A large-scale extraction method for ice surface elevation from ICESat-2

Abstract

The present invention discloses a large-scale extraction method of ice surface elevation for ICESat-2, which uses the local maximum of the photon weight to construct a surface trend line, and combines the roughness index to perform simple and complex morphological area division. For complex morphological areas, the RANSAC algorithm is used to determine the direction of the photon ellipse neighborhood, the Gaussian kernel function is used to calculate the photon similarity density, and the signal photons are extracted by adaptive density threshold along the track; for simple morphological areas, the model valid points obtained by the RANSAC algorithm are directly used as signal photon points. The present invention can better solve the problem of insufficient accuracy of large-scale signal photon extraction caused by factors such as surface morphological changes and differences in data quality along the track, and the simple and complex morphological area division and regional adaptive ice surface elevation extraction can effectively improve the efficiency of data processing, thereby serving the large-scale monitoring of ice surface elevation changes.

Description

Technical Field

[0001] The present invention belongs to the field of spaceborne laser radar photon point cloud processing, and in particular relates to a large-scale extraction method of ice surface elevation for ICESat-2. Background Art

[0002] ICESat-2 (The Ice, Cloud and Land Elevation Satellite-2) is the world's first laser altimeter satellite to utilize photon-counting mode. Its onboard Advanced Topography Laser Altimeter System (ATLAS) utilizes a 532nm wavelength photon-counting lidar, characterized by high repetition rate and low pulse energy, enabling rapid acquisition of high-precision, large-scale three-dimensional ground data. ATLAS's six-beam laser is divided into three pairs based on pulse energy, producing a laser footprint with a diameter of 11 meters and an along-track resolution of 0.7 meters. This significantly improves spatial resolution compared to ICESat, opening up opportunities for monitoring ice sheet elevation, detecting cracks on glacier surfaces, and inverting the depth of in-glacial lakes. Currently, large-scale monitoring of glacier surfaces using spaceborne lidar primarily relies on ICESat-2 ATL06 data. However, the ICESat-2 ATL06 satellite has an along-track resolution of 20 meters, which is too sparse to capture fine glacier surface features such as small crevasses and ice lakes, thus affecting the measurement of the glacier's mass and dynamic balance. Therefore, the key to extracting and monitoring large-scale ice surface elevation is to quickly and efficiently process the ICESat-2 raw ATL03 data.

[0003] Photon-counting lidars have high sensitivity and are susceptible to solar background noise, atmospheric scattering, and instrument dark counts. This results in a significant amount of noisy photons in the ICESat-2 ATL03 data. Currently, the main approaches for denoising photon point clouds can be categorized into three categories: 1) image processing-based denoising algorithms; 2) density-based denoising algorithms; and 3) local statistical parameter-based denoising algorithms. These methods offer high quality for specific terrains, but when processing large areas, their performance is affected by terrain variations and data quality, resulting in low accuracy. Furthermore, existing methods' strategy of uniformly processing data from diverse terrains is time-consuming and inefficient. Summary of the Invention

[0004] This invention aims to address the efficiency and accuracy issues of large-scale ICESat-2 ATL03 data processing in existing technologies by providing a large-scale ice surface elevation extraction method for ICESat-2. This method can rapidly segment data into different morphological regions and ensure the accuracy of adaptive step-by-step processing, thus providing high-resolution surface features for studies of ice surface evolution.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] A large-scale extraction method for ice surface elevation from ICESat-2 includes the following steps:

[0007] Step 1: Extract the photon point data of each photon point in the study area of ICESat-2 ATL03 data. The photon point data includes photon latitude and longitude, photon elevation, photon confidence and photon weight.

[0008] Step 2: Calculate the distance along the track of each photon point;

[0009] Step 3: Screening photon points based on photon confidence;

[0010] Step 4: construct a surface trend line based on the screened photon points;

[0011] Step 5: dividing each along-track segment into a simple morphological area and a complex morphological area based on the surface trend line, wherein the along-track segment includes multiple along-track sections;

[0012] Step 6: Obtain the extraction result of the ice surface elevation of the simple morphological area;

[0013] Step 7: Obtain the extraction result of the ice surface elevation in the complex morphology area;

[0014] Step 8: Combine and merge the ice surface elevation extraction results of the along-track sections in different morphological areas.

[0015] The distance along the track of the photon point is calculated based on the following formula:

[0016] ,

[0017] Where: represents the distance along the track of the i-th photon point, ) is the UTM projection coordinate of the starting photon point, ) is the UTM projection coordinate of the i-th photon point.

[0018] In step 3, low, medium, and high confidence photons are selected as screened photon points.

[0019] The surface trend line construction in step 4 includes the following steps:

[0020] The photon point is segmented along the track to obtain the track segments, and each track segment is divided into along-track sections; for each along-track section , according to the photon weight extracted in step 1, the maximum weight photon in each track segment is obtained; then The maximum weight photon point of the track segment is set as the set ; Select a collection The maximum elevation point is taken as the trend point, and all trend points are connected to complete the construction of the trend line.

[0021] The division of the simple morphological area and the complex morphological area in step 5 includes the following steps:

[0022] Calculate the distance from the maximum weight photon point to the trend line within the track segment; The distance value from the maximum weight photon point to the trend line in the track segment is set as the set ; Calculate the surface roughness based on the following formula:

[0023] ,

[0024] Where: is the surface roughness, From the first track section to the The distance from the maximum weight photon point in the track segment to the surface trend line, is the total number of along-track sections within the along-track segment,

[0025] If the surface roughness of the track segment is greater than or equal to the roughness threshold, the track segment Each along-track section is a complex morphological area; if the surface roughness of the along-track section is less than the roughness threshold, the along-track section Each section along the track is a simple morphological area.

[0026] The step 6 of obtaining the extraction result of the ice surface elevation of the simple morphological area includes the following steps:

[0027] The RANSAC algorithm is used to perform linear fitting on the photon points in the along-track segment to obtain the set of model internal points in the along-track segment. is the ice surface elevation in the simple morphology area.

[0028] The step 7 of obtaining the extraction result of the ice surface elevation in the complex morphology area includes the following steps:

[0029] Step 7.1: Take each photon point in the track segment as the current center photon point , search for photon points in its circular neighborhood, and perform RANSAC linear fitting on the photon points in the neighborhood to obtain the current central photon point In the direction within the circular neighborhood, the circular neighborhood is replaced by an elliptical neighborhood, the major axis of the ellipse is the diameter of the circle, and the direction of the major axis of the ellipse is the current center photon In the directions within the circle neighborhood, the minor axis of the ellipse is set to a fixed value;

[0030] Step 7.2: Calculate the K nearest neighbor distances of each photon point in the track segment.

[0031] Step 7.3, define the photon point in the elliptical neighborhood and its The average distance between the nearest photon points is taken as the photon spacing , evaluate the photon point within the elliptical neighborhood and the current central photon point The difference in the distance between photons, the photon point From the current central photon point Photon collection within an elliptical neighborhood , and then use the one-dimensional Gaussian kernel function to calculate the current central photon point Similarity density:

[0032] ,

[0033] Where: is the current photon center point Similarity density, photon point From the current central photon point Photon collection within an elliptical neighborhood , is the standard deviation of the Gaussian kernel function, is the photon point in the elliptical neighborhood With the current center photon point The difference in spatial distribution of photons, that is, the difference in the distance between photons;

[0034] Step 7.4: Determine each photon point with a similarity density greater than the density threshold in each along-track segment as a signal photon, and determine the photon point with a similarity density less than the density threshold as a noise photon. Finally, use the signal photons after the density threshold screening as the ice surface elevation extraction result in the complex morphology area.

[0035] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method for large-scale extraction of ice surface elevation for ICESat-2 when executing the computer program.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for large-scale extraction of ice surface elevation for ICESat-2.

[0037] A computer program product includes a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for large-scale extraction of ice surface elevation for ICESat-2.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] (1) High precision: The surface roughness calculated by the present invention can reflect the difference in geometric characteristics between simple and complex morphological areas on the glacier surface, and then set a threshold to achieve high-precision division of simple and complex morphological areas.

[0040] (2) High efficiency: The present invention divides the data of different morphological areas by the roughness index and formulates corresponding denoising schemes according to the data characteristics of different morphological areas, which effectively solves the problem of low efficiency of ICESat-2 ATL03 data processing and serves the large-scale ice surface elevation extraction and monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematic diagram of the study area and data, where (a) is the location of the Jakobshavn Glacier on the Greenland Ice Sheet, (b) is the orbital distribution of ICESat-2 data, (c) is the monthly ICESat-2 data volume from 2019 to 2023, (d) is a schematic diagram of confidence photons, and (e) is a schematic diagram of photon weights;

[0042] Figure 2 It is a schematic diagram of the process of the present invention;

[0043] Figure 3 Schematic diagram for constructing surface trend lines, where (a) is a schematic diagram for extracting the maximum weight photon at 3-meter segmentation along the track in complex morphology areas, (b) is a schematic diagram for extracting the maximum elevation photon at 150-meter segmentation along the track in complex morphology areas and calculating trend lines, (c) is a schematic diagram for extracting the maximum weight photon at 3-meter segmentation along the track in simple morphology areas, (d) is a schematic diagram for extracting the maximum elevation photon at 150-meter segmentation along the track in simple morphology areas and calculating trend lines, and (e) is Figure 3 Line illustrations of (a)-(d);

[0044] Figure 4 Schematic diagram of the division of simple and complex morphological regions, where (a) is the roughness result map of data containing complex morphological regions, and (b) is the roughness result map of data with only simple morphological regions;

[0045] Figure 5Schematic diagram of regional adaptive ice surface elevation extraction, where (a) is the ice surface elevation extraction result for a simple morphology area, and (b) is the ice surface elevation extraction result for a complex morphology area;

[0046] Figure 6 This is a partial schematic diagram of the large-scale extraction results of ice surface elevation from ICESat-2 ATL03 data. (a) to (f) are the ice surface elevation extraction results for different morphological areas and different data quality. The specific data descriptions are as follows: (a) is a simple morphological area with good data quality, (b) is a simple morphological area with poor data quality, (c) includes simple and complex morphological areas with good data quality, (d) includes simple and complex morphological areas with good data quality, (e) is a simple morphological area with poor data quality, and (f) is a simple morphological area with good data quality. DETAILED DESCRIPTION

[0047] In order to facilitate those skilled in the art to understand and implement the present invention, the present invention is further described in detail below in conjunction with embodiments. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0048] Example 1

[0049] A large-scale extraction method for ice surface elevation from ICESat-2 includes the following steps:

[0050] Step 1: Data Reading and Cropping. ICESat-2-ATL03 data is stored in HDF5 format. The Python h5py package was used to extract the photon point data for each photon point from the HDF5-formatted ICESat-2-ATL03 data. The photon point data, including latitude and longitude, photon elevation, photon confidence, and photon weight, was saved as a text file. Based on the photon longitude and latitude, a nearest neighbor algorithm was used to find the intersection of the photon trajectory with the study area boundary. This provided the photon point data within the study area, including the photon longitude and latitude, photon elevation, photon confidence, and photon weight.

[0051] Step 2: Calculate the along-track distance of the photon point. The photon point data obtained in step 1 uses the geographic coordinate system as a reference, which is not convenient for distance calculation. Therefore, first convert the photon point cloud in the study area into the UTM projection coordinate system, and use the starting photon point in the study area as the origin to calculate the along-track distance of the photon point;

[0052] Formula (1)

[0053] Where: represents the distance along the track of the i-th photon point, ) is the UTM projection coordinate of the starting photon point, ) is the UTM projection coordinate of the i-th photon point.

[0054] Step 3: Filter photon points based on photon confidence. The photon point cloud contains a significant amount of noise. The ICESat-2ATL03 data assesses the deviation of each photon point from the Earth's surface based on a reference DEM and assigns a confidence level based on the degree of deviation (the smaller the deviation, the higher the confidence level). Confidence levels are categorized into five levels: 0 represents noise photons; 1 represents extended signal photons, which adds a region of possible signal photons beyond the low, medium, and high confidence ranges; 2 represents low confidence photons; 3 represents medium confidence photons; and 4 represents high confidence photons. Based on the photon confidence levels of each photon point read in Step 1, low, medium, and high confidence photons are selected as filtered photon points to eliminate the large number of noise photons far from the Earth's surface. Subsequent steps are processed based on these filtered photon points.

[0055] Step 4: Surface trend line construction. After screening the photon points, in order to reduce the impact of terrain changes, the photon points are segmented along the track to obtain track segments. Each track segment is divided into For each track segment , according to the photon weight extracted in step 1, the maximum weight photon in each track segment is obtained; then, The maximum weight photon point of the track segment is set as the set ; Finally, select the set The maximum elevation point is used as the trend point. After calculating the trend points of each track segment along the track, all trend points are connected to complete the construction of the trend line.

[0056] In this embodiment, The value is 50.

[0057] Step 5: Roughness estimation. Based on the trend line constructed in step 4, first calculate the distance from the maximum weight photon point in the track segment to the trend line; then, The distance value from the maximum weight photon point to the trend line in the track segment is set as the set Finally, the surface roughness of the track section is calculated using formula (2).

[0058] Formula (2)

[0059] Where: is the surface roughness, From the first track section to the The distance from the maximum weight photon point in the track segment to the surface trend line, is the total number of along-track sections within the along-track section. In this embodiment, The value is 50.

[0060] Generally speaking, the terrain in complex morphology areas fluctuates greatly, and the distance from the maximum weighted photon point to the trend line in the track section is relatively large, so the calculated roughness will be larger than that in simple morphology areas. Based on this characteristic, a roughness threshold is set to divide the simple morphology area and the complex morphology area. If the surface roughness of the track section is greater than or equal to the roughness threshold, the track section is rough. Each along-track section is a complex morphological area; if the surface roughness of the along-track section is less than the roughness threshold, the along-track section Each section along the track is a simple morphological area; thus, the division of simple morphological areas and complex morphological areas is achieved, providing a basis for the rapid extraction of ice surface elevation.

[0061] Step 6: Extract ice surface elevation in simple morphological areas.

[0062] Use the RANSAC (Random Sample Consensus) algorithm to perform linear fitting on the photon points in the along-track segment to obtain the model internal point set in the along-track segment. , and the model interior point set Ice surface elevation extraction results as simple morphological areas.

[0063] Step 7: Extract ice surface elevation in complex morphological areas.

[0064] Step 7.1: Use the RANSAC algorithm to determine the elliptical neighborhood of the photon. Take each photon point in the track segment as the current center photon point , search for photon points in its circular neighborhood, and perform RANSAC linear fitting on the photon points in the neighborhood to obtain the current central photon point Direction within the circular neighborhood. Generally speaking, signal photon points are densely distributed along the photon direction. In order to suppress the influence of noise photons in other directions on the subsequent signal photon density estimation, after obtaining the photon direction, the circular neighborhood is replaced with an elliptical neighborhood. Among them, the size of the ellipse major axis is the diameter of the circle, and the direction of the ellipse major axis is the current center photon. The direction within the circle neighborhood, the minor axis of the ellipse is set to a fixed value.

[0065] Step 7.2: Calculate the K nearest neighbor distances of each photon point.

[0066] Due to the large amount of photon point cloud data, KdTree is used to construct the photon space index and calculate the K-nearest neighbor distance of each photon point in the track segment, as shown in formula (3):

[0067] Formula (3)

[0068] Where: Indicates the first The photon point to the nearest The total distance of photons, where The closest photon point The photon points are sorted according to the calculated Euclidean distance. and Respectively represent The distance along the track between the jth photon point and the jth photon point, and Respectively represent The photon elevation of the jth photon point and the photon elevation of the jth photon point, It is calculated The important parameter of is the number of neighboring photons, which is actually a smoothing factor, and its function is to smooth the spatial heterogeneity within the same type of photons. If it is too small, the smoothing effect will not be obvious, and the distances between the signal photons and the noise photons will be close, making it difficult to distinguish them. If it is too large, the difference between noise photons and signal photons will be over-smoothed, resulting in incomplete photon denoising.

[0069] Step 7.3: Calculate the photon similarity density using the Gaussian kernel function.

[0070] After calculating the K nearest neighbor distances, define the photon point in the elliptical neighborhood and its The average distance between the nearest photon points is taken as the photon spacing (i.e. K nearest neighbor distance / ), and evaluate the photon point within the elliptical neighborhood and the current central photon point The difference in the distance between photons, the photon point From the current central photon point Photon collection within an elliptical neighborhood , and then use the one-dimensional Gaussian kernel function to calculate the current central photon point The similarity density is shown in formula (4).

[0071] Formula (4)

[0072] Where: is the current photon center point Similarity density, photon point From the current central photon point Photon collection within an elliptical neighborhood , is the standard deviation of the Gaussian kernel function, is the photon point in the elliptical neighborhood With the current center photon point The difference in spatial distribution of photons is the difference in the distance between photons.

[0073] Step 7.4: Calculate the along-track adaptive density threshold and filter out the signal photons, and extract the ice surface elevation of the filtered signal photons.

[0074] Generally speaking, there is a density difference between signal photons and noise photons in each along-track segment. The density threshold is set as the average similarity density of all photon points within the along-track segment. Photon points with a similarity density greater than the density threshold within each along-track segment are identified as signal photons, while photon points with a similarity density less than the density threshold are identified as noise photons. Finally, the signal photons filtered by the density threshold are used as the ice surface elevation extraction result for complex morphological areas.

[0075] Step 8: Merge the ice surface elevation extraction results for different morphological regions. After the regionally adaptive ice surface elevation extraction, to ensure the overall consistency of the data results, the ice surface elevation extraction results for the along-track sections of different morphological regions are spliced and merged according to the along-track distance. This completes the large-scale ice surface elevation extraction method for ICESat-2.

[0076] To illustrate the effectiveness of the present invention, all data from the Jakobshavn Glacier on the Greenland ice sheet from 2019 to 2023 were selected for testing. Figure 1 This is a schematic diagram of the study area and data. A total of 2019 ICESat-2 ATL03 data sets were collected for Jakobshavn Glacier from 2019 to 2023, all in version V6. Table 1 shows the parameter settings for the regionally adaptive ice surface elevation extraction method.

[0077] Table 1

[0078]

[0079] This method provides relatively accurate simple and complex morphology area delineation and ice surface elevation results. Data from the Jakobshavn Glacier from January to March 2022 were visually verified. A total of 104 data points were collected from the Jakobshavn Glacier during this period. An overall evaluation of the simple and complex morphology area delineation and ice surface elevation extraction is shown in Table 2, which provides an accuracy evaluation of the complex morphology area detection results.

[0080] Table 2

[0081]

[0082] Figure 4 (a) shows the roughness estimation of data with complex morphology, and it takes only 11 seconds for a 20km data area. Figure 4(b) is the roughness estimation of data with simple morphology, which takes only 100 seconds for a 200km data area. Table 3 shows the elevation extraction schedule for areas with complex morphology ( Figure 4 (a) Comparison of the efficiency of ice surface elevation extraction methods with and without regional adaptation.

[0083] Table 3

[0084]

[0085] From Table 3, Figure 6 It can be seen that after adopting the present invention, the efficiency is significantly improved. Therefore, it can be seen that the present invention can meet the large-scale extraction of ice surface elevation of ICESat-2.

[0086] In addition, according to Figure 4 Middle (a), Figure 4 In (b), the roughness threshold selected by the present invention has an obvious difference between the simple morphology area and the complex morphology area, and can accurately divide the simple and complex morphology areas.

[0087] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related 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-mentioned methods.

[0088] Example 2

[0089] In this embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above embodiment 1 when executing the computer program.

[0090] Example 3

[0091] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned embodiment 1 are implemented.

[0092] Example 4

[0093] In this embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above-mentioned embodiment 1 are implemented.

[0094] It should be noted that the embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described embodiments without departing from the spirit of the present invention or exceeding the scope of the appended claims.

Claims

1. A large-scale extraction method for ice surface elevation based on ICESat-2, characterized in that: The following steps are involved: Step 1: Extract the photon point data of each photon point in the study area of ICESat-2 ATL03 data. The photon point data includes photon latitude and longitude, photon elevation, photon confidence and photon weight. Step 2: Calculate the distance along the track of each photon point; Step 3: Screening photon points based on photon confidence; Step 4: construct a surface trend line based on the screened photon points; Step 5: dividing each along-track segment into a simple morphological area and a complex morphological area based on the surface trend line, wherein the along-track segment includes multiple along-track sections; Step 6: Obtain the extraction result of the ice surface elevation of the simple morphological area; Step 7: Obtain the extraction result of the ice surface elevation in the complex morphology area; Step 8: Merge and combine the ice surface elevation extraction results of the along-track sections in different morphological areas; The division of the simple morphological area and the complex morphological area in step 5 includes the following steps: Calculate the distance from the maximum weight photon point to the trend line within the track segment; The distance value from the maximum weight photon point to the trend line in the track segment is set as the set ; The surface roughness is calculated based on the following formula: ， Where: is the surface roughness, From the first track section to the The distance from the maximum weight photon point in the track segment to the surface trend line, is the total number of along-track sections within the along-track segment, If the surface roughness of the track segment is greater than or equal to the roughness threshold, the track segment Each along-track section is a complex morphological area; if the surface roughness of the along-track section is less than the roughness threshold, the along-track section Each section along the track is a simple morphological area.

2. The large-scale extraction method of ice surface elevation for ICESat-2 according to claim 1, characterized in that: The distance along the track of the photon point is calculated based on the following formula: ， Where: represents the distance along the track of the i-th photon point, ) is the UTM projection coordinate of the starting photon point, ) is the UTM projection coordinate of the i-th photon point.

3. The large-scale extraction method of ice surface elevation for ICESat-2 according to claim 1, characterized in that: In step 3, low, medium, and high confidence photons are selected as screened photon points.

4. The large-scale extraction method of ice surface elevation for ICESat-2 according to claim 1, characterized in that: The surface trend line construction in step 4 includes the following steps: The photon point is segmented along the track to obtain the track segments, and each track segment is divided into along-track sections; for each along-track section , according to the photon weight extracted in step 1, the maximum weight photon in each track segment is obtained; then The maximum weight photon point of the track segment is set as the set ; Select a collection The maximum elevation point is taken as the trend point, and all trend points are connected to complete the construction of the trend line.

5. The method for large-scale extraction of ice surface elevation for ICESat-2 according to claim 1, characterized in that: The step 6 of obtaining the extraction result of the ice surface elevation of the simple morphological area includes the following steps: Use the RANSAC algorithm to perform linear fitting on the photon points in the along-track segment to obtain the set of model internal points in the along-track segment is the ice surface elevation in the simple morphology area.

6. The large-scale extraction method of ice surface elevation for ICESat-2 according to claim 1, characterized in that: The step 7 of obtaining the extraction result of the ice surface elevation in the complex morphology area includes the following steps: Step 7.1: Take each photon point in the track segment as the current center photon point , search for photon points in its circular neighborhood, and perform RANSAC linear fitting on the photon points in the neighborhood to obtain the current central photon point In the direction within the circular neighborhood, the circular neighborhood is replaced by an elliptical neighborhood, the major axis of the ellipse is the diameter of the circle, and the direction of the major axis of the ellipse is the current center photon In the directions within the circle neighborhood, the minor axis of the ellipse is set to a fixed value; Step 7.2: Calculate the K nearest neighbor distances of each photon point in the track segment. Step 7.3, define the photon point in the elliptical neighborhood and its The average distance between the nearest photon points is taken as the photon spacing , evaluate the photon point within the elliptical neighborhood and the current central photon point The difference in the distance between photons, the photon point From the current central photon point Photon collection within an elliptical neighborhood , and then use the one-dimensional Gaussian kernel function to calculate the current central photon point Similarity density: ， Where: is the current photon center point Similarity density, photon point From the current central photon point Photon collection within an elliptical neighborhood , is the standard deviation of the Gaussian kernel function, is the photon point in the elliptical neighborhood With the current center photon point The difference in spatial distribution of photons, that is, the difference in the distance between photons; Step 7.4: Determine each photon point with a similarity density greater than the density threshold in each along-track segment as a signal photon, and determine the photon point with a similarity density less than the density threshold as a noise photon. Finally, use the signal photons after the density threshold screening as the ice surface elevation extraction result in the complex morphology area.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the large-scale extraction method of ice surface elevation for ICESat-2 according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the large-scale extraction method of ice surface elevation for ICESat-2 according to any one of claims 1 to 6 are implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the large-scale extraction method of ice surface elevation for ICESat-2 according to any one of claims 1 to 6 are implemented.

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