Landslide spatio-temporal evolution law analysis method and system based on image accumulation difference degree

Abstract

The invention discloses a landslide spatio-temporal evolution law analysis method and system based on an image accumulation difference degree, and relates to the technical field of landslide susceptibility evaluation, and the method comprises the steps: obtaining DEM data of a landslide evolution process of a slope deformation region through an unmanned plane remote sensing technology, carrying out the preprocessing of the DEM data, and reading a pixel value of the preprocessed DEM data, acquiring elevation information of each site, dividing the elevation information into different grades to generate a remote sensing image set, and uniformly dividing the remote sensing image at each time point into a plurality of areas; using a mean Hash algorithm to obtain the Hash value of the remote sensing image of each region at the adjacent time points, and calculating the similarity of the remote sensing image of each region at the adjacent time points; calculating and drawing an accumulated difference degree-time curve according to the similarity in combination with a period-by-period difference degree accumulation method; and drawing a regional cumulative difference degree-time thermodynamic diagram to determine a deformation region landslide evolution law. According to the method, the influence of truncation errors is avoided, and the spatial-temporal evolution law of the landslide area is quantitatively described.

Description

Technical Field

[0001] The invention relates to the technical field of landslide susceptibility assessment, and in particular to a landslide spatiotemporal evolution law analysis method and system based on image cumulative difference. Background Art

[0002] Landslide is the second largest geological disaster after earthquakes. It has been troubling people's lives and property safety and has attracted widespread attention from experts and scholars. With the continuous development and popularization of monitoring technology and equipment, the application of air, space and ground monitoring systems in the fields of slope dynamic stability monitoring and landslide early warning has become increasingly widespread and has made significant progress. However, the evolution process and laws of landslides are affected by a variety of uncertain and dynamically changing factors, including geological structure, hydrological conditions and climate change. The interaction of multiple factors jointly determines the occurrence, development and final form of landslides. Many scholars have done a lot of research on slope deformation monitoring. The monitoring content has gradually become comprehensive, and the monitoring methods have also become diverse and integrated. However, the evolution mechanism of landslides is difficult to accurately grasp.

[0003] Due to the limitation of cost and site conditions, slope deformation monitoring usually uses GNSS, radar and other means to monitor and analyze spatial surface displacement, while slope monitoring drone remote sensing technology is mostly used to provide spatiotemporal information. Both methods obtain the evolution of landslides by processing spatial data, which are limited by technical limitations, insufficient data processing tools, truncation errors in the data processing process, and insufficient understanding of the data source. Therefore, it is urgent to propose a landslide spatiotemporal evolution law analysis method based on the cumulative difference between remote sensing and OpenCV images from the perspective of image pixels to fill the gap in the study of landslide evolution laws. Summary of the invention

[0004] The purpose of the present invention is to provide a landslide spatiotemporal evolution law analysis method and system based on image cumulative difference, which eliminates the influence of truncation error in data processing of existing slope monitoring systems and quantitatively describes the spatiotemporal evolution law of landslide areas.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The landslide spatiotemporal evolution law analysis method based on image cumulative difference includes the following steps:

[0007] Step 1: Obtain DEM data of the landslide evolution process in the slope deformation area through UAV remote sensing technology;

[0008] Step 2: Preprocess the DEM data, obtain the elevation information of each location by reading the pixel values ​​in the preprocessed DEM data, divide the elevation information into different levels, and assign different colors to each level to generate a remote sensing image set;

[0009] Step 3: Use a refined uniform segmentation method to evenly divide the remote sensing images in the remote sensing image set at each time point into multiple regions;

[0010] Step 4: Use the mean hash algorithm to obtain the hash values ​​of the remote sensing images of each region at adjacent time points, and calculate the similarity of the remote sensing images of each region at adjacent time points;

[0011] Step 5: Calculate the difference between the remote sensing images of each region according to the relative degree, and then use the daily cumulative difference method to calculate the cumulative difference at each time point;

[0012] Step 6: Draw a regional cumulative difference degree-time thermodynamic map, and determine the landslide evolution law in the deformation zone based on the thermodynamic map.

[0013] Furthermore, in step 4, the specific calculation formula of the similarity is as follows:

[0014]

[0015] In the formula, S i is the similarity of remote sensing images of each region at adjacent time points; l is the total number of bits of the hash value; H1 and H2 are the hash values ​​of the initial image and the object respectively; H 1,i and H 2,i are the i-th values ​​of H1 and H2 respectively, i ≤ the total number of digits of the hash value.

[0016] Furthermore, in step 2, the preprocessing specifically includes: obtaining an original LiDAR point cloud data map based on the DEM data, removing possible outliers and noise points, and generating preprocessed DEM data.

