Spaceborne photon counting laser radar point cloud denoising method, device and storage medium

Abstract

The present invention discloses a method, device and storage medium for denoising a point cloud of a spaceborne photon counting laser radar. The denoising method comprises: segmenting and delimiting windows in a computing unit along the track direction, and calculating the maximum difference window; taking a window above and below the signal window, defining these three windows as potential signal windows, and removing the noise outside the potential signal window as the first step of coarse denoising; using the calculated average noise density and the product of the circular area as the minimum number of neighborhood points of the DBSCAN algorithm, and performing the second step of coarse denoising on the potential signal window photons; constructing the minimum number of neighborhood points under the influence of different signal-to-noise ratios, and performing fine denoising using a density-based spatial clustering method. The present invention not only considers the signal-to-noise ratio as a dynamic adjustment parameter, but also combines the data distribution characteristics. The method can be adaptively adjusted in mountainous areas with large terrain slopes and high vegetation coverage, reducing the subjectivity brought by artificial determination of clustering thresholds.

Description

Technical Field

[0001] The invention belongs to the field of ecological remote sensing, and in particular relates to a satellite-borne photon counting laser radar point cloud denoising method based on dynamic signal-to-noise ratio and data distribution characteristics. Background Art

[0002] The ICESat-2 space-borne lidar satellite uses multi-beam micro-pulse photon counting altimetry technology. Because its single-photon ranging mode has a higher detection frequency and sensitivity, it is currently widely used in vegetation height measurement in forest areas. The complex mountainous terrain and dense vegetation cover lead to a reduction in the amount of signal, which is the main factor that significantly increases the difficulty of point cloud denoising. However, traditional algorithms and improved algorithms are not well adaptable to special terrain. Therefore, constructing a point cloud denoising algorithm suitable for mountainous dense forest areas is a frontier and hot issue in current research at home and abroad. The use of density-based spatial clustering algorithm is one of the effective denoising algorithms, but the elliptical domain search range is fuzzy, and the artificial determination of the minimum neighborhood point number threshold is highly subjective and empirical. To this end, a new technical solution needs to be designed to solve it. Summary of the invention

[0003] The purpose of the present invention is to provide a space-borne photon counting lidar point cloud denoising method based on dynamic signal-to-noise ratio and data distribution characteristics, which utilizes the characteristics of signal-to-noise density difference to estimate the terrain slope of adjacent segments and thus optimize the elliptical search range; based on the spatial distribution characteristics of photons, an equation for determining the minimum number of neighborhood points under the influence of different signal-to-noise ratios is constructed, thereby solving the aforementioned problems existing in the prior art.

[0004] In order to achieve the above object, the present invention specifically adopts the following technical solutions:

[0005] A method for denoising a point cloud of a spaceborne photon counting laser radar based on dynamic signal-to-noise ratio and data distribution characteristics, characterized in that it comprises the following steps:

[0006] Step 1: Divide the calculation unit range of 1 km along the track into 5 200-meter segment intervals. In each segment interval, every 50 meters along the elevation direction is used as a window to calculate and count the adjacent windows with the maximum photon number difference. ;

[0007] Step 2, in the signal window Take a window above and below as the potential signal window , removing the noise outside the potential signal window is used as the first step of coarse denoising.

[0008] Step 3: Using the average noise density The product of the circle area is used as the minimum number of neighborhood points for the DBSCAN algorithm. Perform the second step of coarse denoising;

[0009] Step 4, normalize the signal-to-noise ratio, based on the empirical rule theory, introduce the signal-to-noise ratio As a dynamic adjustment parameter, it adapts to the window signal strength. The value is refined for denoising.

[0010] In step 1, the segment interval, window, and maximum difference window of each 1km calculation unit are defined, and the specific steps include:

[0011] Step 11: Set the segment interval and divide the single computing unit into five 200-meter segment intervals, denoted as (in ).

[0012] Step 12: In each segment In the definition window is (in , and the height interval of each window is 50 meters).

[0013] Step 13, calculate the difference in the number of photons in adjacent windows. and , the difference in the number of photons in adjacent windows can be expressed as:

[0014]

[0015] In the formula, and The windows are and The number of photons in Represents the absolute value of the difference in the number of photons in adjacent windows.

[0016] Step 14, repeat step 13 in each 200-meter segment. Calculate the difference in the number of photons in each window and find the largest difference:

[0017]

[0018] In the formula, Represents the maximum absolute value of the difference in the number of photons in adjacent windows, Indicates the window label corresponding to the maximum absolute value of the difference in the number of photons in adjacent windows.

[0019] We will label it as The window is defined as the signal window .

