Seabed topography photon extraction method, equipment, storage medium and product

Abstract

The present invention discloses a method, device, storage medium, and product for extracting seabed topography photons. The method comprises extracting sea surface photon data and water body photon data from shallow sea segment photon data; initializing the major and minor semi-axes of an elliptical filter kernel; calculating a first density value for each photon in the water body photon data; determining a first density threshold based on the initial values ​​of the major and minor semi-axes; performing a primary filtering on the water body photon data; updating the major and minor semi-axes based on the elevation of each photon; calculating a second density value for the photons in the primary filtered water body photon data; calculating a second density threshold for a second elevation segment based on the first density value; calculating a third density threshold based on the updated major semi-axis; and performing a secondary filtering based on the second density threshold, the third density threshold, and the second density value to obtain seabed topography photon data. The present invention improves filtering accuracy and alleviates issues of missed detection and false detection.

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 method, device, storage medium and product for extracting seabed topography photons taking into account spatial relationships. Background Art

[0002] Because the ATLAS (Advanced Topographic Laser Altimeter System) carried by the ICESat-2 satellite uses micro-pulse photon technology, it is easily affected by factors such as solar background and atmospheric scattering, resulting in the received echo signals containing varying degrees of noise photons. Therefore, in order to obtain water depth data from the ATLAS data, it is critical to filter the raw ATLAS data and extract the seabed topography photons.

[0003] Research has found that using a local parameter estimation method for shallow sea terrain photon extraction is more practical. Reference 1 (see: Zhu Xiaoxiao, Nie Sheng, Wang Cheng, Xi Xiaohuan, Hu Zhenyue. A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data[J]. Remote Sensing, 2018, 10(12).) takes slope adaptation into account in the photon density calculation, uses Gaussian fitting to calculate the photon density threshold for the photon density histogram, and extracts signal photons. This method does not take into account the complex distribution of signal photons and is not effective for extracting sparsely distributed signal photons. Reference 2 (see: Chen Yifu, Le Yuan, Zhang Dongfang, Wang Yong, Qiu Zhenge, Wang Lizhe. A photon-counting LiDAR bathymetric method based on adaptive variable ellipse filtering [J]. Remote Sensing of Environment, 2021, 256.) utilizes the density difference between signal photons and noise photons in ATL03 data, and adaptively adjusts the size of the elliptical filter kernel and the density threshold according to the photon depth, thereby realizing the recognition and extraction of photons on the sea, sea surface and seabed surface. The application effect on high signal-to-noise ratio photon data is very good, but the relationship between photon density and water depth is simplified to linear, which weakens the generalization ability of the model and will introduce more noise when applied to low signal-to-noise ratio data.Reference 3 (Hsu, H., Huang, C., Jasinski, MF, Li, Y., Gao, H., Yamanokuchi, T., Wang, C., Chang, TM, Ren, H., Kuo, C., & Tseng, KA semi-empirical scheme for bathymetric mapping in shallow water by ICESat-2and Sentinel-2: A case study in the South China Sea. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 2021, 1-19.) utilizes the difference in the aggregation of signal photon and noise photon densities in ATL03 data, and uses a bimodal Gaussian fitting algorithm to identify and extract sea surface and seabed photons in the window divided along the track. The extraction effect is mainly affected by the sharpness of the peak within the window. For low signal-to-noise ratio data with weak sea surface and seabed signal photon aggregation, the fitting effect is poor and a large amount of noise will be introduced. Reference 4 (see: N.Xu, Y.Ma, H.Zhou, W.Zhang, Z.Zhang and XHWang, "A Method to DeriveBathymetry for Dynamic Water Bodies Using ICESat-2and GSWD Data Sets," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 1500305, doi: 10.1109 / LGRS.2020.3019396.) uses an adaptive DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise) to extract underwater photons from ICESat-2 data. This algorithm may misjudge local areas where photon data is sparse and the terrain changes dramatically.Reference 5 (see: P. Yang, H. Fu, J. Zhu, Y. Li and C. Wang, "An Elliptical Distance Based Photon Point Cloud Filtering Method in Forest Area," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 6504705, doi: 10.1109 / LGRS.2021.3124612.) uses a slope-adaptive fixed ellipse to traverse photons to calculate the sum of photon neighborhood distances, and then identifies and extracts signal photons based on the distance threshold, taking into account the geographical similarity between adjacent photons. However, the application effect is not good for the extraction of discretely distributed terrain photons. Reference 6 (see: ZHANG Guoping, XU Qing, XING Shuai, et al. A noise-removal algorithm without input parameters based on quadtree isolation for photon-counting LiDAR [J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.) uses the MATLAS data of simulated ICESat-2 to perform quadtree denoising without input parameters to complete the recognition and extraction of point cloud signals. This method has a high error rate for noise photons under strong noise background. In response to the problems in reference 6, reference 7 (see: Liu Xiang, Zhang Lihua, Dai Zeyuan, et al. A method for ICESat-2 point cloud denoising under strong noise background without input parameters [J]. Acta Photonica Sinica, 2022, 51 (11): 346-356.) uses ATL03 data, performs pruning on the basis of quadtree establishment, and adds secondary denoising, which improves the extraction effect of signal photons.Reference 8 (see: G. Zhang, W. Lian, S. Li, H. Cui, M. Jing and Z. Chen, "A Self-Adaptive Denoising Algorithm Based on Genetic Algorithm for Photon-Counting Lidar Data," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 6501405, doi: 10.1109 / LGRS.2021.3067609.) calculates the neighbor distance based on the weighted distance of the photon neighbors and uses it as a feature to extract signal photons. This method is unstable when applied to photon data with low signal-to-noise ratio.

[0004] Key to extracting seafloor topography from photons lies in the selection of the shape and size of the density statistical unit and the setting of the density segmentation threshold. In recent years, the shape of the filter kernel has evolved from a circle to an ellipse to a parallelogram, while its size and density segmentation threshold have evolved from fixed to adaptive. However, most methods simplify the calculation of photon density by establishing a linear relationship with water depth, rarely considering the geographical variability of photon data. As a result, these methods are effective in extracting seafloor topography from photon data with high signal-to-noise ratios, but the processing results for photon data with low signal-to-noise ratios may result in a high number of missed terrain photons and falsely detected noise photons. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, device, storage medium and product for extracting photons from seabed topography to solve the problem that traditional methods do not take into account the geographical differences of photon data, resulting in missed detection and false detection of photon data with low signal-to-noise ratio.

