InSAR deformation monitoring suitability evaluation method and system in mountainous and hilly areas

Abstract

The invention discloses an InSAR deformation monitoring suitability evaluation method and system for mountainous and hilly areas, and relates to the field of terrain monitoring; the method comprises: gridding a target area to obtain a plurality of grid areas; collecting parameter information based on SAR data; obtaining terrain information based on DEM data; inputting parameter information and terrain information into an MDDG mathematical function model to obtain MDDG features; obtaining geometric distortion types based on parameter information, terrain information and MDDG features; calculating a normalized vegetation index based on Landsat8 optical images; and obtaining an InSAR suitability grade distribution map of the target area based on the geometric distortion type, the normalized vegetation index and the MDDG features. The invention makes the evaluation of InSAR more objective and comprehensive.

Description

Technical Field

[0001] The invention relates to the field of terrain monitoring, and in particular to an InSAR deformation monitoring suitability evaluation method and system for mountainous and hilly areas. Background Art

[0002] In recent years, the Interferometric Synthetic Aperture Radar (InSAR) technology has become well-known for its obvious advantages in large-scale monitoring and low-cost displacement detection. For the monitoring of geological hazards (such as landslides), InSAR technology may fail to be used for displacement monitoring in some cases due to complex terrain, large deformation gradients of the observed objects, and dense vegetation cover. Therefore, it is crucial to evaluate the feasibility of InSAR technology in advance when considering terrain and surface cover characteristics, especially in large-scale InSAR deformation monitoring.

[0003] In terms of evaluating the feasibility of InSAR technology, it can be divided into two categories. One category focuses on the imaging characteristics of synthetic aperture radar (SAR) satellites and the sensitivity of InSAR technology to displacement detection along the line of sight. These technical solutions attempt to evaluate the areas that cannot be detected due to low sensitivity and determine the unobservable areas due to SAR geometric distortion to avoid invalid observation of the intended target. The other category focuses on the relationship between different land cover types and ground reflectivity and wavelength, and further evaluates the number of InSAR measurable points on different land use types. Based on the collected SAR satellite parameters and ground feature parameters, the terrain visibility of InSAR and the applicability of SAR data can be judged by the above methods. However, for accurate monitoring of slope displacement, the magnitude of displacement is very important for evaluating the feasibility of InSAR technology, especially when the deformation gradient of the slope exceeds the maximum detectable deformation gradient (MDDG) of InSAR. Therefore, MDDG can be regarded as a key indicator for evaluating the feasibility of InSAR considering the SAR geometric distortion effect. In addition, surface cover characteristics can also be regarded as an important factor affecting the detectability of InSAR, especially in areas with high vegetation cover density. According to relevant technical information in the field, few technicians use MDDG to evaluate the feasibility of InSAR technology when considering terrain, let alone the suitability evaluation of InSAR in large-scale applications. In addition, for target areas with dense vegetation coverage, there is currently no technical method or solution that uses the Normalized Difference Vegetation Index (NDVI) and MDDG as evaluation indicators.

[0004] Notti et al. (Notti et al., 2014) used the hill shadow model to obtain the shadow and overlap distribution of the study area, and obtained the perspective contraction distribution of the target area with the help of the R index; and then used the land use data of the target area to evaluate the applicability and feasibility of the PSInSAR technology. This technical solution requires additional collection of land use data of the target area, and needs to be processed separately to obtain shadows and overlaps, and finally obtain the perspective contraction distribution through the R index, which is relatively cumbersome and time-consuming; when evaluating the suitability of InSAR, it is easily affected by the processing algorithm and parameter settings, so the InSAR point targets on different land use types may not be consistent with the theory.

