A glacier movement speed calculation method, system, device, medium and product based on three-dimensional laser scanning point cloud data

Abstract

The application discloses a glacier movement speed calculation method, system, device, medium and product based on three-dimensional laser scanning point cloud data, relates to the field of geographic environment survey, and comprises the following steps: performing three-dimensional laser scanning on a glacier surface to obtain point cloud data at different time points; adopting a Cosi-Corr algorithm to calculate a displacement field between the point cloud data at different time points; and calculating the glacier movement speed based on the displacement field. The point cloud data obtained based on three-dimensional laser scanning is subjected to automatic data registration and analysis 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 geographic environment surveying, and in particular to a glacier movement speed calculation method, system, device, medium and product based on three-dimensional laser scanning point cloud data. BACKGROUND

[0002] Traditional glacier movement speed calculation is mainly based on optical remote sensing images and manual marking, and the precision is limited by image resolution, and the calculation process is relatively complex, which is difficult to meet the demand 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, but there is currently a lack of mature automatic speed calculation method, and the calculation efficiency is low. SUMMARY

[0004] The purpose of the present application is to provide a glacier movement speed calculation method, system, device, medium and product based on three-dimensional laser scanning point cloud data, which is based on three-dimensional laser scanning point cloud data, and performs data registration and analysis through Cosi-Corr technology to accurately calculate the glacier movement speed.

[0005] To achieve the above purpose, the present application provides the following solutions:

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

[0007] Performing three-dimensional laser scanning on the surface of the glacier to obtain point cloud data at different times;

[0008] Calculating the displacement field between the point cloud data at different times by using Cosi-Corr algorithm;

[0009] Calculating the glacier movement speed based on the displacement field.

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

[0011] A data acquisition module for performing three-dimensional laser scanning on the surface of the glacier to obtain point cloud data at different times;

[0012] A displacement field calculation module for calculating the displacement field between the point cloud data at different times by using Cosi-Corr algorithm;

[0013] A movement speed calculation module for calculating the glacier movement speed 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 glacier movement speed calculation method based on three-dimensional laser scanning point cloud data.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the glacier movement speed calculation method based on three-dimensional laser scanning point cloud data.

[0016] In a fifth aspect, the present application provides a computer program product, comprising a computer program, wherein the computer program is executed by a processor to implement the glacier movement speed calculation method based on three-dimensional laser scanning point cloud data.

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

[0018] The present application provides a glacier movement speed calculation method, system, device, medium and product based on three-dimensional laser scanning point cloud data, which can accurately calculate the glacier movement speed through automatic data registration and analysis by Cosi-Corr algorithm based on the point cloud data obtained by three-dimensional laser scanning. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 A flowchart of a glacier movement speed calculation method based on three-dimensional laser scanning point cloud data provided by an embodiment of the present application is shown in the figure.

[0021] Figure 2 A structural diagram of a computer device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0023] In order to make the above objectives, features and advantages of the present application more apparent, more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] In one exemplary embodiment, as shown in Figure 1 A method for calculating glacier movement speed based on three-dimensional laser scanning point cloud data is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or can be executed by a terminal and a server together, in the embodiment of the present application, taking the method applied to the server as an example, comprising the following steps S1 to S3.

[0025] Among them:

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

[0027] Specifically, a fixed position or a drone-mounted scanner is used to perform multiple three-dimensional laser scanning on the surface of the glacier 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. Specifically, the following steps are included:

[0029] (1) The point cloud data at different times is denoised, filtered and adjusted to improve the 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 structure of the glacier surface.

[0031] Bilateral filtering: combining spatial information and intensity information, improving data smoothness while maintaining clear boundaries.

[0032] Error correction based on ground control points (GCP): through known ground control points (GPS positioning), the point cloud data is globally geometrically corrected to improve the geographical consistency of the data.

[0033] (2) The point cloud data at different times is spatially aligned. It mainly includes two stages: coarse registration stage and fine registration stage.

[0034] 1) First, calculate the FPFH (Fast Point Feature Histograms) feature descriptor of each point in the point cloud data at different times, and perform coarse registration on the point cloud data at different times based on the FPFH feature descriptor.