[0006] The present invention solves the above technical problems through the following technical solutions: A method for extracting photons from seabed topography, comprising:

[0007] Preprocess ICESat-2 / ATL03 photon data to obtain shallow sea photon data;

[0008] Extracting sea surface photon data and water body photon data from the shallow sea section photon data;

[0009] Constructing a mathematical model of an elliptical filter kernel according to the along-track distance and elevation of the photons, and initializing the major semi-axis and the minor semi-axis of the elliptical filter kernel;

[0010] Calculating a first density value of each photon in the water body photon data according to the initial values ​​of the major semi-axis and the minor semi-axis and a mathematical model of an elliptical filter kernel;

[0011] Determining a first density threshold according to the water body photon data and initial values ​​of the major semi-axis and the minor semi-axis;

[0012] Performing a primary filtering on the water body photon data according to the first density threshold and a first density value of each photon;

[0013] updating the major semi-axis and the minor semi-axis according to the elevation of each photon in the water body photon data after the primary filtering; calculating the second density value of the corresponding photon in the water body photon data after the primary filtering according to the updated major semi-axis and the minor semi-axis and the mathematical model of the elliptical filter kernel;

[0014] calculating a second density threshold for each second elevation segment based on the first density value of each photon;

[0015] Calculate the third density threshold based on the updated major semi-axis;

[0016] The water body photon data after the primary filtering is subjected to secondary filtering according to the second density threshold, the third density threshold and the second density value of each photon to obtain seabed topography photon data.

[0017] Furthermore, extracting sea surface photon data and water body photon data from the shallow sea section photon data includes:

[0018] Setting the width of the first elevation segment, projecting the shallow sea segment photon data according to the elevation, and counting the number of photons in each first elevation segment to form an elevation distribution histogram;

[0019] A bimodal Gaussian distribution fitting algorithm is used to fit the elevation distribution histogram, and then the sea surface photon data and the water body photon data are extracted.

[0020] Further, initializing the major semi-axis and the minor semi-axis of the elliptical filter kernel according to the sea surface photon data includes:

[0021] The sea surface photon data is divided into n segments along the track according to the step size, and the maximum elevation difference of each first segment along the track is calculated; the initial values ​​of the major and minor axes of the elliptical filter kernel are calculated, and the specific formula is:

[0022] b0=|H max -H min |;

[0023]

[0024] Among them, b0 represents the initial value of the short semi-axis of the elliptical filter kernel, H max Indicates the upper boundary of the sea surface photon data, H min represents the lower boundary of the sea surface photon data, a0 represents the initial value of the major semi-axis of the elliptical filter kernel, R abrepresents the shape adjustment factor of the elliptical filter kernel, Δd represents the step size, ΔSH i Indicates the maximum elevation difference of the i-th first along-track segment.

[0025] Furthermore, the specific process of calculating the first density value or the second density value of each photon includes:

[0026] Calculating the distance between each photon and other photons according to the mathematical model of the elliptical filter kernel;

[0027] A first distance threshold is set; for each photon, the number of photons whose distance does not exceed the first distance threshold is counted, where the number of photons is the first density value or the second density value of the photon.

[0028] Furthermore, the formula for determining the first density threshold is:

[0029]

[0030] Among them, D T1 represents the first density threshold, SN1 represents the signal photon distribution density within the initial unit elliptical kernel area, SN2 represents the noise photon distribution density within the initial unit elliptical kernel area, a0 represents the initial value of the major semi-axis of the elliptical filter kernel, b0 represents the initial value of the minor semi-axis of the elliptical filter kernel, M represents the number of photons in the water body photon data, ΔH represents the vertical distance between the highest photon and the lowest photon in the water body photon data, ΔL represents the difference between the maximum along-track distance and the minimum along-track distance in the water body photon data, m l Represents the number of photons within the elevation range of the noise photon distribution in the water body photon data, N H Indicates the elevation range of the noise photon distribution in the set water body photon data.

[0031] Furthermore, the updating formulas of the major and minor semi-axis are:

[0032] b=b0+τ|h p -H min |,a=R ab b;

[0033] Among them, b represents the updated minor semi-axis, b0 represents the initial value of the minor semi-axis of the elliptical filter kernel, τ represents the scaling factor of the ellipse, and h p represents the height of the p-th photon, H min represents the lower boundary of the sea surface photon data, a represents the updated major semi-axis, R ab Represents the shape adjustment factor of the elliptical filter kernel.

[0034] Furthermore, calculating a second density threshold in each second elevation segment according to the first density value of each photon includes:

[0035] Set the width of the second elevation segment, project the filtered water body photon data according to the elevation, count the first density values ​​of all photons in each second elevation segment, and sort the first density values ​​in descending order;

[0036] The maximum inter-class variance method is used to calculate the initial density threshold of each second elevation segment;

[0037] The second density threshold of the corresponding second elevation segment is calculated according to the initial density threshold of each second elevation segment and the initial density thresholds of its adjacent second elevation segments.

