR is an original image, the coverage area is wide, the data volume is large, and the calculation efficiency is affected, the original SAR image data needs to be cut.
Step 2: and monitoring the deformation of the earth surface.
(1) And generating a connection graph, wherein the process carries out pairing processing on the input data of the interference image pair, if N scene images are input, the obtained maximum pairing digit (N x (N-1))/2 is obtained, and a connection graph generating tool selects an optimal combination mode for pairing. This process requires setting temporal and spatial baselines, generally speaking, the larger the temporal and spatial baselines, the better the inversion effect. The time base depends on the area of investigation and can be set at 500-800 days in dry areas, while in wet or dense areas the value is reduced, otherwise decoherence is caused. In order to determine the optimal space-time baseline for soil erosion inversion of the Pengxi river basin, after a plurality of tests, when the space-time baseline is 365d and 2m respectively, the inversion effect is good, the result of the connection graph is relatively uniform, no image pair discarding phenomenon exists, and the situation that SBAS data sets can be paired under an ideal state is met is shown in fig. 2.
(2) And (3) interfering the working flow, wherein the process mainly carries out interference treatment on the paired interference image pairs, and carries out registration on the paired pairs according to DEM data, removes the terrain phase and acquires an interference image with better quality. The process needs to set the multi-view, and generally, the effect is better when the view is more than 1: 1. Through the trial, the vision is 1:4, the interference pattern has obvious fringes, and the inversion result is better shown in figure 3.
(3) And (4) phase unwrapping, wherein the 3D unwrapping method can be used for effectively unwrapping the low-coherence region. The azimuth and distance scale factors are 3, and the unwrapping coherence threshold is 0.3. After all the images are accurately registered to the super main image, all the matched flattening interferograms, coherence coefficient graphs and unwrapping graphs are generated through the steps of filtering, flattening, unwrapping and the like. The basic principle and formula of the filtering algorithm are as follows: dividing an interference pattern into a plurality of mutually overlapped phase blocks; ② converting the frequency domain into a frequency domain according to Fourier transform, and processing H (u, v) ═ S { | F (u, v) | }the power spectrumαF (u, v), wherein S {. is a smoothing operator; f (u, v) and H (u, v) are frequency domain interferograms before and after filtering respectively; (u, v) are coordinate values of a certain pixel point in the interferogram in the frequency domain; alpha is a filter parameter, and alpha belongs to [0, 1 ]]. The specific formula of the 3D unwrapping method is as follows: phi is aij＝φij+2πkij，φij∈[-π，π]，kijI.e., the phase unwrapping result, kijThe size of the network flow on each side of the triangle is represented as shown in fig. 4, the positive and negative residual error points are regarded as the supply point and the demand point, the connection problem of the residual error points is converted into the network flow problem, and in the constructed sparse triangular network, an L1 norm optimization model is adopted to minimize the total compensation number, namely min Σ | cijkij|。
(4) And (3) track refining and flattening, wherein the track refining and the re-flattening are mainly performed with interference workflow processing, a scene is selected to represent the interference of most results, a reference DEM is added, a control point is selected, and track errors are corrected. The control points try to select the points with small phase in the graph after the unwrapping, such as town and country resident points. The common method is to check and judge the deplat interferogram, the coherence coefficient map and the unwrapping map generated by all pairs one by one, select and generate a GCP file on the unwrapping map or the interferogram in a manual mode, and estimate and remove residual constant phases and phase ramps still existing after unwrapping.
(5) The first inversion. The first inversion estimates deformation rate and residual topography by Singular Value Decomposition (SVD), which is an important matrix decomposition method in linear algebra, and for an m × n matrix, if orthogonal matrixes U (m × m order) and V (n × n order) exist, A ═ U Σ VTThis process is a singular value decomposition, where U is an M × N orthogonal matrix and the diagonal elements of Σ are singular values σ i (i ═ 1...., N), V is an M × N orthogonal matrix, and its least-squares norm solution is:
wherein ∑-1＝diag(1/σ1,...,1/σN-L+1,0,...,0). In order to obtain a solution which accords with the meaning, the solution of the phase is converted into the solution of the phase velocity, and the parameter vector to be solved is shown as follows
(6) And (5) performing second inversion. And on the basis of the deformation rate estimated for the first time, removing the atmospheric phase by using a filtering method to obtain the final time sequence deformation quantity. The filtering process includes that assuming the used template is K, the gaussian filtering in the spatial domain can be expressed as F ═ K × Y, where x represents the convolution operation, Y is the noisy image, and the template K selects a fixed size (3 pixels × 3 pixels).