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

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

[0037] Specifically, after coarse registration, the NDT algorithm uses 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, the mean square error (MSE) is calculated: the Euclidean distance between the corresponding points of the point cloud at different times is calculated 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 point cloud data at different times. Specifically, it includes: calculating the correlation of point cloud data at different times within a preset sliding window region to obtain a correlation value; obtaining the displacement vector corresponding to the maximum correlation value to generate a displacement field.

[0040] 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 the analysis of glacier movement, the Cosi-Corr algorithm calculates the displacement field between point cloud data at different times to derive the glacier movement velocity vector. The following is the specific calculation process:

[0041] (1) Preset 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 movement.

[0043] Method:

[0044] A fixed-size sliding window (w x h pixels) is selected, usually in the range of 16 x 16 to 64 x 64 pixels, and the appropriate window size is determined according to the texture characteristics of the glacier surface.

[0045] Stable feature area priority: Avoid pure snow surface or shadow area, and prefer to choose glacier area with clear surface texture, such as ice cracks or sediments.

[0046] Scale adaptive adjustment:

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

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

[0049] (2) Correlation calculation

[0050] In the point cloud data (or gray scale image projected on the image) at two different times, the similarity of the preset sliding window area is calculated to determine the displacement of the 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 and subsequent times, respectively. And are the mean values of the 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 closer the value is to 1, the higher the matching degree.

[0054] (3) Application of correlation calculation results

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

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

[0057] After the Cosi-Corr calculation in step S2, the displacement data (dx, dy) of each point on the glacier surface is obtained. Next, the movement speed of the glacier needs to be calculated, and a complete speed field model needs to be constructed in order to carry out more accurate glacier dynamic monitoring and prediction.

[0058] (1) Relationship between displacement calculation results and movement speed vector.

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

[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 movement speed vector V is the displacement divided by the time interval Δt:

[0063]

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

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

[0066] This embodiment needs to establish a mathematical model to represent the movement speed distribution of the entire glacier surface. Let the two-dimensional spatial coordinates of the glacier surface be (x, y), then the speed field at any position (x, y) can be represented as:

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

[0068] This means that each point on the glacier surface has a speed vector, which describes the direction and speed of the point.

[0069] (3) Generation of continuous speed field and visualization using interpolation algorithm.

[0070] Since the actual measured speed data is discrete (only has speed values at certain sampling points), in order to describe the movement of the entire glacier surface, an interpolation method is needed to estimate the speed at unmeasured points to generate a continuous speed field.

[0071] This embodiment selects the inverse distance weighting method to interpolate the data, the principle of which is: when calculating the speed at an unmeasured point, the distance between it and the surrounding measured points is considered, the closer the distance, the greater the weight. The formula is as follows:

[0072]

[0073] where: V i is the velocity of known point i, d i is the distance from the point to be interpolated to the known point i, N is the number of point clouds, and p is a parameter controlling the weight decay, usually p = 2.

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

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

[0076] Finally, the velocity field is visualized using a grid chart. In ENVI (The Environment for Visualizing Images), the continuous velocity field obtained through the previous series of steps is input, and after visualization, the visualization result of the glacier movement trend is obtained.

[0077] After calculating the glacier movement velocity, the glacier movement trend can be analyzed to reveal the long-term change characteristics. Specifically, by calculating the difference between the historical velocity fields, it is evaluated whether the glacier is accelerating:

[0078] ΔV = V current - V history

[0079] If ΔV > 0, it indicates that the glacier is accelerating; if ΔV < 0, it indicates that the glacier is decelerating or stagnant.

[0080] Key areas on the glacier (such as the tongue, cracks) are selected to analyze their velocity changes and determine the stability of the glacier.

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

[0082] Compared with the prior art, the main advantages of the glacier movement velocity calculation method based on three-dimensional laser scanning point cloud data provided by the embodiment include:

[0083] (1) High precision: Using three-dimensional laser scanning point cloud data provides more detailed surface morphology information than traditional optical images. The Cosi-Corr technique combined with point cloud data can improve the displacement calculation accuracy.

[0084] (2) High efficiency: The data preprocessing automation process implemented by code reduces manual intervention and improves calculation efficiency.

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

[0086] Based on the same inventive concept, the embodiments of the present application also provide a system for calculating the movement speed of a glacier based on three-dimensional laser scanning point cloud data. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more system embodiments for calculating the movement speed of a glacier based on three-dimensional laser scanning point cloud data provided below can refer to the limitations of the method for calculating the movement speed of a glacier based on three-dimensional laser scanning point cloud data described above, and will not be repeated here.