[0038] Furthermore, the extraction method further includes using a bidirectional seed point growth algorithm to supplement the seabed topography photon data, specifically including:

[0039] Calculating the difference in along-track distances between every two adjacent photons in the seabed topography photon data;

[0040] Calculating a second distance threshold according to the difference in along-track distances between every two adjacent photons;

[0041] Determine a discontinuous area according to the difference in along-track distances between every two adjacent photons and a second distance threshold;

[0042] For discontinuous areas, one of the two adjacent photons is used as the initial seed point, and the nearest neighbor photon search is performed in the direction of the other photon to obtain the candidate photon;

[0043] Determine whether the candidate photon is a signal photon or a noise photon; when the candidate photon is a signal photon, add the signal photon to the seabed topography photon data, and use the signal photon as a new seed point, repeat the steps of nearest neighbor photon search and candidate photon determination until the seed point exceeds the discontinuous area; when the candidate photon is a noise photon, complete the processing of one side of the discontinuous area;

[0044] Taking the other photon of the two adjacent photons as the initial seed point, a nearest neighbor photon search is performed in the direction of one of the photons to obtain a candidate photon;

[0045] Determine whether the candidate photon is a signal photon or a noise photon; when the candidate photon is a signal photon, add the signal photon to the seabed topography photon data, and use the signal photon as a new seed point, repeat the steps of nearest neighbor photon search and candidate photon determination until the candidate photon exceeds the discontinuous area; when the candidate photon is a noise photon, complete the processing on the other side of the discontinuous area;

[0046] Merging the seabed topography photon data obtained after processing on both sides of the discontinuous area, and dividing the merged seabed topography photon data into multiple segments along the track to obtain multiple second along-track segments;

[0047] For each of the second along-track segments, segment the segments according to the elevation to obtain a plurality of third elevation segments, count the number of photons in each third elevation segment, and find the third elevation segment with the largest number of photons;

[0048] Determine whether the photon number of the third elevation segment with the largest photon number is greater than twice the photon data of its two adjacent third elevation segments. If so, retain the third elevation segment with the largest photon number and discard the two adjacent third elevation segments of the third elevation segment with the largest photon number. If not, reduce the width of the third elevation segment and repeat the steps of segmenting and judging according to the elevation until a third elevation segment with a photon number greater than twice the photon data of its two adjacent third elevation segments is found.

[0049] All the processed second along-track segments are merged to obtain the final seabed topography photons.

[0050] Based on the same concept, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program / instruction stored in the memory, wherein the processor executes the computer program / instruction to implement the above-mentioned method for extracting photons from seabed topography.

[0051] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the above-mentioned method for extracting photons from seabed topography is implemented.

[0052] Based on the same concept, the present invention also provides a computer program product, comprising a computer program / instruction, which implements the above-mentioned method for extracting seabed topography photons when executed by a processor.

[0053] Beneficial effects

[0054] Compared with the prior art, the advantages of the present invention are:

[0055] The present invention first calculates a first density threshold based on the initial values ​​of the major and minor semi-axes of the elliptical filter kernel. The first density threshold filters the water body photon data once to achieve global filtering of the water body photon data; then, considering the variability and diversity of the spatial distribution of photons, the density threshold (i.e., the second density threshold) of each elevation segment is calculated, and a nonlinear relationship between photon density and water depth is established. This can better reflect the density difference between signal photons and noise photons in each elevation segment, improve the accuracy of filtering, and improve the problems of missed detection and false detection.

[0056] Taking into account that each elevation segment may have signal photons and noise photons of comparable density, a bidirectional seed point growth algorithm is used to supplement the seabed topography photon data, that is, the signal photons filtered out by the secondary filtering are added to the seabed topography photon data, which further improves the accuracy of the filtering and avoids missed detection and false detection problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 This is a flow chart of a method for extracting photons from seabed topography according to an embodiment of the present invention;

[0059] Figure 2 is an elevation distribution histogram in an embodiment of the present invention;

[0060] Figure 3 Schematic diagram of sea surface photon data in an embodiment of the present invention; wherein the two straight lines are the upper and lower boundary lines of the sea surface photon data respectively;

[0061] Figure 4 1 is a schematic diagram of a filtering result in an embodiment of the present invention;

[0062] Figure 5 Schematic diagram of elliptical filter kernels for different photons in an embodiment of the present invention;

[0063] Figure 6 In the embodiment of the present invention, the water body photon data after the primary filtering is segmented in elevation;

[0064] Figure 7 is the second density threshold in each second elevation segment (or vertical water depth segment) in the embodiment of the present invention;

[0065] Figure 8 Schematic diagram of secondary filtering results in an embodiment of the present invention; wherein the green dots represent seabed topography photons obtained by secondary filtering;

[0066] Figure 9 Schematic diagram of discontinuous region positioning in an embodiment of the present invention; wherein each rectangular box is a discontinuous region, and the blue and red dots within the rectangular box are two adjacent photons that determine the discontinuous region;

[0067] Figure 10 This is the supplementary result of left-direction seed point growth in the embodiment of the present invention;

[0068] Figure 11This is the supplementary result of rightward seed point growth in the embodiment of the present invention;

[0069] Figure 12 It is the supplementary result of seed point growth after left and right directions are merged in the embodiment of the present invention;

[0070] Figure 13 Schematic diagram of the second along-track segmentation and the third elevation segmentation in an embodiment of the present invention;

[0071] Figure 14 is the final seabed topography photon data obtained after coarse denoising in the embodiment of the present invention;

[0072] Figure 15 is the extraction result of the first ICESat-2 / ATL03 photon data according to the embodiment of the present invention;

[0073] Figure 16 is the extraction result of the second ICESat-2 / ATL03 photon data according to the embodiment of the present invention;

[0074] Figure 17 is the extraction result of the third ICESat-2 / ATL03 photon data in the embodiment of the present invention;

[0075] Figure 18 This is the extraction result of the 4th ICESat-2 / ATL03 photon data by the present invention in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The following is a clear and complete description of the technical solutions of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0077] The following specific embodiments are used to describe the technical solution of the present application in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0078] Example 1

[0079] like Figure 1 As shown, a method for extracting photons from seabed topography provided by an embodiment of the present invention includes the following steps:

[0080] Step S1: Preprocess the ICESat-2 / ATL03 photon data to obtain shallow sea segment photon data.

[0081] ICESat-2 / ATL03 photon data were downloaded from the public NSIDC as HDF5 (Hierarchical Data Format) files. Key photon information (such as latitude and longitude, photon altitude (or elevation), and along-track distance) was extracted from the HDF5 files to form a multidimensional list. Initial data cleaning was then performed based on latitude and longitude and elevation, removing photon data outside the target area, as well as non-marine data with excessively high or low elevations and noise. Visualization was then performed using photon elevation and along-track distance as coordinate axes. Areas with seafloor reflection signals were visually identified and cropped according to the along-track distance range corresponding to these areas. This completed the photon data cleaning process and generated shallow-water photon data.