(7) And geocoding, namely geocoding the result obtained by the second inversion, converting the inversion result into a geographical coordinate unified with the reference DEM, correcting the deformation influence of the terrain change on the image, and generating an LOS (line of sight) deformation rate result (figures 5-8). The key to geocoding is the geolocation process, which isThe method is used for determining the coordinates of the earth surface pixel points in a preselected reference Cartesian coordinate system, and obtaining the coordinate values (x) corresponding to the pixels in the reference Cartesian coordinate system by solving the following nonlinear equationse,ye,ze)：r＝|s(t)-P|，V(t)·(s(t)-P)＝0，
Wherein P is the pixel position vector to be obtained, V (t) is the satellite velocity vector, s (t) is the satellite vector position, r is the distance between the satellite and the sight line direction of the reference ellipsoid, a and b are the major axis radius and the minor axis radius of the reference ellipsoid, and h is the height of the target relative to the reference ellipsoid.
(8) Soil erosion inversion, namely obtaining regional landmark deformation data according to the steps, dividing the regional landmark deformation data into an erosion region and a accumulation region according to the calculation result and the landmark deformation characteristics, thereby obtaining soil erosion inversion data (2017 + 2018 Kaizhou district hydro-fluctuation belt average soil erosion modulus is 53.74t/(hm h m)2A), the amount of soil erosion was 9178.75t/a with reference to the fine sand density.
The method utilizes the radar to transmit microwaves to a target area, then receives echoes reflected by the target to obtain an SAR complex image pair imaged by the same target area, if a coherence condition exists between the complex image pair, the SAR complex image pair is subjected to conjugate multiplication to obtain an interference diagram, and the path difference of the microwaves in two imaging processes is obtained according to the phase value of the interference diagram, so that the small changes of the terrain, the landform and the surface of the target area are calculated, and the method can be used for digital elevation model establishment, crust deformation detection and the like
The method has the advantages that the microwave image is adopted, the micro earth surface deformation is calculated, the soil erosion is inverted, the microwave data is introduced, the research range of the remote sensing technology is greatly expanded, and particularly in winter. Meanwhile, the defects of the existing soil erosion model are considered, the soil erosion research means is effectively expanded, and the application field of microwave remote sensing is deepened.
The invention well solves the quantitative research of the deposition part in the soil erosion process, sets the precision threshold from the generation and the editing of the connection diagram to the geocoding, can adjust corresponding parameters through repeated tests, improves the inversion precision and has simple and convenient operation.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (2)
1. A soil erosion calculation method, comprising the steps of,
s1, acquiring SAR original image data, and cutting the SAR original image data;
s2 monitors the surface deformation.
2. The soil erosion calculation method of claim 1, wherein said step S2 includes,
s21 generating a connection graph, performing pairing processing on the input data by an interference image pair, and setting a time baseline to be 365d and a space baseline to be 2 m;
s22, interfering the workflow, carrying out interference processing on the paired interference image pairs, carrying out registration on the paired relative pairs according to DEM data, removing terrain phase, obtaining a texture interference image, and setting the multi-view vision as 1: 4;
s23, performing phase unwrapping, namely, generating a flattened interferogram, a coherence coefficient graph and an unwrapping graph which are matched in pairs by accurately registering all images to a super main image through the steps of filtering, flattening, unwrapping and the like by using a 3D unwrapping method, wherein the scale factors of the azimuth direction and the distance direction are 3, the unwrapping coherence threshold is 0.3;
s24, refining and leveling the track, wherein the refining and re-leveling the track are mainly to select a scene to be interfered relatively after interference workflow processing, add a reference DEM, select a control point and correct track errors;
s25, carrying out first inversion to estimate the deformation rate and the residual terrain;
s26, performing second inversion, and removing the atmospheric phase by using a filtering method on the basis of the deformation rate estimated for the first time to obtain the final time sequence deformation quantity;
s27, geocoding, namely geocoding the result obtained by the second inversion, converting the inversion result into a geographic coordinate unified with the reference DEM, and correcting the deformation influence of the terrain change on the image;
and S28, soil erosion inversion, namely obtaining regional landmark deformation data according to the steps, and dividing the regional landmark deformation data into an erosion region and a stacking region according to the calculation result and the landmark deformation characteristics to obtain soil erosion inversion data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011595305.3A CN112666070A (en) | 2020-12-29 | 2020-12-29 | Soil erosion calculation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011595305.3A CN112666070A (en) | 2020-12-29 | 2020-12-29 | Soil erosion calculation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112666070A true CN112666070A (en) | 2021-04-16 |