[0020] In step 2, the potential signal window and the potential noise window are divided, the noise density and the signal density are calculated, and the minimum number of neighborhood points of the spatial clustering algorithm is preliminarily set. , complete the first step of coarse denoising, the specific steps include:

[0021] Step 21. In the window labeled Signal window Take a window above and below and , these three windows are defined as potential signal windows (Potential Signal Window, PSW); the window outside the potential signal window is regarded as potential noise window (Potential Noise Window, PNW), which is defined as:

[0022]

[0023] In the formula, is the potential signal window, is the potential noise window;

[0024] Assume that the total number of photons in the first segment is , the total number of photons in the potential signal window is , the total number of photons in the potential noise window is , defined as:

[0025]

[0026] In the formula, This is the number of remaining photons after the first step of coarse denoising. That is the number of noise photons removed in the first step;

[0027] Step 22, set Potential noise window The number of noise photons in , the area of ​​the window is ; Then, the window The noise density is the number of noises per unit area. for:

[0028] For all potential noise windows, the number of potential noise windows is , the average value of the noise density It can be expressed as:

[0029]

[0030] Similarly, Potential signal window The number of signals per unit area is the signal density , the area of ​​the window is ; Then, the window The number of signals per unit area is the signal density for:

[0031] For all potential signal windows, the average value of the signal density It is expressed as:

[0032]

[0033] In the formula, They are the labels of the potential signal windows;

[0034] Step 23: The number of noise points obtained by multiplying the average noise density by the circle area is used as the minimum number of neighborhood points. , with the minimum number of neighborhood points is the parameter, the minimum number of neighborhood points for:

[0035] In the formula, is the area of ​​the circle;

[0036]

[0037] In the formula, is the radius of the circle.

[0038] Based on the first step of coarse denoising, the DBSCAN clustering algorithm is used for the second step of coarse denoising, and the amount of noise removed is , defined as:

[0039]

[0040] In the formula, is the number of remaining photons after the second step of coarse denoising using the DBSCAN clustering algorithm;

[0041] In step 4, the dynamic minimum number of neighborhood points is calculated based on statistical empirical rules according to the distribution characteristics of photon data and the signal-to-noise ratio. , the specific steps include:

[0042] Step 41, by continuously adjusting the direction angle of the elliptical search domain, count each photon point The maximum number of neighboring photons , divide the quantity into intervals and count the frequency The maximum number of neighborhood photons obtained is Sum frequency Draw a number-frequency histogram and perform Gaussian fitting on the drawn histogram data to obtain the best mean and standard deviation .

[0043] Step 42, specifically, within each 1km calculation unit, calculate the signal-to-noise ratio of each 200m segment interval , and its calculation formula is:

[0044]

[0045] In the formula, and are the average values ​​of signal and noise density in the segmented interval, respectively;

[0046] Step 43, consider the signal-to-noise ratio for clustering parameters The influence of Scaling to between 0 and 1, the calculation formula is as follows:

[0047]

[0048] In the formula, and It indicates the minimum and maximum value of the signal-to-noise ratio within each 1km calculation unit.

[0049] Step 44, use the normalized signal-to-noise ratio to adjust the multiple:

[0050]

[0051] In the formula, is the base value, To control the parameters of the influence of the signal-to-noise ratio, the fold change is proportional to the signal-to-noise ratio and remains within a reasonable range. is 1, is 2.

[0052] Step 45, Keep Adjust within a reasonable range and calculate the dynamics using the following formula :

[0053]

[0054] Step 46, based on the coarse denoising in the second step, the improved DBSCAN algorithm (optimized ellipse search angle, based on the signal-to-noise ratio and data distribution characteristics) is used. Parameters) for fine denoising, the amount of noise removed is , defined as:

[0055]

[0056] In the formula, The number of remaining photons after the third step of fine denoising using the improved DBSCAN clustering algorithm.

[0057] Beneficial effects of the invention: The invention provides a method for denoising a point cloud of a spaceborne photon counting lidar based on dynamic signal-to-noise ratio and data distribution characteristics. First, the potential signal window is found within a 200-meter segmented interval by using the signal-to-noise density difference characteristic, the signal density and noise density are calculated, and the noise outside the potential signal window is removed as the first step of coarse denoising; then, the average noise density is used to calculate the noise density. The product of the circle area is used as the minimum number of neighborhood points for the DBSCAN algorithm. Perform the second step of rough denoising; then calculate the terrain slope of adjacent segments based on the distribution characteristics of photons along the slope direction ,by Limit the ellipse search angle to the interval range, and then optimize the search range of the elliptical domain; finally, based on the empirical rule theory, the signal-to-noise ratio is introduced As a dynamic adjustment parameter, it adapts to the window signal strength. Based on photon density and terrain slope, the present invention realizes the point cloud denoising process under the influence of signal-to-noise ratio, reduces the subjectivity brought by artificial determination of the minimum number of neighborhood points of the clustering threshold, makes up for the defects of point cloud denoising in complex mountainous areas at this stage, and makes the density-based spatial clustering algorithm more universal. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic flow chart of a method for denoising a point cloud of a spaceborne photon counting laser radar based on a dynamic signal-to-noise ratio and data distribution characteristics provided by the present invention;