[0082] Step S2: extracting sea surface photon data and water body photon data from the shallow sea section photon data.

[0083] In a specific embodiment of the present invention, extracting sea surface photon data and water body photon data from shallow sea section photon data includes:

[0084] Step S2.1: Set the width of the first elevation segment, project the shallow sea photon data according to the elevation, count the number of photons in each first elevation segment, and form an elevation distribution histogram, as shown in Figure 2 As shown;

[0085] Step S2.2: Use the bimodal Gaussian distribution fitting algorithm to fit the elevation distribution histogram, and then extract the sea surface photon data and water body photon data.

[0086] In this embodiment, the width of the first elevation segment is 0.1m. The size of each first elevation segment is:

[0087]

[0088] Among them, V g Indicates the size of the g-th first elevation segment, H 1g Indicates the elevation range of the g-th first elevation segment, n g represents the number of photons in the first elevation segment of the gth order. The expression of the bimodal Gaussian function is:

[0089]

[0090] Among them, f g(h) represents a double-peak Gaussian function; h represents the photon elevation; A1 and A2 represent the amplitudes of the double peaks, which are approximately the maximum number of sea surface photons and the maximum number of seabed photons falling within the first elevation segment; μ1 and μ2 represent the expectations of the double peaks (μ2 < μ1), that is, the approximate sea surface elevation and seabed elevation; σ1 and σ2 represent the standard deviations of the double peaks, that is, the standard deviation of the sea surface photon peak and the standard deviation of the seabed photon peak, respectively.

[0091] The photon noise above the sea surface is: h>μ1+2σ1;

[0092] The photon data of the sea surface is: μ1-2σ1≤h≤μ1+2σ1;

[0093] The photon data of water body is: h<μ1-2σ1;

[0094] The upper and lower boundaries of sea surface photons are: H max =μ1+2σ1,H min =μ1-2σ1, where H max Indicates the upper boundary of the sea surface photon data, H min Indicates the lower boundary of the sea surface photon data.

[0095] The standard deviation σ2 is used to characterize the signal-to-noise ratio of photon data: when σ2≤1, the signal-to-noise ratio is high, and when σ2>1, the signal-to-noise ratio is low.

[0096] After the sea surface photons are extracted, the remaining water body photons also include seabed photons and noise, such as Figure 3 shown.

[0097] Step S3: Construct a mathematical model of the elliptical filter kernel according to the along-track distance and elevation of the photons, and initialize the major and minor semi-axes of the elliptical filter kernel.

[0098] In this embodiment, the mathematical model of the elliptical filter kernel is expressed as:

[0099]

[0100] Among them, dist(p,q) represents the distance between photon p and photon q, a represents the major semi-axis of the elliptical filter kernel, b represents the minor semi-axis of the elliptical filter kernel, and d p d q They represent the distances along the track of photon p and photon q, respectively, and h p 、h q They represent the elevations of photon p and photon q respectively.

[0101] There are two ways to initialize the major and minor axes of the elliptical filter kernel. The first is to set the initial values ​​of the major and minor axes of the elliptical filter kernel based on experience. The second is to calculate the initial values ​​of the major and minor axes of the elliptical filter kernel based on sea surface photon data. Using the first method, when processing different ICESat-2 / ATL03 photon data, it is necessary to manually set appropriate empirical values ​​each time, which cannot be widely used. Using the second method, the initial values ​​of the major and minor axes can be adaptively calculated based on the extracted sea surface photon data without the need for empirical value setting. It has a wide range of applications, takes into account spatial correlation, and has higher accuracy. Therefore, the embodiment of the present invention calculates the initial values ​​of the major and minor axes of the elliptical filter kernel based on sea surface photon data, specifically including:

[0102] Step S3.1: Divide the sea surface photon data into n segments along the track according to the step size Δd, and calculate the maximum elevation difference ΔSH of each first segment along the track i , that is, the difference between the maximum elevation and the minimum elevation in each first track segment.

[0103] Step S3.2: Calculate the initial values ​​of the major and minor axes of the elliptical filter kernel. The specific formula is:

[0104] b0=|H max -H min | (4)

[0105]

[0106] Among them, b0 represents the initial value of the short semi-axis of the elliptical filter kernel, H max Indicates the upper boundary of the sea surface photon data, H min represents the lower boundary of the sea surface photon data, a0 represents the initial value of the major semi-axis of the elliptical filter kernel, R ab represents the shape adjustment factor of the elliptical filter kernel, Δd represents the step size, ΔSH i represents the maximum elevation difference of the first track segment i. In this embodiment, the step length Δd is 7m.

[0107] Step S4: Calculate the first density value of each photon in the water body photon data according to the initial values ​​of the major semi-axis and the minor semi-axis and the mathematical model of the elliptical filter kernel.

[0108] First, substitute the initial values ​​of the major and minor semi-axis calculated by formulas (4) and (5) into formula (3). Then, calculate the distance dist(p,q) between each photon p and other photons q in the water body photon data. Count the number of photons whose distance dist(p,q) does not exceed a first distance threshold. The first density value of photon p is the number of photons whose distance dist(p,q) does not exceed the first distance threshold. In this embodiment, the first distance threshold is 1 meter.

[0109] Step S5: Determine a first density threshold based on the water body photon data and the initial values ​​of the major and minor semi-axes.

[0110] In this embodiment, the formula for determining the first density threshold is:

[0111]

[0112]

[0113] Among them, D T1 represents the first density threshold, SN1 represents the signal photon distribution density within the initial unit elliptical kernel area, SN2 represents the noise photon distribution density within the initial unit elliptical kernel area, M represents the number of photons in the water body photon data, ΔH represents the vertical distance between the highest photon and the lowest photon in the water body photon data, ΔL represents the difference between the along-track distance of the leftmost photon and the along-track distance of the rightmost photon in the water body photon data (i.e., the difference between the maximum along-track distance and the minimum along-track distance), m l Represents the number of photons within the elevation range of the noise photon distribution in the water body photon data, N H Indicates the elevation range of the noise photon distribution in the set water body photon data. In this embodiment, N H Set to 5m.