[0005] Bonì (Bonì et al., 2020) and others also used the hill shadow model to obtain the shadow and overlap distribution and constructed the InSAR terrain visibility formula. In addition, they collected land use data in the target area and evaluated the InSAR detectability performance on different land types. Although this method takes into account the impact of different land use types on InSAR monitoring effects, its effect is not as intuitive as NDVI for densely vegetated areas. In addition, for landslide monitoring, the deformation detection gradient of InSAR is not considered, and MDDG is not introduced into the suitability evaluation of InSAR. Summary of the invention

[0006] The purpose of the present invention is to provide an InSAR mountainous and hilly area deformation monitoring suitability evaluation method and system, and to introduce MDDG and NDVI as two new evaluation indicators into the InSAR suitability evaluation, so as to make the evaluation of InSAR technology more objective and comprehensive.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] The InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas includes:

[0009] Acquire a target area, and divide the target area into grids to obtain a plurality of grid areas;

[0010] Collect SAR data, DEM data and Landsat8 optical images for each grid area;

[0011] Loading and processing the SAR data of each grid area, and collecting parameter information of each grid area; the parameter information includes the wavelength, slant range resolution, flight azimuth and incident angle of the SAR satellite data used;

[0012] Analyze the DEM data of each grid area to obtain terrain information of each grid area; the terrain information includes slope and slope direction;

[0013] Input the parameter information and terrain information of each grid area into the MDDG mathematical function model to obtain the MDDG characteristics of each grid area;

[0014] According to the parameter information, terrain information and MDDG features of each grid area, the geometric distortion type of each grid area is obtained; the geometric distortion type includes resolution, shadow, overlap and perspective contraction;

[0015] The normalized vegetation index of each grid area is calculated based on the Landsat8 optical image of each grid area;

[0016] According to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area, the InSAR suitability grade distribution map of each grid area was obtained;

[0017] The InSAR suitability grade distribution map of the target area is obtained based on the InSAR suitability grade distribution map of each grid area.

[0018] Optionally, the DEM data of each grid area is analyzed to obtain the terrain information of each grid area, including:

[0019] The slope and aspect analysis tools in ArcGIS were used to analyze the DEM data of each grid area to obtain the terrain information of each grid area.

[0020] Optionally, the normalized vegetation index of each grid area is calculated based on the Landsat8 optical image of each grid area, specifically including:

[0021] Preprocessing the Landsat8 optical image of each grid area to obtain a preprocessed Landsat8 optical image of each grid area; the preprocessing includes: splicing processing, atmospheric correction and radiation correction;

[0022] The normalized vegetation index of each grid area is calculated based on the preprocessed Landsat8 optical image of each grid area.

[0023] Optionally, the MDDG mathematical function model is:

[0024] MDD slope =λ÷2η(cosβsinθsinαcosδ+sinβsinθcosαcosδ+cosθsinδ);

[0025] Among them, MDDG sloperepresents the MDDG feature; λ represents the wavelength of the SAR satellite data; η represents the slant range resolution of the SAR image; β represents the flight azimuth of the SAR satellite; θ represents the incident angle of the SAR satellite; α represents the slope direction; and δ represents the slope.

[0026] Optionally, according to the parameter information, terrain information and MDDG features of each grid area, the geometric distortion type of each grid area is obtained, specifically including:

[0027] Based on the parameter information and terrain information of the grid area, it is judged whether the grid area meets the shadow condition, and a first judgment result is obtained; the shadow condition is that the slope of the grid area is within a first angle range and the slope of the grid area is greater than a second angle range; the first angle range is A=[0...180-β]∪[360-β...360]; the second angle range is B=[90...θ];

[0028] If the first judgment result is yes, determining that the geometric distortion type of the grid area is shadow;

[0029] If the first judgment result is no, judging whether the grid area satisfies an overlap condition based on the parameter information and terrain information of the grid area, and obtaining a second judgment result; the overlap condition is that the slope direction of the grid area is within a third angle range and the slope of the grid area is greater than θ; the third angle range is C=[180-β...360-β];

[0030] If the second judgment result is yes, determining that the geometric distortion type of the grid area is overlap;

[0031] If the second judgment result is no, judging whether the grid area satisfies a perspective shrinkage condition based on the MDDG feature of the grid area, and obtaining a third judgment result; the perspective shrinkage condition is that the MDDG feature of the grid area is less than zero;

[0032] If the third judgment result is yes, determining that the geometric distortion type of the grid area is perspective contraction;

[0033] If the third judgment result is no, determining that the geometric distortion type of the grid area is resolution;

[0034] Wherein, A represents the first angle range; B represents the second angle range; C represents the third angle range; β represents the flight azimuth of the SAR satellite; θ represents the incident angle of the SAR satellite.