[0059] Figure 2 It is a division diagram of the computing unit, segmentation interval, and window in a specific embodiment;

[0060] Figure 3 It is the first step rough denoising image of the window photon point cloud in the specific embodiment;

[0061] Figure 4 It is the second step rough denoising image of the window photon point cloud in the specific embodiment;

[0062] Figure 5 It is a refined denoising image of the window photon point cloud in a specific embodiment. DETAILED DESCRIPTION

[0063] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0064] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0065] Example 1

[0066] This embodiment provides a method for denoising a point cloud of a spaceborne photon counting laser radar based on a dynamic signal-to-noise ratio and data distribution characteristics. The denoising flow chart is as follows: Figure 1 As shown, the following steps are included:

[0067] S1. Define the segmentation interval, window and maximum interpolation window of the calculation unit. The specific method is:

[0068] Every 1 km along the track direction is taken as a calculation unit, and each calculation unit is divided into 5 200 m segmented intervals. In each segmented interval, every 50 m along the elevation direction is taken as a window, and the window with the maximum photon number difference is calculated and counted as the signal window.

[0069] The length of the calculation unit, the length of the segment and the elevation data of the window are not limited to 1 km, 200 m and 50 m data. 1 km, 200 m and 50 m are just a specific embodiment.

[0070] Taking the Tunxi River Basin as an example, step S1 includes:

[0071] S1.1. Log in to the ICESat-2 official website and select an ATL03 data file of the Tunxi River Basin for download.

[0072] S1.2. Draw a scatter plot based on the extracted photon height h_ph and along_track_x, divide the scatter plot into 1 km calculation units, 20 m segment intervals, and 50 m windows, and calculate and count the window with the maximum photon number difference as the signal window ,like Figure 2 shown.

[0073] S2. Based on the potential signal window, remove the noise outside the potential signal window as the first step of coarse denoising:

[0074] The specific method of step S2 is:

[0075] In the window labeled Signal window Take a window above and below and , these three windows are defined as potential signal windows (Potential Signal Window, PSW); the window outside the potential signal window is regarded as potential noise window (Potential Noise Window, PNW), which is defined as:

[0076]

[0077] In the formula, is the potential signal window, Potential noise window.

[0078] Assume that the total number of photons in the first segment is , the total number of photons in the potential signal window is , the total number of photons in the potential noise window is , defined as:

[0079]

[0080] In the formula, This is the number of remaining photons after the first step of coarse denoising. This is the number of noise photons removed in the first step.

[0081] After completing the first step of denoising, Figure 3 shown.

[0082] S3, based on the DBSCAN algorithm, the product of noise density and circle area is used as The parameters perform a second step of coarse denoising on the potential signal window photons:

[0083] Step S3 includes:

[0084] Using the calculated average noise density The product of the circle area is used as the minimum number of neighborhood points for the DBSCAN algorithm. The second step of coarse denoising is performed, such as Figure 4 shown.

[0085] S3.1, Set Potential noise window The number of noise photons in , the area of ​​the window is ; Then, the window The noise density is the number of noises per unit area. for:

[0086] For all potential noise windows, the number of potential noise windows is , the average value of the noise density It is expressed as:

[0087]

[0088] Similarly, Potential signal window The number of signals per unit area is the signal density , the area of ​​the window is ; Then, the window The number of signals per unit area is the signal density for:

[0089]

[0090] For all potential signal windows, the average value of the signal density It is expressed as:

[0091]

[0092] In the formula, are the labels of the potential signal windows, respectively.

[0093] S3.2. The number of noise points obtained by multiplying the average noise density by the circle area is taken as the minimum number of neighborhood points. , with the minimum number of neighborhood points is the parameter, the minimum number of neighborhood points for:

[0094] In the formula, is the area of ​​the circle;

[0095]

[0096] In the formula, is the radius of the circle.