[0114] Step S6: filtering the water body photon data according to the first density threshold and the first density value of each photon.

[0115] The photons with a first density value less than the first density threshold are filtered out to achieve global filtering of the water body photon data, such as Figure 4 shown. Figure 4 In the figure, the ellipse is the elliptical filter kernel, the blue dots are the noise photons filtered out by one filter, the brown dots are the water photons retained after one filter, and the two straight lines represent the elevation range N of the noise photon distribution. H .

[0116] Step S7: updating the major semi-axis and the minor semi-axis according to the elevation of each photon in the water body photon data after the single filtering; calculating the second density value of the corresponding photon in the water body photon data after the single filtering according to the updated major semi-axis and minor semi-axis and the mathematical model of the elliptical filter kernel.

[0117] For the photon p in the water body photon data after one filtering, the update formulas of the major and minor axes are:

[0118] b=b0+τ|h p -H min |,a=R ab b (8)

[0119] Where b represents the updated minor semi-axis, b0 represents the initial value of the minor semi-axis of the elliptical filter kernel, τ represents the scaling factor of the ellipse (τ>0), and h p It represents the elevation of the pth photon in the water body photon data after one filtering, H min represents the lower boundary of the sea surface photon data, a represents the updated major semi-axis, R ab Represents the shape adjustment factor of the elliptical filter kernel. R ab >1, shape adjustment factor R ab It can be set based on experience or calculated according to formula (5).

[0120] Substitute the major semi-axis a and minor semi-axis b calculated by formula (8) into formula (3) to obtain the elliptical filter kernel centered on photon p, and then calculate the second density value of photon p: use the elliptical filter kernel centered on photon p to calculate the distance dist(p,q) between photon p and other photons q in the water body photon data after one filtering, and count the number of photons whose distance dist(p,q) does not exceed the first distance threshold. Then the second density value of photon p is the number of photons whose distance dist(p,q) does not exceed the first distance threshold. Figure 5 The elliptical filter kernel corresponding to different photons is shown. Figure 5 It can be seen that the elliptical filter kernel increases with the increase of water depth (ie, the difference between the elevation of the photon and the lower boundary of the sea surface photon data).

[0121] The present invention adaptively updates the major and minor semi-axes according to formula (8), and then generates an adaptive elliptical filter kernel. The adaptive elliptical filter kernel is used to calculate the density value of photons (i.e., the second density value). The complex and unknown nonlinear relationship between photon density and water depth that cannot be directly expressed by a formula is taken into account, which is beneficial to the filtering processing of photon data with low signal-to-noise ratio.

[0122] Step S8: Calculate the second density threshold of each second elevation segment according to the first density value of each photon.

[0123] In a specific embodiment of the present invention, calculating the second density threshold in each second elevation segment according to the first density value of each photon includes:

[0124] Step S8.1: Set the width of the second elevation segment, project the filtered water body photon data according to the elevation, count the first density values ​​of all photons in each second elevation segment, and sort the first density values ​​in descending order.

[0125] Figure 6 Each second elevation segment is shown, each rectangle is a second elevation segment, and the width of each second elevation segment is 1m; Figure 7 Each second elevation segment S is shown kThe median of the first density value and the second density threshold, where the green line represents the corresponding second elevation segment S k The median of the first density value within the red line indicates the corresponding second elevation segment S k The second density threshold.

[0126] Step S8.2: Calculate the maximum inter-class variance method (OTSU algorithm) for each second elevation segment S k The initial density threshold includes:

[0127] Calculate the between-class variance between the “signal” photons and the “noise” photons. The specific formula is:

[0128]

[0129]

[0130]

[0131]

[0132] μ(t)=w s (t)×μ s (t)+w n (t)×μ n (t) (13)

[0133] (σ(t)) 2 =w s (t)×(μ s (t)-μ(t)) 2 +w n (t)×(μ n (t)-μ(t)) 2 (14)

[0134] Among them, w s (t) represents the proportion of “signal” photons; t represents the number of “signal” photons; N represents the number of photons in the water body photon data after one filtering; w n (t) represents the proportion of “noise” photons; μ s (t) represents the density mean of “signal” photons; den(i) represents the second elevation segment S k The first density value of the photon in the i-th row; μ n (t) represents the density mean of “noise” photons; μ(t) represents the density mean of water photon data after one filtering; (σ(t)) 2 Represents the between-class density variance of “signal photons” and “noise” photons.

[0135] According to the descending order of the first density values ​​of all photons in each second elevation segment, the value of t starts from 1 and increases by 1 each time until t is N. When t = 1, den(t) is the first density value of the photon ranked first in the second elevation segment. According to formulas (9) to (14), the inter-class density variance at t = 1 can be calculated; when t = 2, den(t-1) and den(t) are the first density values ​​of the photons ranked first and second in the second elevation segment. According to formulas (9) to (14), the inter-class density variance at t = 2 can be calculated; and so on, according to formulas (9) to (14), the inter-class density variance when t is 1 to N can be calculated.

[0136] The maximum inter-class density variance is determined from the inter-class density variance when t is 1 to N, thereby determining t corresponding to the maximum inter-class density variance. At this time, the first density value den(t) of the t-th photon in the second elevation segment is the initial density threshold of the second elevation segment.

[0137] Step S8.3: Calculate the second density threshold of the corresponding second elevation segment based on the initial density threshold of each second elevation segment and the initial density threshold of its adjacent second elevation segment. The specific formula is:

[0138]

[0139] Among them, D T2 Indicates the second elevation segment S k The second density threshold, Indicates the second elevation segment S k The initial density threshold, Indicates the second elevation segment S k Two adjacent second elevation segments S k-1 、S k+1 The initial density threshold.