[0035] Optionally, according to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area, an InSAR suitability grade distribution map of the target area is obtained, specifically including:

[0036] The suitability level of each grid area was determined based on the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area;

[0037] According to the suitability level of each grid area, the InSAR suitability level distribution map of the target area is obtained.

[0038] Optionally, the suitability level of each grid area is determined according to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area, specifically including:

[0039] Determine whether the geometric distortion type, the normalized vegetation index, and the MDDG feature of the grid area meet the excellent adaptability level condition, and obtain a fourth judgment result; the excellent adaptability level condition is that the geometric distortion type is resolution, the normalized vegetation index is within the first index range, and the MDDG feature is within the first feature range; the first index range is 0.1 to 0.3; the first feature range is 8 to 12;

[0040] If the fourth judgment result is yes, determining that the suitability level of the grid area is excellent;

[0041] If the fourth judgment result is no, then determine whether the geometric distortion type, the normalized vegetation index, and the MDDG feature of the grid area meet the good adaptability level condition to obtain a fifth judgment result; the good adaptability level condition is that the geometric distortion type is resolution, the normalized vegetation index is within the second index range, and the MDDG feature is within the second feature range; the second index range is 0.3 to 0.5; the second feature range is 4 to 8;

[0042] If the fifth judgment result is yes, determining that the suitability level of the grid area is good;

[0043] If the fifth judgment result is no, then determine whether the geometric distortion type, the normalized vegetation index, and the MDDG feature of the grid area meet the medium adaptability level condition to obtain a sixth judgment result; the medium adaptability level condition is that the geometric distortion type is resolution, the normalized vegetation index is within the third index range, and the MDDG feature is within the third feature range; the third index range is 0.5 to 0.7; the third feature range is 0 to 4;

[0044] If the sixth judgment result is yes, determining that the suitability level of the grid area is medium;

[0045] If the sixth judgment result is no, then determine whether the geometric distortion type, the normalized vegetation index, and the MDDG feature of the grid area meet the conditions of the poor adaptability level, and obtain a seventh judgment result; the poor adaptability level condition is that the geometric distortion type is perspective contraction, the normalized vegetation index is within the fourth index range, and the MDDG feature is within the fourth feature range; the fourth index range is 0.7 to 1; the fourth feature range is less than 0;

[0046] If the seventh judgment result is yes, determining that the suitability level of the grid area is poor;

[0047] If the seventh judgment result is no, then the suitability level of the grid area is determined to be none.

[0048] An InSAR deformation monitoring suitability evaluation system for mountainous and hilly areas, the InSAR deformation monitoring suitability evaluation system for mountainous and hilly areas comprising:

[0049] A division module is used to obtain a target area and divide the target area into grids to obtain multiple grid areas;

[0050] The collection module is used to collect SAR data, DEM data and Landsat8 optical images in each grid area;

[0051] A processing module is used to load and process the SAR data of each grid area and collect parameter information of each grid area; the parameter information includes the wavelength, slant range resolution, flight azimuth and incident angle of the SAR satellite data used;

[0052] An analysis module is used to analyze the DEM data of each grid area to obtain terrain information of each grid area; the terrain information includes slope and slope direction;

[0053] The MDDG mathematical function model module is used to input the parameter information and terrain information of each grid area into the MDDG mathematical function model to obtain the MDDG characteristics of each grid area;

[0054] A geometric distortion distribution module is used to obtain the geometric distortion type of each grid area according to the parameter information, terrain information and MDDG features of each grid area; the geometric distortion types include resolution, shadow, overlap and perspective contraction;

[0055] The NDVI module is used to calculate the NDVI of each grid area based on the Landsat8 optical image of each grid area;

[0056] The InSAR suitability grade distribution map determination module of the grid area is used to obtain the InSAR suitability grade distribution map of each grid area according to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area;

[0057] The module for determining the InSAR suitability grade distribution map of the target area is used to obtain the InSAR suitability grade distribution map of the target area according to the InSAR suitability grade distribution map of each grid area.