[0017] Furthermore, the step 5, based on the similarity, combined with the time period cumulative difference method, calculates and draws a cumulative difference-time curve, specifically including:

[0018] S1, by subtracting the cumulative difference at each time point from the similarity of the initial image, the similarity between each time point and the initial image is obtained; finally, based on the similarity between each time point and the initial image, the cumulative difference CV is calculated;

[0019] S2. Calculate and draw the cumulative difference-time curve. The specific calculation formula is as follows:

[0020]

[0021] Where D i is the difference between remote sensing images of adjacent dates; S0 is the similarity between the first day image and the initial date image; (CV-T)i It is the cumulative difference degree-time of the remote sensing images on each date.

[0022] Furthermore, in step 6, determining the landslide evolution law of the deformation zone according to the thermal map specifically includes:

[0023] If the rate of change of the cumulative difference degree-time in the upper part of the deformation area in the thermal map exceeds the rate of change in the lower part, the landslide mode is push type;

[0024] On the contrary, if the rate of change of the upper cumulative difference degree-time is lower than that of the lower part, the landslide mode is traction type.

[0025] The present invention also provides a landslide spatiotemporal evolution law analysis system based on image cumulative difference, which is used to execute the landslide spatiotemporal evolution law analysis method based on image cumulative difference, comprising:

[0026] The data acquisition module is used to obtain DEM data of the landslide evolution process in the slope deformation area through UAV remote sensing technology;

[0027] A preprocessing module is used to preprocess the DEM data, obtain the elevation information of each location by reading the pixel values ​​in the preprocessed DEM data, divide the elevation information into different levels, and assign different colors to each level to generate a remote sensing image set;

[0028] A segmentation module, for evenly dividing the remote sensing images in the remote sensing image set at each time point into a plurality of regions using a refined even segmentation method;

[0029] The similarity calculation module is used to obtain the hash values ​​of the remote sensing images of each region at adjacent time points by using the mean hash algorithm, and calculate the similarity of the remote sensing images of each region at adjacent time points;

[0030] A cumulative difference calculation module is used to calculate the difference between the remote sensing images of each region according to the relative degree, and then use a daily cumulative difference method to calculate the cumulative difference at each time point;

[0031] The determination module is used to draw a regional cumulative difference degree-time thermal map, and determine the landslide evolution law in the deformation zone according to the thermal map.

[0032] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the method and system for analyzing the spatiotemporal evolution law of landslides based on the cumulative difference of images provided by the present invention, by using the high-resolution spatiotemporal remote sensing images of slope survey drones, coupling OpenCV technology (steps 3 and 4), and using the comparative image similarity method to analyze and process the remote sensing images every day, and proposing the concept of Cumulative Variance-Time (CV-T), starting from the image pixel perspective, eliminating the influence of truncation errors on the existing slope monitoring system during data processing, quantitatively describing the spatiotemporal evolution law of the landslide area, and providing a new perspective for the study of landslide evolution laws. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] 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 labor.

[0034] Figure 1 A flowchart of generating remote sensing images by processing DEM data in an embodiment of the present invention;

[0035] Figure 2 Embodiment 2 of the present invention n ×2 n Schematic diagram of image division when the refined uniform segmentation method n=1;

[0036] Figure 3 This is a schematic diagram of a remote sensing image set from May 9 to May 15 according to an embodiment of the present invention;

[0037] Figure 4 The embodiment of the present invention is image 2 n ×2 n Schematic diagram of image division when the refined uniform segmentation method n=2;

[0038] Figure 5 This is a cumulative difference degree-time (CV-T) thermodynamic diagram in an embodiment of the present invention;

[0039] Figure 6 The figure is a flow chart of a method for analyzing the spatiotemporal evolution law of landslides based on the cumulative difference of images in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] 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.

[0041] The purpose of the present invention is to provide a landslide spatiotemporal evolution law analysis method and system based on image cumulative difference, which eliminates the influence of truncation error in data processing of existing slope monitoring systems and quantitatively describes the spatiotemporal evolution law of landslide areas.

[0042] 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.

[0043] like Figure 1-6 As shown, the method for analyzing the spatiotemporal evolution law of landslides based on the cumulative difference of images provided in the embodiment of the present invention comprises the following steps:

[0044] Step 1: Obtain DEM data of the landslide evolution process in the slope deformation area through UAV remote sensing technology;

[0045] Step 2: Preprocess the DEM data, obtain the elevation information of each location by reading the pixel values ​​in the preprocessed DEM data, divide the elevation information into different levels, and assign different colors to each level to generate a remote sensing image set, such as Figure 3 As shown, in the figure, (a)-(g) represent the remote sensing image sets from May 09 to May 15 respectively;