[0140] Usually, the second density of photons and the second density threshold are calculated by establishing a linear relationship between water depth and photons. However, as the water depth increases, the size of the elliptical filter kernel also increases, which will cause the noise in the deep water in the water body photon data to reach the equivalent density of the signal photons in the shallow water, and at the same water depth, the density of signal photons and noise photons presents diversity. Therefore, using the linear relationship between water depth and photons for processing ignores the variability and diversity of the spatial distribution of photons, leading to missed detection and false detection problems. Since it is impossible to use a formula to express the nonlinear relationship between the unknown spatial distribution of photons and water depth, the present invention segments the water depth (i.e., the second elevation segmentation), calculates the density threshold (i.e., the second density threshold) based on the signal photon density and noise photon density in each water depth segment, establishes a nonlinear relationship between photon density and water depth, can better reflect the density difference between signal photons and noise photons in each water depth segment, improves the accuracy of filtering, and improves missed detection and false detection problems.

[0141] Step S9: Calculate a third density threshold according to the updated major semi-axis.

[0142] Considering that the elliptical filter kernel increases with the increase of water depth, which may introduce noise photons in deep water, a third density threshold guided by the major semi-axis in the along-track distance direction is added. The specific formula is:

[0143]

[0144] Among them, D T3 Indicates the third density threshold, int indicates the rounding function, N sm Indicates that in the along-track direction d sm The number of sea surface photons contained in the window of meters. According to experience, d sm Set to 1000, N sm Set to 500.

[0145] Step S10: performing secondary filtering on the water body photon data after the primary filtering according to the second density threshold, the third density threshold and the second density value of each photon to obtain seabed topography photon data.

[0146] If the second density value of the photon in the water body photon data after the first filter is not less than the corresponding second density threshold and third density threshold, the photon is a signal photon; otherwise, it is a noise photon. The noise photons are filtered out from the water body photon data after the first filter to obtain the seabed topography photon data, such as Figure 8 shown.

[0147] Step S11: using a bidirectional seed point growth algorithm to supplement the seabed topography photon data to obtain the final seabed topography photon data.

[0148] Considering the geographic diversity of photons in water-body photon data, the density distribution of photons along the track varies within the same water depth segment. This results in the inability to fully extract the seabed topography photons of different densities within the same water depth segment (i.e., some signal photons are filtered out, resulting in discontinuous seabed topography photon data). This, in turn, leads to missed detections in the seabed topography photon data obtained through secondary filtering. To address this issue, a bidirectional seed point growing algorithm, based on prior knowledge of the continuity of the spatial distribution of seabed topography, is used to supplement the seabed topography photon data, resulting in complete and continuous seabed topography photon data.

[0149] In a specific embodiment of the present invention, a bidirectional seed point growing algorithm is used to supplement the seabed topography photon data, including:

[0150] Step S11.1: Calculate the difference in along-track distance between every two adjacent photons in the seabed topography photon data. The specific formula is:

[0151] Δd ij =|d j -d i | (17)

[0152] Where Δd ij represents the difference in the distance along the track between two adjacent photons i and j, d j represents the distance along the track of photon j, d i represents the distance along the track of photon i.

[0153] Step S11.2: Calculate the second distance threshold based on the difference in along-track distance between every two adjacent photons. The specific formula is:

[0154] T d2 =mean i≠j (Δd ij )+stb i≠j (Δd ij ) (18)

[0155] Among them, T d2 Indicates the second distance threshold, mean i≠j (Δd ij ) represents the mean of the difference in along-track distances between all two adjacent photons i and j in the seabed topography photon data, stb i≠j (Δd ij ) represents one standard deviation of the difference in along-track distances between all two adjacent photons i and j in the seabed topography photon data, mean represents the mean function, and stb represents the one standard deviation function.

[0156] Step S11.3: Determine a discontinuous region according to the difference in along-track distances between every two adjacent photons and a second distance threshold.

[0157] When the difference Δd between the distances along the track of two adjacent photons i and j is ij Greater than the second distance threshold T d2 When , there is a discontinuous region between photons i and j, such as Figure 9 As shown in the figure, for the seafloor topography photon data, there are no other photons between photons i and j. However, for the water body photon data after single filtering, there are other photons between photons i and j. A bidirectional seed point growth algorithm is used to add the photons that meet the conditions between photons i and j to the seafloor topography photon data to solve the problem of missed detection.

[0158] Step S11.4: For each discontinuous region, use one of the two adjacent photons, photon i, as the initial seed point and use the KD tree search algorithm to search for the nearest neighbor photon in the direction of the other photon j to obtain a candidate photon;

[0159] Step S11.5: Determine whether the candidate photon is a signal photon or a noise photon; when the candidate photon is a signal photon, add the signal photon to the seabed topography photon data, and use the signal photon as a new seed point, repeat steps S11.4 and S11.5 until the seed point exceeds the discontinuous area; when the candidate photon is a noise photon, complete the processing of one side (left side) of the discontinuous area, such as Figure 10 As shown;

[0160] Step S11.6: For each discontinuous region, use the other photon j of the two adjacent photons as the initial seed point and use the KD tree search algorithm to search for the nearest neighbor photon in the direction of one of the photons i to obtain a candidate photon;

[0161] Step S11.7: Determine whether the candidate photon is a signal photon or a noise photon; when the candidate photon is a signal photon, add the signal photon to the seabed topography photon data, and use the signal photon as a new seed point, repeat steps S11.6 and S11.7 until the seed point exceeds the discontinuous area; when the candidate photon is a noise photon, complete the processing of the other side (right side) of the discontinuous area, such as Figure 11 shown.

[0162] In step S11.5 or step S11.7, when σ2≤1, the alternative photon is a signal photon; when σ2>1 and the elevation difference between the alternative photon and the seed point is less than or equal to the first distance threshold, the alternative photon is a signal photon; when σ2>1 and the elevation difference between the alternative photon and the seed point is greater than the first distance threshold, the alternative photon is a noise photon.