[0058] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas when executing the computer program.

[0059] A computer-readable storage medium stores a computer program, which, when executed, implements the above-mentioned InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas.

[0060] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0061] The present invention obtains the MDDG characteristics and geometric distortion types of each grid area through the SAR data and DEM data of each grid area; then the normalized vegetation index of each grid area is calculated based on the Landsat8 optical image of each grid area, and then multiple grid areas are combined to obtain the InSAR suitability grade distribution map of the target area. The present invention introduces MDDG and NDVI as two new evaluation indicators into the suitability evaluation of InSAR, making the evaluation of InSAR technology more objective and comprehensive. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 Flow chart of the InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] The purpose of the present invention is to provide an InSAR mountainous and hilly area deformation monitoring suitability evaluation method and system, and to introduce MDDG and NDVI as two new evaluation indicators into the InSAR suitability evaluation, so as to make the evaluation of InSAR technology more objective and comprehensive.

[0066] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] like Figure 1 As shown, the InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas of the present invention comprises:

[0068] Step 101: Acquire a target area, and divide the target area into grids to obtain a plurality of grid areas.

[0069] With the help of the fishnet tool in ArcGIS, the target area is gridded to obtain multiple grid areas.

[0070] Step 102: Collect SAR data, DEM data and Landsat8 optical images for each grid area.

[0071] Due to the differences in imaging methods and widths between SAR and Landsat8, it is necessary to check whether the acquired SAR images and Landsat8 images completely cover the grid area; collect DEM data of the grid area. Since the free and open DEM is published in grid form, splicing processing is required when necessary.

[0072] As an embodiment, Sentinel-1 satellite ascending orbit SAR data and SRTM-DEM with a spatial resolution of 30 m were obtained.

[0073] Step 103: Load and process the SAR data of each grid area, and collect parameter information of each grid area; the parameter information includes the wavelength, slant range resolution, flight azimuth and incident angle of the SAR satellite data used.

[0074] With the help of SAR processing software, the SAR data of each grid area is loaded and processed, and the parameter information of each grid area is collected.

[0075] Step 104: Analyze the DEM data of each grid area to obtain terrain information of each grid area; the terrain information includes slope and slope direction.

[0076] Step 105: Input the parameter information and terrain information of each grid area into the MDDG mathematical function model to obtain the MDDG features of each grid area.

[0077] The MDDG mathematical function model involves a total of six parameter information, namely: SAR satellite wavelength, slant range resolution, incident angle, flight azimuth, slope and slope direction. Therefore, with the help of the MDDG model that takes terrain into consideration, the MDDG characteristics of each grid area can be successfully obtained.

[0078] Step 106: According to the parameter information, terrain information and MDDG features of each grid area, the geometric distortion type of each grid area is obtained; the geometric distortion type includes resolution, shadow, overlap and perspective contraction.

[0079] Step 107: Calculate the normalized vegetation index of each grid area based on the Landsat8 optical image of each grid area.

[0080] Since only the interference of vegetation on InSAR signals is considered in the present invention, only [0.1, 1] is considered when taking the value of the normalized vegetation index, and then further classification is performed.

[0081] Step 108: Obtain an InSAR suitability grade distribution map of each grid area according to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area.

[0082] Step 109: Obtain the InSAR suitability grade distribution map of the target area according to the InSAR suitability grade distribution map of each grid area.

[0083] As a specific embodiment, the DEM data of each grid area is analyzed to obtain the terrain information of each grid area, which specifically includes:

[0084] The slope and aspect analysis tools in ArcGIS were used to analyze the DEM data of each grid area to obtain the terrain information of each grid area.