[0046] Step 3: Use 2 n ×2 n The refined uniform segmentation method (n=2) evenly divides the remote sensing images in the remote sensing image set at each time point into multiple regions, such as Figure 4 The specific calculation results of the similarity of the remote sensing images of each adjacent date area are shown in Table 1:

[0047] Table 1 Similarity of remote sensing images in different time periods and regions

[0048]

[0049]

[0050] Step 4: Use the mean hash algorithm to obtain the hash values ​​of the remote sensing images of each region at adjacent time points, and calculate the similarity of the remote sensing images of each region at adjacent time points. The specific calculation formula of the similarity is as follows:

[0051]

[0052] In the formula, S i is the similarity of remote sensing images of each region at adjacent time points; l is the total number of bits of the hash value; H1 and H2 are the hash values ​​of the initial image and the object respectively; H 1,i and H 2,i are the i-th values ​​of H1 and H2, respectively, representing the grayscale value of the i-th pixel after the two images are reduced, i≤the total number of bits of the hash value; δ i is an indicator variable used to indicate whether the grayscale value of the i-th pixel in the two images is different. If different (i.e. H 1,i ≠H 2,i ), then δ i =1; if the same (i.e. H 1,i =H 2,i ), then δ i =0.

[0053] Step 5: Based on the relative degree and the cumulative difference method for each period, calculate and draw a cumulative difference-time curve. The specific calculation formula is as follows:

[0054]

[0055] Where D i is the difference between remote sensing images of adjacent dates; S0 is the similarity between the first day image and the initial date image; (CV-T) i It is the cumulative difference degree-time of the remote sensing images on each date.

[0056] The specific calculation results of the cumulative difference degree-time of the remote sensing images of each region are shown in Table 2:

[0057] Table 2 Cumulative difference of remote sensing images in each region at each time period - time

[0058]

[0059]

[0060] Step 6: Draw a regional cumulative difference-time thermodynamic map, and determine the landslide evolution law in the deformation zone based on the thermodynamic map, such as Figure 5 As shown, the heat map of this case satisfies If this holds true for all i and this situation persists for a period of time, the upper area can be considered as the early start-up area and judged to be a push-type landslide.

[0061] In this embodiment, a method of accumulating the differences day by day is adopted to calculate the cumulative differences at each time point, specifically including: subtracting the cumulative differences at each time point from the similarity of the initial image to obtain the similarity between each time point and the initial image; finally, based on the similarity between each time point and the initial image, the cumulative differences are calculated.

[0062] In this embodiment, determining the landslide evolution law of the deformation zone according to the thermal map specifically includes:

[0063] If the rate of change of the cumulative difference degree-time in the upper deformation area in the thermal map exceeds the rate of change in the lower part, the landslide mode is a push type; conversely, if the rate of change of the cumulative difference degree-time in the upper part is lower than the rate of change in the lower part, the landslide mode is a traction type.

[0064] Example 2

[0065] This embodiment provides a landslide spatiotemporal evolution law analysis system based on image cumulative difference, which is used to execute the landslide spatiotemporal evolution law analysis method based on image cumulative difference described in Embodiment 1, including:

[0066] The data acquisition module is used to obtain DEM data of the landslide evolution process in the slope deformation area through UAV remote sensing technology;

[0067] A preprocessing module is used to preprocess the DEM data, obtain the elevation information of each location by reading the pixel values ​​in the preprocessed DEM data, divide the elevation information into different levels, and assign different colors to each level to generate a remote sensing image set;

[0068] A segmentation module, for evenly dividing the remote sensing images in the remote sensing image set at each time point into a plurality of regions using a refined even segmentation method;

[0069] The similarity calculation module is used to obtain the hash values ​​of the remote sensing images of each region at adjacent time points by using the mean hash algorithm, and calculate the similarity of the remote sensing images of each region at adjacent time points;

[0070] A cumulative difference calculation module is used to calculate the difference between the remote sensing images of each region according to the relative degree, and then use a daily cumulative difference method to calculate the cumulative difference at each time point;

[0071] The determination module is used to draw a regional cumulative difference degree-time thermal map, and determine the landslide evolution law in the deformation zone according to the thermal map.

[0072] In summary, the method and system for analyzing the spatiotemporal evolution of landslides based on cumulative image differences provided by the present invention use high-resolution spatiotemporal UAV remote sensing images of slope surveys, couple with OpenCV technology, and adopt the method of comparing image similarity to analyze and process the remote sensing images every day. The concept of Cumulative Variance-Time (CV-T) is proposed, and from the perspective of image pixels, the influence of truncation errors on the data processing of the existing slope monitoring system is eliminated, and the spatiotemporal evolution law of the landslide area is quantitatively described, providing a new perspective for the study of the evolution law of open-pit mine landslides.

[0073] The remaining technical features in this embodiment can be flexibly selected by those skilled in the art according to actual conditions to meet different specific practical needs. However, it is obvious to those of ordinary skill in the art that it is not necessary to adopt these specific details to implement the present invention. In other examples, in order to avoid confusing the present invention, the well-known composition and structure are not specifically described, and they are all within the technical protection scope defined by the technical solution claimed for protection in the claims of the present invention.

[0074] Modifications and changes made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the scope of protection of the claims attached to the present invention. In the above description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details are not necessary to practice the present invention. In other examples, in order to avoid confusing the present invention, well-known technologies, such as specific construction details, operating conditions and other technical conditions, are not specifically described.

[0075] 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. A landslide spatiotemporal evolution law analysis method based on image cumulative difference, characterized in that: The following steps are involved: Step 1: Obtain DEM data of the landslide evolution process in the slope deformation area through UAV remote sensing technology; Step 2: Preprocess the DEM data, obtain the elevation information of each location by reading the pixel values ​​in the preprocessed DEM data, divide the elevation information into different levels, and assign different colors to each level to generate a remote sensing image set; Step 3: Use a refined uniform segmentation method to evenly divide the remote sensing images in the remote sensing image set at each time point into multiple regions; Step 4: Use the mean hash algorithm to obtain the hash values ​​of the remote sensing images of each region at adjacent time points, and calculate the similarity of the remote sensing images of each region at adjacent time points; Step 5: Based on the similarity, combined with the cumulative difference method for each period, calculate and draw the cumulative difference-time curve; Step 6: Draw a regional cumulative difference degree-time thermodynamic map, and determine the landslide evolution law in the deformation zone based on the thermodynamic map.

2. The method for analyzing the spatiotemporal evolution law of landslide based on image cumulative difference according to claim 1 is characterized in that: In step 4, the specific calculation formula of the similarity is as follows: In the formula, S i is the similarity of remote sensing images of each region at adjacent time points; l is the total number of bits of the hash value; H1 and H2 are the hash values ​​of the initial image and the object respectively; H 1,i and H 2,i are the i-th values ​​of H1 and H2 respectively, i≤the total number of digits of the hash value;δ i Used to indicate whether the grayscale value of the i-th pixel in the two images is different.

3. The method for analyzing the spatiotemporal evolution of landslides based on image cumulative difference slide according to claim 1 is characterized in that: In step 2, the preprocessing specifically includes: obtaining an original LiDAR point cloud data map based on the DEM data, removing possible outliers and noise points, and generating preprocessed DEM data.

4. The method for analyzing the spatiotemporal evolution of landslides based on image cumulative difference according to claim 2 is characterized in that: The step 5, based on the similarity, combined with the time period cumulative difference method, calculates and draws a cumulative difference-time curve, specifically including: S1, by subtracting the cumulative difference at each time point from the similarity of the initial image, the similarity between each time point and the initial image is obtained; finally, based on the similarity between each time point and the initial image, the cumulative difference CV is calculated; S2. Calculate and draw the cumulative difference-time curve. The specific calculation formula is as follows: Where D i is the difference between remote sensing images of adjacent dates; S0 is the similarity between the first day image and the initial date image; (CV-T) i It is the cumulative difference degree-time of the remote sensing images on each date.

5. The method for analyzing the spatiotemporal evolution of landslides based on image cumulative difference according to claim 1 is characterized in that: In step 6, determining the landslide evolution law of the deformation zone according to the thermal map specifically includes: If the rate of change of the cumulative difference degree-time in the upper part of the deformation area in the thermal map exceeds the rate of change in the lower part, the landslide mode is push type; On the contrary, if the rate of change of the upper cumulative difference degree-time is lower than that of the lower part, the landslide mode is traction type.

6. A landslide spatiotemporal evolution law analysis system based on image cumulative difference, used to execute the landslide spatiotemporal evolution law analysis method based on cumulative difference as claimed in any one of claims 1 to 5, characterized in that: include: The data acquisition module is used to obtain DEM data of the landslide evolution process in the slope deformation area through UAV remote sensing technology; A preprocessing module is used to preprocess the DEM data, obtain the elevation information of each location by reading the pixel values ​​in the preprocessed DEM data, divide the elevation information into different levels, and assign different colors to each level to generate a remote sensing image set; A segmentation module, for evenly dividing the remote sensing images in the remote sensing image set at each time point into a plurality of regions using a refined even segmentation method; The similarity calculation module is used to obtain the hash values ​​of the remote sensing images of each region at adjacent time points by using the mean hash algorithm, and calculate the similarity of the remote sensing images of each region at adjacent time points; A cumulative difference calculation module is used to calculate and draw a cumulative difference-time curve based on the relative degree and a method of accumulating the difference in each period; The determination module is used to draw a regional cumulative difference degree-time thermal map, and determine the landslide evolution law in the deformation zone according to the thermal map.

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