[0163] Step S11.8: Merge the seabed topography photon data obtained in step S11.5 and step S11.7, and perform the merged seabed topography photon data (such as Figure 12As shown) along the track is divided into multiple sections to obtain multiple second along-track sections;

[0164] Step S11.9: For each second track segment, divide it into multiple third elevation segments according to elevation (e.g. Figure 13 ), count the number of photons in each third elevation segment, and find the third elevation segment with the largest number of photons;

[0165] Step S11.10: If the photon count of the third elevation segment with the largest photon count is greater than twice the photon count of its two adjacent third elevation segments, retain the third elevation segment with the largest photon count and discard the two adjacent third elevation segments of the third elevation segment with the largest photon count;

[0166] If the number of photons in the third elevation segment with the largest number of photons is less than or equal to twice the photon data of its two adjacent third elevation segments, the width of the third elevation segment is reduced and the process goes to step S11.9 until a third elevation segment with a photon number greater than twice the photon data of its two adjacent third elevation segments is found.

[0167] When the width of the third elevation segment is less than 0.2 m, and a third elevation segment having a photon number greater than twice the photon data of its two adjacent third elevation segments is not found, the second along-track segment is discarded.

[0168] Step S11.11: Merge all the second along-track segments obtained in step S11.10 to obtain the final seabed topography photon data, such as Figure 14 shown.

[0169] Considering that each water depth segment contains signal photons and noise photons of comparable density, the density threshold of the photons within that water depth segment filters out some signal photons to accurately filter out noise, resulting in discontinuous extraction of seabed topography photons. A bidirectional seed point growth algorithm is used to supplement the discontinuous regions in the seabed topography photon data obtained by double filtering, resulting in more complete, continuous, and accurate seabed topography photons.

[0170] The method of the present invention is used to extract seabed topography photon data from 4 ICESat-2 / ATL03 photon data, such as Figures 15-18 As shown. Figures 15-18 It can be seen that for the four ICESat-2 / ATL03 photon data, the present invention can obtain accurate, complete and continuous seabed topography signal photon results.

[0171] Table 1 Comparison of the present invention and three existing filtering methods

[0172]

[0173] In Table 1, the R index represents the recall rate, the P index represents the precision rate, and the F index represents the balance index between the recall rate and the precision rate. The larger the balance index, the better the extraction effect.

[0174] The method of the present invention was compared with three filtering methods: AVEBM (Adaptive Variable Ellipse Filtering Bathymetric Method), bimodal Gaussian fitting, and quadtree segmentation, as shown in Table 1. As shown in Table 1, the F-index value of the extraction result of the method of the present invention is the highest, indicating that the method of the present invention has the best extraction effect on the seabed topography photon data.

[0175] The present invention uses the same adaptive filter as the AVEBM method, but AVEBM introduces some noise photons around the seabed topography, resulting in insufficient filtering; the bimodal Gaussian fitting method encounters a sparse distribution of topographic signal photons due to the weakening effect of water on the laser in deep water areas, which will introduce more noise photons, and also performs poorly for situations with large terrain fluctuations; the result of the quadtree segmentation method is obviously a filtering wave, which only retains high-density signal photons, filters out low-density signal photons, and also retains high-density noise photons, resulting in sparse and discrete filtering results. The present invention significantly avoids the above-mentioned shortcomings, performs elevation segmentation on the water depth, and the adaptive density threshold (i.e., the second density threshold) is a fitting process based on the nonlinear relationship between water depth and photon density, which improves the accuracy of identifying and extracting seabed topography photons with diverse density distributions under different water depth segments, and uses a seed point growth algorithm to ensure the integrity of the seabed topography, and ultimately extracts accurate, complete, and continuous seabed topography photons.

[0176] Example 2

[0177] An embodiment of the present invention also provides an electronic device, which includes: a memory, a processor, and a computer program / instruction stored in the memory, and the processor executes the computer program / instruction to implement the seabed topography photon extraction method in the embodiment of the present application.

[0178] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in a read-only memory (ROM) or programs and / or data loaded from a storage portion into a random access memory (RAM). The processor can be a multi-core processor or can include multiple processors. In some embodiments, the processor can include a general-purpose main processor and one or more special coprocessors, such as a central processing unit, a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), etc. Various programs and data required for device operation are also stored in RAM. The processor, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0179] The processor and memory are used together to execute the program / instructions stored in the memory. When the program / instructions are executed by the computer, the methods, steps or functions described in the above embodiments can be implemented.

[0180] Although not shown, an embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the method for extracting seabed topography photons in the embodiment of the present application.

[0181] Storage media in embodiments of the present invention include permanent and non-permanent, removable and non-removable items that can be used to store information using any method or technology. Examples of storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0182] Computer-readable storage media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0183] Although not shown, an embodiment of the present invention further provides a computer program product, including: a computer program / instruction, which, when executed by a processor, implements the seabed topography photon extraction method in the embodiment of the present application.

[0184] The above disclosure is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, and they should all be covered by the scope of protection of the present invention.

Claims

1. A method for extracting photons from seabed topography, characterized in that: The extraction method comprises: Preprocess ICESat-2 / ATL03 photon data to obtain shallow sea photon data; Extracting sea surface photon data and water body photon data from the shallow sea section photon data; Constructing a mathematical model of an elliptical filter kernel according to the along-track distance and elevation of the photons, and initializing the major semi-axis and the minor semi-axis of the elliptical filter kernel; Calculating a first density value of each photon in the water body photon data according to the initial values ​​of the major semi-axis and the minor semi-axis and a mathematical model of an elliptical filter kernel; Determining a first density threshold according to the water body photon data and initial values ​​of the major semi-axis and the minor semi-axis; Performing a primary filtering on the water body photon data according to the first density threshold and a first density value of each photon; updating the major semi-axis and the minor semi-axis according to the elevation of each photon in the water body photon data after the primary filtering; calculating the second density value of the corresponding photon in the water body photon data after the primary filtering according to the updated major semi-axis and the minor semi-axis and the mathematical model of the elliptical filter kernel; calculating a second density threshold for each second elevation segment based on the first density value of each photon; Calculate the third density threshold based on the updated major semi-axis; The water body photon data after the primary filtering is subjected to secondary filtering according to the second density threshold, the third density threshold and the second density value of each photon to obtain seabed topography photon data.

2. The method for extracting seabed topography photons according to claim 1, characterized in that: Extracting sea surface photon data and water body photon data from the shallow sea section photon data includes: Setting the width of the first elevation segment, projecting the shallow sea segment photon data according to the elevation, and counting the number of photons in each first elevation segment to form an elevation distribution histogram; A bimodal Gaussian distribution fitting algorithm is used to fit the elevation distribution histogram, and then the sea surface photon data and the water body photon data are extracted.

3. The method for extracting seabed topography photons according to claim 1, characterized in that: Initializing the major semi-axis and the minor semi-axis of the elliptical filter kernel according to the sea surface photon data, including: Dividing the sea surface photon data into n segments along the track according to the step length, and calculating the maximum elevation difference of each first segment along the track; Calculate the initial values ​​of the major and minor axes of the elliptical filter kernel. The specific formula is: b0=|H max -H min |; a0=R ab b0, Among them, b0 represents the initial value of the short semi-axis of the elliptical filter kernel, H max Indicates the upper boundary of the sea surface photon data, H min represents the lower boundary of the sea surface photon data, a0 represents the initial value of the major semi-axis of the elliptical filter kernel, R ab represents the shape adjustment factor of the elliptical filter kernel, Δd represents the step size, ΔSH i Indicates the maximum elevation difference of the i-th first along-track segment.

4. The method for extracting seabed topography photons according to claim 1, characterized in that: The specific process of calculating the first density value or the second density value of each photon includes: Calculating the distance between each photon and other photons according to the mathematical model of the elliptical filter kernel; A first distance threshold is set; for each photon, the number of photons whose distance does not exceed the first distance threshold is counted, where the number of photons is the first density value or the second density value of the photon.

5. The method for extracting seabed topography photons according to claim 1, characterized in that: The formula for determining the first density threshold is: Among them, D T1 represents the first density threshold, SN1 represents the signal photon distribution density within the initial unit elliptical kernel area, SN2 represents the noise photon distribution density within the initial unit elliptical kernel area, a0 represents the initial value of the major semi-axis of the elliptical filter kernel, b0 represents the initial value of the minor semi-axis of the elliptical filter kernel, M represents the number of photons in the water body photon data, ΔH represents the vertical distance between the highest photon and the lowest photon in the water body photon data, ΔL represents the difference between the maximum along-track distance and the minimum along-track distance in the water body photon data, m l Represents the number of photons within the elevation range of the noise photon distribution in the water body photon data, N H Indicates the elevation range of the noise photon distribution in the set water body photon data.

6. The method for extracting seabed topography photons according to claim 1, characterized in that: The updating formulas of the major and minor semi-axis are: b=b0+τ|h p -H min |,a=R ab b; Among them, b represents the updated minor semi-axis, b0 represents the initial value of the minor semi-axis of the elliptical filter kernel, τ represents the scaling factor of the ellipse, and h p represents the height of the p-th photon, H min represents the lower boundary of the sea surface photon data, a represents the updated major semi-axis, R ab Represents the shape adjustment factor of the elliptical filter kernel.

7. The method for extracting seabed topography photons according to claim 1, characterized in that: Calculating a second density threshold within each second elevation segment according to the first density value of each photon, including: Set the width of the second elevation segment, project the filtered water body photon data according to the elevation, count the first density values ​​of all photons in each second elevation segment, and sort the first density values ​​in descending order; The maximum inter-class variance method is used to calculate the initial density threshold of each second elevation segment; The second density threshold of the corresponding second elevation segment is calculated according to the initial density threshold of each second elevation segment and the initial density thresholds of its adjacent second elevation segments.

8. The method for extracting seabed topography photons according to any one of claims 1 to 7, characterized in that: The extraction method further includes using a bidirectional seed point growth algorithm to supplement the seabed topography photon data, specifically including: Calculating the difference in along-track distances between every two adjacent photons in the seabed topography photon data; Calculating a second distance threshold according to the difference in along-track distances between every two adjacent photons; Determine a discontinuous area according to the difference in along-track distances between every two adjacent photons and a second distance threshold; For discontinuous areas, one of the two adjacent photons is used as the initial seed point, and the nearest neighbor photon search is performed in the direction of the other photon to obtain the candidate photon; Determine whether the candidate photon is a signal photon or a noise photon; when the candidate photon is a signal photon, add the signal photon to the seabed topography photon data, and use the signal photon as a new seed point, repeat the steps of nearest neighbor photon search and candidate photon determination until the seed point exceeds the discontinuous area; when the candidate photon is a noise photon, complete the processing of one side of the discontinuous area; Taking the other photon of the two adjacent photons as the initial seed point, a nearest neighbor photon search is performed in the direction of one of the photons to obtain a candidate photon; Determine whether the candidate photon is a signal photon or a noise photon; when the candidate photon is a signal photon, add the signal photon to the seabed topography photon data, and use the signal photon as a new seed point, repeat the steps of nearest neighbor photon search and candidate photon determination until the candidate photon exceeds the discontinuous area; when the candidate photon is a noise photon, complete the processing on the other side of the discontinuous area; Merging the seabed topography photon data obtained after processing on both sides of the discontinuous area, and dividing the merged seabed topography photon data into multiple segments along the track to obtain multiple second along-track segments; For each of the second along-track segments, segment the segments according to the elevation to obtain a plurality of third elevation segments, count the number of photons in each third elevation segment, and find the third elevation segment with the largest number of photons; Determine whether the photon number of the third elevation segment with the largest photon number is greater than twice the photon data of its two adjacent third elevation segments. If so, retain the third elevation segment with the largest photon number and discard the two adjacent third elevation segments of the third elevation segment with the largest photon number. If not, reduce the width of the third elevation segment and repeat the steps of segmenting and judging according to the elevation until a third elevation segment with a photon number greater than twice the photon data of its two adjacent third elevation segments is found. All the processed second along-track segments are merged to obtain the final seabed topography photons.

9. An electronic device comprising a memory, a processor, and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the method for extracting photons from seabed topography according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the method for extracting photons from seabed topography according to any one of claims 1 to 8 is implemented.

11. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the method for extracting photons from seabed topography according to any one of claims 1 to 8 is implemented.

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