[0087] In an exemplary embodiment, a system for calculating the movement speed of a glacier based on three-dimensional laser scanning point cloud data is provided, comprising:

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

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

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

[0091] In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, and the memory stores a computer program. When the processor executes the computer program, the steps in the above method embodiments are implemented. The computer device can be a server or a terminal, and its internal structure diagram can be as shown in Figure 2 The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. 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 the computer device is used to provide computing and control capabilities. The memory of the 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 the computer device is used to store data to be processed. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a method for calculating the movement speed of a glacier based on three-dimensional laser scanning point cloud data.

[0092] Those skilled in the art can understand that Figure 2 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method embodiments.

[0093] In an exemplary embodiment, a computer readable storage medium is provided, storing a computer program, which is executed by a processor to implement the steps in the above method embodiments.

[0094] In an exemplary embodiment, a computer program product is provided, including a computer program, which is executed by a processor to implement the steps in the above method embodiments.

[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 the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

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

[0097] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0098] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0099] The principles and implementation modes of the present application are described by applying specific examples herein, and the above-mentioned embodiments are only used to help understand the method and its core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range can be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for calculating the velocity of glaciers based on three-dimensional laser scanning point cloud data, characterized in that, include: Three-dimensional laser scanning was performed on the glacier surface to obtain point cloud data at different times; The Cosi-Corr algorithm is used to calculate the displacement field between point cloud data at different times; specifically, it includes: calculating the correlation of point cloud data at different times within a preset sliding window area to obtain the correlation value; obtaining the displacement vector corresponding to the maximum correlation value to generate the displacement field; The glacier's velocity is calculated based on the displacement field. After obtaining point cloud data at different times, the process also includes: preprocessing the point cloud data at different times; specifically, this includes: denoising, filtering, and adjustment of the point cloud data at different times; and spatial alignment of the point cloud data at different times. Spatial alignment of point cloud data at different times includes coarse registration and fine registration stages; The spatial alignment of 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 of the point cloud data at different times based on the FPFH feature descriptor; converting the point cloud into a probability distribution using the NDT algorithm based on the coarsely registered point cloud data; and performing fine registration of the coarsely registered point cloud data using a Gaussian mixture model based on the probability distribution.

2. The method for calculating glacier movement velocity based on three-dimensional laser scanning point cloud data according to claim 1, characterized in that, The calculation of glacier movement velocity based on the displacement field specifically includes: Calculate the motion velocity vector of each point in the point cloud data based on the displacement field; The velocity vector at each point is interpolated to determine the glacier's velocity.

3. A system for calculating the velocity of glaciers based on three-dimensional laser scanning point cloud data, characterized in that, include: The data acquisition module is used to perform three-dimensional laser scanning on the glacier surface to obtain point cloud data at different times; The displacement field calculation module is used to calculate the displacement field between point cloud data at different times using the Cosi-Corr algorithm; specifically, it includes: calculating the correlation of point cloud data at different times within a preset sliding window area to obtain the correlation value; obtaining the displacement vector corresponding to the maximum correlation value to generate the displacement field; A motion velocity calculation module is used to calculate the glacier's motion velocity based on the displacement field; After obtaining point cloud data at different times, the process also includes: preprocessing the point cloud data at different times; specifically, this includes: denoising, filtering, and adjustment of the point cloud data at different times; and spatial alignment of the point cloud data at different times. Spatial alignment of point cloud data at different times includes coarse registration and fine registration stages; The spatial alignment of 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 of the point cloud data at different times based on the FPFH feature descriptor; converting the point cloud into a probability distribution using the NDT algorithm based on the coarsely registered point cloud data; and performing fine registration of the coarsely registered point cloud data using a Gaussian mixture model based on the probability distribution.

4. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the glacier motion velocity calculation method based on three-dimensional laser scanning point cloud data as described in any one of claims 1-2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for calculating glacier movement velocity based on three-dimensional laser scanning point cloud data as described in any one of claims 1-2.

6. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for calculating glacier movement velocity based on three-dimensional laser scanning point cloud data as described in any one of claims 1-2.

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