[0085] As a specific embodiment, the normalized vegetation index of each grid area is calculated based on the Landsat8 optical image of each grid area, specifically including:

[0086] The Landsat8 optical image of each grid area is preprocessed to obtain the preprocessed Landsat8 optical image of each grid area; the preprocessing includes: splicing processing, atmospheric correction and radiation correction. The main purpose is to obtain high-precision NDVI values ​​and their distribution.

[0087] The normalized vegetation index of each grid area is calculated based on the preprocessed Landsat8 optical image of each grid area.

[0088] As a specific embodiment, the MDDG mathematical function model is:

[0089] MDD slope =λ÷2η(cosβsinθsinαcosδ+sinβsinθcosαcosδ+cosθsinδ).

[0090] Among them, MDDG slope represents the MDDG feature; λ represents the wavelength of the SAR satellite data; η represents the slant range resolution of the SAR image; β represents the flight azimuth of the SAR satellite; θ represents the incident angle of the SAR satellite; α represents the slope direction; and δ represents the slope.

[0091] As a specific embodiment, according to the parameter information, terrain information and MDDG features of each grid area, the geometric distortion type of each grid area is obtained, which specifically includes:

[0092] Based on the parameter information and terrain information of the grid area, it is determined whether the grid area meets the shadow condition to obtain a first judgment result; the shadow condition is that the slope direction of the grid area is within a first angle range and the slope of the grid area is greater than a second angle range; the first angle range is A=[0...180-β]∪[360-β...360]; the second angle range is B=[90...θ].

[0093] If the first judgment result is yes, determining that the geometric distortion type of the grid area is shadow;

[0094] If the first judgment result is no, then based on the parameter information and terrain information of the grid area, it is judged whether the grid area meets the overlapping condition to obtain a second judgment result; the overlapping condition is that the slope direction of the grid area is within a third angle range and the slope of the grid area is greater than θ; the third angle range is C = [180-β...360-β].

[0095] If the second judgment result is yes, it is determined that the geometric distortion type of the grid area is overlapping.

[0096] If the second judgment result is no, then based on the MDDG feature of the grid area, it is judged whether the grid area meets the perspective shrinkage condition to obtain a third judgment result; the perspective shrinkage condition is that the MDDG feature of the grid area is less than zero.

[0097] If the third judgment result is yes, it is determined that the geometric distortion type of the grid area is perspective contraction.

[0098] If the third judgment result is no, it is determined that the geometric distortion type of the grid area is resolution.

[0099] Wherein, A represents the first angle range; B represents the second angle range; C represents the third angle range; β represents the flight azimuth of the SAR satellite; θ represents the incident angle of the SAR satellite.

[0100] As a specific embodiment, according to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area, an InSAR suitability grade distribution map of the target area is obtained, which specifically includes:

[0101] The suitability level of each grid area was determined based on its geometric distortion type, normalized difference vegetation index, and MDDG characteristics.

[0102] According to the suitability level of each grid area, the InSAR suitability level distribution map of the target area is obtained.

[0103] As a specific embodiment, the suitability level of each grid area is determined according to the geometric distortion type, normalized difference vegetation index and MDDG feature of each grid area, specifically including:

[0104] Determine whether the geometric distortion type, normalized vegetation index and MDDG feature of the grid area meet the excellent adaptability level conditions to obtain a fourth judgment result; the excellent adaptability level conditions are that the geometric distortion type is resolution, the normalized vegetation index is within the first index range and the MDDG feature is within the first feature range; the first index range is 0.1 to 0.3; the first feature range is 8 to 12.

[0105] If the fourth judgment result is yes, the suitability level of the grid area is determined to be excellent.

[0106] If the fourth judgment result is no, then judge whether the geometric distortion type, normalized vegetation index and MDDG feature of the grid area meet the good adaptability level conditions to obtain the fifth judgment result; the good adaptability level conditions are that the geometric distortion type is resolution, the normalized vegetation index is within the second index range and the MDDG feature is within the second feature range; the second index range is 0.3 to 0.5; the second feature range is 4 to 8.

[0107] If the fifth judgment result is yes, it is determined that the suitability level of the grid area is good.

[0108] If the fifth judgment result is no, then judge whether the geometric distortion type, normalized vegetation index and MDDG feature of the grid area meet the medium adaptability level conditions to obtain the sixth judgment result; the medium adaptability level conditions are that the geometric distortion type is resolution, the normalized vegetation index is within the third index range and the MDDG feature is within the third feature range; the third index range is 0.5 to 0.7; the third feature range is 0 to 4.

[0109] If the sixth judgment result is yes, it is determined that the suitability level of the grid area is medium.

[0110] If the sixth judgment result is no, then determine whether the geometric distortion type, normalized vegetation index and MDDG feature of the grid area meet the conditions of the poor adaptability level to obtain the seventh judgment result; the poor adaptability level conditions are that the geometric distortion type is perspective contraction, the normalized vegetation index is within the fourth index range and the MDDG feature is within the fourth feature range; the fourth index range is 0.7 to 1; the fourth feature range is less than 0.

[0111] If the seventh judgment result is yes, the suitability level of the grid area is determined to be poor.

[0112] If the seventh judgment result is no, then the suitability level of the grid area is determined to be none.

[0113] The present invention considers three indicators to evaluate the suitability of InSAR, wherein the suitability can be divided into five levels, namely, excellent suitability, good suitability, medium suitability, poor suitability and no suitability. The classification criteria can be referred to in Table 1.

[0114] Table 1 InSAR suitability table

[0115]

[0116] Note: HR stands for High Resolution, FS stands for Foreshortening, SD stands for Shadowing and LO stands for Overlay.

[0117] Example 2

[0118] An InSAR deformation monitoring suitability evaluation system for mountainous and hilly areas, the InSAR deformation monitoring suitability evaluation system for mountainous and hilly areas comprising:

[0119] The division module is used to obtain the target area, divide the target area into grids, and obtain multiple grid areas.

[0120] The collection module is used to collect SAR data, DEM data and Landsat8 optical images in each grid area.

[0121] The processing module is used to load and process the SAR data of each grid area and collect parameter information of each grid area; the parameter information includes the wavelength, slant range resolution, flight azimuth and incident angle of the SAR satellite data used.

[0122] The analysis module is used to analyze the DEM data of each grid area to obtain the terrain information of each grid area; the terrain information includes slope and slope direction.

[0123] The MDDG mathematical function model module is used to input the parameter information and terrain information of each grid area into the MDDG mathematical function model to obtain the MDDG characteristics of each grid area.

[0124] The geometric distortion distribution module is used to obtain the geometric distortion type of each grid area according to the parameter information, terrain information and MDDG features of each grid area; the geometric distortion types include resolution, shadow, overlap and perspective contraction.

[0125] The NDVI module is used to calculate the NDVI of each grid area based on the Landsat8 optical image of each grid area.

[0126] The InSAR suitability grade distribution map determination module of the grid area is used to obtain the InSAR suitability grade distribution map of each grid area according to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area.

[0127] The module for determining the InSAR suitability grade distribution map of the target area is used to obtain the InSAR suitability grade distribution map of the target area according to the InSAR suitability grade distribution map of each grid area.

[0128] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas as described in Example 1 when executing the computer program.

[0129] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas as described in Example 1 is implemented.

[0130] The present invention obtains the MDDG features and geometric distortion types of each grid area through the Synthetic Aperture Radar (SAR) data and Digital Elevation Model (DEM) data of each grid area; then the normalized vegetation index of each grid area is calculated based on the Landsat8 optical image of each grid area, and then multiple grid areas are combined to obtain the InSAR suitability grade distribution map of the target area. The present invention introduces the Maximum detectable deformation gradients (MDDG) and the Normalized Difference Vegetation Index (NDVI) as two new evaluation indicators into the suitability evaluation of InSAR, making the evaluation of InSAR technology more objective and comprehensive.

[0131] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0132] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. InSAR deformation monitoring suitability evaluation method in mountainous and hilly areas, It is characterized in that The InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas includes: Acquire a target area, and divide the target area into grids to obtain a plurality of grid areas; Collect SAR data, DEM data and Landsat8 optical images for each grid area; Loading and processing the SAR data of each grid area, and collecting parameter information of each grid area; the parameter information includes the wavelength, slant range resolution, flight azimuth and incident angle of the SAR satellite data used; Analyze the DEM data of each grid area to obtain terrain information of each grid area; the terrain information includes slope and slope direction; Input the parameter information and terrain information of each grid area into the MDDG mathematical function model to obtain the MDDG characteristics of each grid area; According to the parameter information, terrain information and MDDG features of each grid area, the geometric distortion type of each grid area is obtained; the geometric distortion type includes resolution, shadow, overlap and perspective contraction; The normalized vegetation index of each grid area is calculated based on the Landsat8 optical image of each grid area; According to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area, the InSAR suitability grade distribution map of each grid area was obtained; The InSAR suitability grade distribution map of the target area is obtained based on the InSAR suitability grade distribution map of each grid area.

2. The InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas according to claim 1, It is characterized in that Analyze the DEM data of each grid area to obtain the terrain information of each grid area, including: The slope and aspect analysis tools in ArcGIS were used to analyze the DEM data of each grid area to obtain the terrain information of each grid area.

3. The InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas according to claim 1, It is characterized in that The normalized vegetation index of each grid area is calculated based on the Landsat8 optical image of each grid area, including: Preprocessing the Landsat8 optical image of each grid area to obtain a preprocessed Landsat8 optical image of each grid area; the preprocessing includes: splicing processing, atmospheric correction and radiation correction; The normalized vegetation index of each grid area is calculated based on the preprocessed Landsat8 optical image of each grid area.

4. The InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas according to claim 1, It is characterized in that The MDDG mathematical function model is: MDDG slope =λ÷2η(cosβsinθsinαcosδ+sinβsinθcosαcosδ+cosθsinδ); Among them, MDDG slope represents the MDDG feature; λ represents the wavelength of the SAR satellite data; η represents the slant range resolution of the SAR image; β represents the flight azimuth of the SAR satellite; θ represents the incident angle of the SAR satellite; α represents the slope direction; and δ represents the slope.

5. The InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas according to claim 1, It is characterized in that According to the parameter information, terrain information and MDDG features of each grid area, the geometric distortion type of each grid area is obtained, including: Based on the parameter information and terrain information of the grid area, it is judged whether the grid area meets the shadow condition, and a first judgment result is obtained; the shadow condition is that the slope of the grid area is within a first angle range and the slope of the grid area is greater than a second angle range; the first angle range is A=[0...180-β]∪[360-β...360]; the second angle range is B=[90...θ]; If the first judgment result is yes, determining that the geometric distortion type of the grid area is shadow; If the first judgment result is no, judging whether the grid area satisfies an overlap condition based on the parameter information and terrain information of the grid area, and obtaining a second judgment result; the overlap condition is that the slope direction of the grid area is within a third angle range and the slope of the grid area is greater than θ; the third angle range is C=[180-β...360-β]; If the second judgment result is yes, determining that the geometric distortion type of the grid area is overlap; If the second judgment result is no, judging whether the grid area satisfies a perspective shrinkage condition based on the MDDG feature of the grid area, and obtaining a third judgment result; the perspective shrinkage condition is that the MDDG feature of the grid area is less than zero; If the third judgment result is yes, determining that the geometric distortion type of the grid area is perspective contraction; If the third judgment result is no, determining that the geometric distortion type of the grid area is resolution; Wherein, A represents the first angle range; B represents the second angle range; C represents the third angle range; β represents the flight azimuth of the SAR satellite; θ represents the incident angle of the SAR satellite.

6. The InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas according to claim 1, It is characterized in that According to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area, the InSAR suitability grade distribution map of the target area is obtained, including: The suitability level of each grid area was determined based on the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area; According to the suitability level of each grid area, the InSAR suitability level distribution map of the target area is obtained.

7. The InSAR deformation monitoring suitability evaluation method for mountainous and hilly areas according to claim 6, It is characterized in that According to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area, the suitability level of each grid area is determined, including: Determine whether the geometric distortion type, the normalized vegetation index, and the MDDG feature of the grid area meet the excellent adaptability level condition, and obtain a fourth judgment result; the excellent adaptability level condition is that the geometric distortion type is resolution, the normalized vegetation index is within the first index range, and the MDDG feature is within the first feature range; the first index range is 0.1 to 0.3; the first feature range is 8 to 12; If the fourth judgment result is yes, determining that the suitability level of the grid area is excellent; If the fourth judgment result is no, then determine whether the geometric distortion type, the normalized vegetation index, and the MDDG feature of the grid area meet the good adaptability level condition to obtain a fifth judgment result; the good adaptability level condition is that the geometric distortion type is resolution, the normalized vegetation index is within the second index range, and the MDDG feature is within the second feature range; the second index range is 0.3 to 0.5; the second feature range is 4 to 8; If the fifth judgment result is yes, determining that the suitability level of the grid area is good; If the fifth judgment result is no, then determine whether the geometric distortion type, the normalized vegetation index, and the MDDG feature of the grid area meet the medium adaptability level condition to obtain a sixth judgment result; the medium adaptability level condition is that the geometric distortion type is resolution, the normalized vegetation index is within the third index range, and the MDDG feature is within the third feature range; the third index range is 0.5 to 0.7; the third feature range is 0 to 4; If the sixth judgment result is yes, determining that the suitability level of the grid area is medium; If the sixth judgment result is no, then determine whether the geometric distortion type, the normalized vegetation index, and the MDDG feature of the grid area meet the conditions of the poor adaptability level, and obtain a seventh judgment result; the poor adaptability level condition is that the geometric distortion type is perspective contraction, the normalized vegetation index is within the fourth index range, and the MDDG feature is within the fourth feature range; the fourth index range is 0.7 to 1; the fourth feature range is less than 0; If the seventh judgment result is yes, determining that the suitability level of the grid area is poor; If the seventh judgment result is no, then the suitability level of the grid area is determined to be none.

8. An InSAR deformation monitoring suitability evaluation system for mountainous and hilly areas, It is characterized in that The InSAR deformation monitoring suitability evaluation system for mountainous and hilly areas includes: A division module is used to obtain a target area and divide the target area into grids to obtain multiple grid areas; The collection module is used to collect SAR data, DEM data and Landsat8 optical images in each grid area; A processing module is used to load and process the SAR data of each grid area and collect parameter information of each grid area; the parameter information includes the wavelength, slant range resolution, flight azimuth and incident angle of the SAR satellite data used; An analysis module is used to analyze the DEM data of each grid area to obtain terrain information of each grid area; the terrain information includes slope and slope direction; The MDDG mathematical function model module is used to input the parameter information and terrain information of each grid area into the MDDG mathematical function model to obtain the MDDG characteristics of each grid area; A geometric distortion distribution module is used to obtain the geometric distortion type of each grid area according to the parameter information, terrain information and MDDG features of each grid area; the geometric distortion types include resolution, shadow, overlap and perspective contraction; The NDVI module is used to calculate the NDVI of each grid area based on the Landsat8 optical image of each grid area; The InSAR suitability grade distribution map determination module of the grid area is used to obtain the InSAR suitability grade distribution map of each grid area according to the geometric distortion type, normalized difference vegetation index and MDDG characteristics of each grid area; The module for determining the InSAR suitability grade distribution map of the target area is used to obtain the InSAR suitability grade distribution map of the target area according to the InSAR suitability grade distribution map of each grid area.

9. An electronic device, It is characterized in that The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, It is characterized in that The storage medium stores a computer program, which implements the method according to any one of claims 1 to 7 when executed.

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