Family
ID=75411929
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011595305.3A Pending CN112666070A (en) | 2020-12-29 | 2020-12-29 | Soil erosion calculation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112666070A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117312746A (en) * | 2023-08-08 | 2023-12-29 | 中国科学院地理科学与资源研究所 | Rainfall erosion force calculation method based on satellite rainfall data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103293285A (en) * | 2013-06-01 | 2013-09-11 | 西北农林科技大学 | Method for determining soil erosion on drainage basin or regional scale |
| CN108801211A (en) * | 2018-04-08 | 2018-11-13 | 山东农业大学 | A kind of sinking land in coalmining areas INSAR image method for quickly identifying |
| CN110780297A (en) * | 2019-09-30 | 2020-02-11 | 中国国土勘测规划院 | Method and system for monitoring soil erosion by adopting satellite-borne synthetic aperture radar |
| CN111998766A (en) * | 2020-08-31 | 2020-11-27 | 同济大学 | Surface deformation inversion method based on time sequence InSAR technology |
-
2020
- 2020-12-29 CN CN202011595305.3A patent/CN112666070A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103293285A (en) * | 2013-06-01 | 2013-09-11 | 西北农林科技大学 | Method for determining soil erosion on drainage basin or regional scale |
| CN108801211A (en) * | 2018-04-08 | 2018-11-13 | 山东农业大学 | A kind of sinking land in coalmining areas INSAR image method for quickly identifying |
| CN110780297A (en) * | 2019-09-30 | 2020-02-11 | 中国国土勘测规划院 | Method and system for monitoring soil erosion by adopting satellite-borne synthetic aperture radar |
| CN111998766A (en) * | 2020-08-31 | 2020-11-27 | 同济大学 | Surface deformation inversion method based on time sequence InSAR technology |
Non-Patent Citations (3)
| Title |
|---|
| 刘辉: "《MIMO稀疏阵列SAR天线设计与三维成像技术》", 21 March 2019 * |
| 吴良斌: "《SAR图像处理与目标识别》", 31 January 2013 * |
| 焦明连等: "《合成孔径雷达干涉测量理论与应用》", 31 March 2009 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117312746A (en) * | 2023-08-08 | 2023-12-29 | 中国科学院地理科学与资源研究所 | Rainfall erosion force calculation method based on satellite rainfall data |
| CN117312746B (en) * | 2023-08-08 | 2024-03-22 | 中国科学院地理科学与资源研究所 | Rainfall erosion force calculation method based on satellite rainfall data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104123464B (en) | Method for inversion of ground feature high elevation and number of land subsidence through high resolution InSAR timing sequence analysis | |
| CN111208512B (en) | Interferometric measurement method based on video synthetic aperture radar | |
| CN110456346B (en) | Power transmission tower inclination monitoring method based on InSAR technology | |
| CN102654576B (en) | Image registration method based on synthetic aperture radar (SAR) image and digital elevation model (DEM) data | |
| CN109031301A (en) | Alpine terrain deformation extracting method based on PSInSAR technology | |
| CN105954747A (en) | Tower foundation stability analyzing method based on three-dimensional deformation monitoring of unfavorable geologic body of power grid | |
| CN108007401A (en) | A kind of river and lake storehouse bank deformation detecting device and method based on boat-carrying InSAR platforms | |
| CN109100719B (en) | Terrain map joint mapping method based on satellite-borne SAR (synthetic aperture radar) image and optical image | |
| CN108983239B (en) | Spaceborne interference SAR digital elevation model reconstruction method | |
| CN103454636B (en) | Differential interferometric phase estimation method based on multi-pixel covariance matrixes | |
| CN109239710B (en) | Method and device for acquiring radar elevation information and computer-readable storage medium | |
| CN113960595A (en) | Surface deformation monitoring method and system | |
| Mason et al. | Data utilization in flood inundation modelling | |
| CN104316920A (en) | High-precision sea surface height extracting method of radar altimeter through small incidence angle interference | |
| CN112882030B (en) | InSAR imaging interference integrated processing method | |
| CN105824022A (en) | Method for monitoring three-dimensional deformation of unfavorable geologic body under power grid | |
| CN113281749A (en) | Time sequence InSAR high-coherence point selection method considering homogeneity | |
| CN106530334A (en) | Complex image registration method and complex image registration system of airborne interferometric synthetic aperture radar | |
| CN117523404A (en) | Method, device, terminal and storage medium for monitoring dynamic change of storage yard | |
| CN117406221A (en) | InSAR deformation monitoring method for high-resolution DEM of corner reflector | |
| CN112666070A (en) | Soil erosion calculation method | |
| CN106872979B (en) | Sea wave parameter acquisition method based on sea surface fluctuation moving target SAR image refocusing | |
| CN109324326B (en) | SAR baseline calibration method for surveying and mapping without control points | |
| CN111174689A (en) | Bridge deformation monitoring method | |
| CN113516764B (en) | Lake and reservoir underwater three-dimensional terrain simulation method and device based on digital elevation model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |