Glacier movement speed calculation method, system and equipment based on three-dimensional laser scanning point cloud data, medium and product

Abstract

The invention discloses a glacier movement speed calculation method, system and device based on three-dimensional laser scanning point cloud data, a medium and a product, and relates to the field of geographical environment survey, and the method comprises the steps: carrying out the three-dimensional laser scanning of the surface of a glacier, and obtaining the point cloud data at different moments; adopting a Cosi-Corr algorithm to calculate a displacement field between the point cloud data at different moments; and calculating the glacier movement speed based on the displacement field. According to the invention, the point cloud data is obtained based on three-dimensional laser scanning, automatic data registration and analysis are carried out through the Cosi-Corr algorithm, and the glacier movement speed can be accurately calculated.

Description

Technical Field

[0001] The present application relates to the field of geographical environment survey, and in particular to a method, system, equipment, medium and product for calculating the glacier movement speed based on three-dimensional laser scanning point cloud data. Background Art

[0002] Traditional glacier movement speed calculations are mainly based on optical remote sensing images and manual marking. The accuracy is limited by the image resolution, and the calculation process is relatively complex, making it difficult to meet the needs of high-precision glacier monitoring.

[0003] In recent years, three-dimensional laser scanning technology has developed rapidly, providing a high-precision data source for glacier morphology monitoring. However, there is currently a lack of mature automated velocity calculation methods, resulting in low computational efficiency. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, equipment, medium and product for calculating the movement speed of glaciers based on three-dimensional laser scanning point cloud data, based on three-dimensional laser scanning point cloud data, through Cosi-Corr technology to perform data alignment and analysis, and accurately calculate the movement speed of glaciers.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for calculating glacier movement velocity based on three-dimensional laser scanning point cloud data, comprising:

[0007] Conduct 3D laser scanning on the glacier surface to obtain point cloud data at different times;

[0008] The Cosi-Corr algorithm is used to calculate the displacement field between point cloud data at different times;

[0009] The glacier movement velocity is calculated based on the displacement field.

[0010] In a second aspect, the present application provides a glacier velocity calculation system based on three-dimensional laser scanning point cloud data, comprising:

[0011] The data acquisition module is used to perform three-dimensional laser scanning on the glacier surface to obtain point cloud data at different times;

[0012] The displacement field calculation module is used to calculate the displacement field between point cloud data at different times using the Cosi-Corr algorithm;

[0013] The movement speed calculation module is used to calculate the movement speed of the glacier based on the displacement field.

[0014] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for calculating glacier movement speed based on three-dimensional laser scanning point cloud data.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for calculating the glacier movement speed based on three-dimensional laser scanning point cloud data.

[0016] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for calculating glacier movement speed based on three-dimensional laser scanning point cloud data.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects:

[0018] The present application provides a method, system, equipment, medium and product for calculating the movement speed of glaciers based on three-dimensional laser scanning point cloud data. The point cloud data obtained by three-dimensional laser scanning is automatically aligned and analyzed through the Cosi-Corr algorithm, which can accurately calculate the movement speed of glaciers. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A schematic flow chart of a method for calculating glacier movement velocity based on three-dimensional laser scanning point cloud data provided in one embodiment of the present application;

[0021] Figure 2 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0023] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0024] In an exemplary embodiment, Figure 1 As shown, a method for calculating the movement speed of glaciers based on three-dimensional laser scanning point cloud data is provided. The method is executed by a computer device, and can be executed separately by a computer device such as a terminal or a server, or can be executed jointly by a terminal and a server. In an embodiment of the present application, the method is applied to a server as an example for explanation, including the following steps S1 to S3.

[0025] in:

[0026] S1: Perform three-dimensional laser scanning on the glacier surface to obtain point cloud data at different times.

[0027] Specifically, a fixed-position or drone-mounted scanner is used to perform multiple three-dimensional laser scans of the glacier surface at different times to ensure spatial coverage and obtain high-precision point cloud data.

[0028] After obtaining the point cloud data at different times, the data needs to be preprocessed. The specific steps include:

[0029] (1) De-noising, filtering and adjustment are performed on point cloud data at different times to improve data quality.

[0030] Denoising based on neighborhood analysis: Calculate the geometric features of each point and the surrounding points, and remove abnormal points that do not conform to the glacier surface structure.

[0031] Bilateral filtering: combines spatial information and intensity information to improve data smoothness while maintaining clear boundaries.

[0032] Error correction based on ground control points (GCP): Global geometric correction of point cloud data is performed using known ground control points (GPS positioning) to improve the geographic consistency of the data.

[0033] (2) Spatial alignment of point cloud data at different times. This mainly includes two stages: coarse alignment and fine alignment.

[0034] 1) First, the FPFH (Fast Point Feature Histograms) feature descriptor of each point in the point cloud data at different times is calculated, and the point cloud data at different times are roughly aligned based on the FPFH feature descriptor.

[0035] Specifically, calculate the normal vector of each point (usually using the neighborhood information of the point cloud), and calculate the geometric relationship within the neighborhood based on the normal vector; calculate the FPFH feature descriptor according to the geometric relationship within the neighborhood (such as angle); based on the FPFH feature descriptor, then use a feature matching method (such as distance-based matching) to find matching points; remove incorrect matches through the RANSAC algorithm (Random Sample Consensus) to obtain the preliminary rotation and translation transformation.

[0036] 2) Secondly, based on the point cloud data after coarse registration, use the NDT (Normal Distributions Transform) algorithm to convert the point cloud into a probability distribution, and based on the probability distribution, use a Gaussian mixture model to perform fine registration on the point cloud data after coarse registration.

[0037] Specifically, after coarse registration, the NDT algorithm will use the normal information of the point cloud to convert the point cloud data into a probability distribution. Based on NDT, a Gaussian mixture model can be used to fit the point cloud into multiple Gaussian distributions (one Gaussian distribution for each point cloud region), improve the registration accuracy, and optimize the transformation matrix through maximum likelihood estimation to minimize the registration error between the point cloud after coarse registration and the target point cloud.

[0038] 3) Finally, perform the mean square error (MSE) calculation: statistically calculate the Euclidean distance between the corresponding points of the point clouds at different times to ensure that the mean square error is within an acceptable range.

[0039] S2: Use the Cosi-Corr algorithm to calculate the displacement field between the point cloud data at different times. Specifically, it includes: calculating the correlation of the point cloud data at different times within a preset sliding window area to obtain a correlation value; obtaining the displacement vector corresponding to the maximum correlation value to generate a displacement field.

[0040] The Cosi-Corr (Co-registration of Optically Sensed Images and Correlation) algorithm is an optical image correlation analysis method, commonly used to calculate the relative displacement between time series images. In glacier motion analysis, the Cosi-Corr algorithm derives the glacier motion velocity vector by calculating the displacement field between the point cloud data at different times. The following is the specific calculation process:

[0041] (1) Preset a sliding window

[0042] Purpose: To avoid noise interference, improve the stability of correlation calculation, and at the same time ensure sufficient spatial resolution to detect glacier motion.

[0043] Method:

[0044] Select a sliding window of a fixed size (w×h pixels), usually in the range of 16×16 to 64×64 pixels, and determine an appropriate window size according to the texture characteristics of the glacier surface.

[0045] Priority for stable feature areas: Avoid pure snow or shadow areas, and preferentially select glacier areas with clear surface textures, such as ice cracks or sediments.

[0046] Scale adaptive adjustment:

[0047] For glaciers with slow movement, use a larger sliding window to improve stability.

[0048] For glaciers with fast flow, use a smaller sliding window to obtain more detailed local displacement information.

[0049] (2) Correlation calculation

[0050] In the point cloud data at two different times (or the grayscale image projected onto the image), calculate the similarity of the preset sliding window area to judge the displacement of this area.

[0051] Correlation calculation formula (Normalized Cross-Correlation, NCC):

[0052]

[0053] Where, I1(x,y) and I2(x',y') are the pixel intensities (or point cloud reflectivity values) at the initial time and the subsequent time respectively. and are the means of their respective sliding window areas. (x',y') represents the matching position of the sliding window area at time t+Δt. The NCC value ranges from -1 to 1, and the value closer to 1 represents a higher degree of matching.

[0054] (3) Application of correlation calculation results

[0055] Calculate the NCC over the entire glacier surface through the sliding window, and find the displacement vector (dx,dy) corresponding to the maximum correlation value, that is, the local movement amount of the glacier surface, and then generate a displacement field, which provides input for subsequent deformation optimization.

[0056] S3: Calculate the glacier movement speed based on the displacement field. Specifically, it includes: calculating the movement speed vector of each point in the point cloud data based on the displacement field; performing interpolation processing on the movement speed vector of each point to determine the glacier movement speed.

[0057] After the Cosi-Corr calculation is completed in step S2, the displacement data (dx, dy) for each point on the glacier surface is obtained. Next, the glacier's velocity needs to be calculated and a complete velocity field model needs to be constructed for more accurate glacier dynamic monitoring and prediction.

[0058] (1) The relationship between the displacement calculation results and the motion velocity vector.

[0059] In step S2, the displacement field of the glacier surface at different time points is calculated using the Cosi-Corr algorithm, namely:

[0060] (dx,dy)=(x t+Δt -x t ,y t+Δt -y t )

[0061] It represents the horizontal displacement of a point on the glacier surface within a time interval Δt.

[0062] The velocity vector V is the displacement divided by the time interval Δt:

[0063]

[0064] That is, the velocity vector of each point is the time derivative of its displacement.

[0065] (2) Establishment of motion speed model.

[0066] This embodiment requires the establishment of a mathematical model to represent the velocity distribution of the entire glacier surface. Assuming the two-dimensional spatial coordinates of the glacier surface are (x, y), the velocity field at any position (x, y) can be expressed as:

[0067] V(x,y)=(V x (x,y),V y (x,y))

[0068] This means that every point on the glacier's surface has a velocity vector that describes the direction and magnitude of its motion.

[0069] (3) Use interpolation algorithm to generate continuous velocity field and visualization.

[0070] Since the actual measured velocity data is discrete (velocity values are only available at specific sampling points), in order to describe the movement of the entire glacier surface, it is necessary to use interpolation methods to estimate the velocity at unmeasured points and generate a continuous velocity field.

[0071] This embodiment uses the inverse distance weighting method to interpolate the data. The principle is: when calculating the speed of an unmeasured point, the distance between it and the surrounding measurement points is taken into account. The closer the distance, the greater the weight. The formula is as follows:

[0072]

[0073] Where: V i is the velocity of the known point i, d i is the distance from the interpolation point to the known point i, N is the number of point clouds, and p is the parameter that controls weight attenuation, usually p = 2.

[0074] After interpolation, the continuous velocity field of the entire glacier surface is obtained, which is a two-dimensional vector field and can be expressed as:

[0075] V(x,y)=(V x (x,y),V y (x,y))

[0076] Finally, a raster graph is used to visualize the velocity field. The continuous velocity field obtained through the previous series of steps is input into ENVI (The Environment for Visualizing Images), and the visualization result of the glacier movement trend is obtained after visualization.

[0077] After calculating the glacier velocity, we can analyze the glacier movement trend to reveal the long-term change characteristics. Specifically, by calculating the difference between the historical velocity field, we can assess whether the glacier is accelerating:

[0078] ΔV=V current -V history

[0079] If ΔV>0, it means that the glacier movement is accelerating; if ΔV<0, it means that the glacier is slowing down or stagnating.

[0080] Select key areas on the glacier (such as ice tongues and cracks), analyze their velocity changes, and determine the stability of the glacier.

[0081] This embodiment may also use linear regression, time series models, or machine learning methods to predict the future movement of glaciers and conduct risk assessments.

[0082] Compared with the existing technology, the main advantages of the glacier movement velocity calculation method based on 3D laser scanning point cloud data provided by this embodiment include:

[0083] (1) High Precision: Using 3D laser scanning point cloud data, it provides more detailed surface morphology information than traditional optical imaging. Cosi-Corr technology combined with point cloud data can improve the accuracy of displacement calculation.

[0084] (2) High efficiency: The data preprocessing process is automated through code, which reduces manual intervention and improves computing efficiency.

[0085] (3) Strong applicability: Applicable to different types of glaciers without relying on ground measurement markers. It can be applied to data analysis on different time scales to meet the needs of long-term monitoring.

[0086] Based on the same inventive concept, the embodiments of the present application also provide a glacier movement speed calculation system based on three-dimensional laser scanning point cloud data. The implementation solutions provided by this system to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the glacier movement speed calculation system based on three-dimensional laser scanning point cloud data provided below can refer to the limitations on the glacier movement speed calculation method based on three-dimensional laser scanning point cloud data in the above text, and will not be repeated here.

[0087] In an exemplary embodiment, a glacier movement speed calculation system based on three-dimensional laser scanning point cloud data is provided, including:

[0088] A data acquisition module, configured to perform three-dimensional laser scanning on the glacier surface to obtain point cloud data at different times.

[0089] A displacement field calculation module, configured to calculate the displacement field between the point cloud data at different times by using the Cosi-Corr algorithm.

[0090] A movement speed calculation module, configured to calculate the glacier movement speed based on the displacement field.

[0091] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 2 shown. This computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of this computer device is used to store data to be processed. The input / output interface of this computer device is used to exchange information between the processor and external devices. The communication interface of this computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a glacier movement speed calculation method based on three-dimensional laser scanning point cloud data.

[0092] Those skilled in the art can understand that Figure 2 The structure shown in Figure 2 is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0093] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0094] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0095] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0096] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0097] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0098] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0099] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application; at the same time, for those of ordinary skill in the art, according to the ideas of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for calculating the glacier movement speed based on three-dimensional laser scanning point cloud data, characterized in that Including: Performing three-dimensional laser scanning on the glacier surface to obtain point cloud data at different times; Using the Cosi-Corr algorithm to calculate the displacement field between the point cloud data at different times; Calculating the glacier movement speed based on the displacement field.

2. The method for calculating the glacier movement speed based on three-dimensional laser scanning point cloud data according to claim 1, wherein After obtaining the point cloud data at different times, it further includes: preprocessing the point cloud data at different times.

3. The method for calculating the glacier movement speed based on the three-dimensional laser scanning point cloud data according to claim 2, wherein Preprocessing the point cloud data at different times specifically includes: Performing denoising, filtering, and adjustment processing on the point cloud data at different times; Performing spatial alignment on the point cloud data at different times.

4. The method for calculating the glacier movement speed based on three-dimensional laser scanning point cloud data according to claim 3, wherein Performing spatial alignment on the point cloud data at different times includes a coarse registration stage and a fine registration stage; Among them, performing spatial alignment on the point cloud data at different times specifically includes: Calculating the FPFH feature descriptor for each point in the point cloud data at different times; Performing coarse registration on the point cloud data at different times based on the FPFH feature descriptor; Based on the coarsely registered point cloud data, using the NDT algorithm to convert the point cloud into a probability distribution; Based on the probability distribution, using the Gaussian mixture model to perform fine registration on the coarsely registered point cloud data.

5. The method for calculating the glacier movement speed based on three-dimensional laser scanning point cloud data according to claim 1, wherein Using the Cosi-Corr algorithm to calculate the displacement field between the point cloud data at different times specifically includes: Calculating the correlation of the point cloud data at different times within a preset sliding window area to obtain a correlation value; Obtaining the displacement vector corresponding to the maximum correlation value to generate a displacement field.

6. The method for calculating the glacier movement speed based on three-dimensional laser scanning point cloud data according to claim 1, wherein Calculating the glacier movement speed based on the displacement field specifically includes: Calculating the movement speed vector for each point in the point cloud data based on the displacement field; Performing interpolation processing on the movement speed vector of each point to determine the glacier movement speed.

7. A glacier movement speed calculation system based on three-dimensional laser scanning point cloud data, characterized in that Including: A data acquisition module for performing three-dimensional laser scanning on the glacier surface to obtain point cloud data at different times; A displacement field calculation module for using the Cosi-Corr algorithm to calculate the displacement field between the point cloud data at different times; A movement speed calculation module for calculating the glacier movement speed based on the displacement field.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for calculating the glacier movement speed based on three-dimensional laser scanning point cloud data according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for calculating the glacier movement speed based on three-dimensional laser scanning point cloud data according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for calculating the glacier movement speed based on three-dimensional laser scanning point cloud data according to any one of claims 1-6.

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