[0097] S3.3, with the minimum number of neighborhood points As the clustering parameter, the DBSCAN clustering algorithm is used for the second step of coarse denoising, and the amount of noise removed is ; After the second step, the remaining number of photons is obtained:

[0098] In the formula, It is the number of remaining photons after the second step of coarse denoising using the DBSCAN clustering algorithm.

[0099] S4, introduce the signal-to-noise ratio parameter to construct the The calculation formula is used, and the density-based spatial clustering method is used for fine denoising;

[0100] Step S4 comprises:

[0101] S4.1. Count each photon point by continuously adjusting the direction angle of the elliptical search domain The maximum number of neighboring photons , divide the quantity into intervals and count the frequency The maximum number of neighborhood photons obtained is Sum frequency Draw a number-frequency histogram and perform Gaussian fitting on the drawn histogram data to obtain the best mean and standard deviation .

[0102] S4.2. Specifically, within each 1km calculation unit, calculate the signal-to-noise ratio of each 200m segment. , and its calculation formula is:

[0103]

[0104] In the formula, and are the average values ​​of signal and noise density in the segmented interval, respectively.

[0105] S4.3. Considering the minimum number of neighborhood points for clustering parameters based on signal-to-noise ratio The influence of Scaling to between 0 and 1, the calculation formula is as follows:

[0106]

[0107] In the formula, and They represent the minimum and maximum signal-to-noise ratio in each 1km calculation unit respectively.

[0108] S4.4. Use normalized signal-to-noise ratio to adjust the multiple:

[0109]

[0110] In the formula, is the base value, To control the parameters of the influence of the signal-to-noise ratio, the fold change is proportional to the signal-to-noise ratio and remains within a reasonable range. is 1, is 2.

[0111] S4.5. Dynamically calculate the minimum number of neighborhood points using the following formula :

[0112]

[0113] S4.6, based on the second step of coarse denoising, the minimum number of neighborhood points As the clustering parameter, the DBSCAN algorithm is used for fine denoising, and the amount of noise removed is ; After fine denoising, the final number of remaining photons is obtained:

[0114] In the formula, It is the number of remaining photons after the third step of fine denoising based on the DBSCAN clustering algorithm.

[0115] Maximum number of neighboring photons in step S4.1 The method of determining is:

[0116] The search angle range is determined, and an elliptical neighborhood is used to search for the maximum number of neighboring photon points of each photon point within the search angle range.

[0117] Calculate the terrain slope of adjacent segmented intervals. Assume that two adjacent segmented intervals and , each segment interval has a signal window , the slope of adjacent segment intervals is defined as , that is, the maximum possible distribution angle of signal photons in adjacent signal windows is:

[0118]

[0119] In the formula, and The signal windows corresponding to two adjacent segment intervals are The center height of Indicates the horizontal distance between two adjacent segmented intervals, i.e. 200 meters;

[0120] Since the distribution of signal photons along the slope direction is somewhat random, As the signal photon distribution angle range, the search angle of the elliptical domain is optimized, that is, the optimized search angle range is:

[0121] In the formula, is the search angle range.

[0122] Based on the DBSCAN clustering algorithm, is the center of the ellipse, and its The ellipse distance Defined as:

[0123]

[0124] in,

[0125] In the formula, and are the major and minor axes of the ellipse, respectively. For forest vegetation covered areas, a=6b is usually taken, and for crop growing areas, a=9b is taken. is the direction angle of the elliptical search domain, and Points The horizontal and vertical coordinates of and Points The horizontal and vertical coordinates of .

[0126] By continuously rotating the elliptical domain and calculating the number of photon points at each angle, the maximum number of neighborhood photon points of each photon point in the search interval is explored, thereby improving the accuracy and efficiency of photon point cloud analysis.

[0127] For a given point and angle parameters ,function Indicates the number of points in the data set that meet the following conditions: Belongs to the dataset ;point With point The distance between ) is less than or equal to 1.

[0128]

[0129] In layman's terms, this formula calculates In, all The number of points whose distance does not exceed 1, that is, the number of photon points inside the ellipse.

[0130] Next, define the range of the direction angles:

[0131]

[0132] In the formula, Indicates the minimum value to round down to. Indicates the maximum value rounded up.

[0133] In Angle Points within range The maximum number of neighboring photons in the elliptical search domain The calculation of is as follows:

[0134] In the formula, Indicated in lower point of angle The number of photons in the elliptical search domain.

[0135] Example 2

[0136] This embodiment provides a device for calculating flow generation. The device includes a processor and a memory; the memory stores a program or instruction, and the program or instruction is loaded and executed by the processor to implement the space-borne photon counting laser radar point cloud denoising method of embodiment 1.

[0137] Example 3

[0138] This embodiment provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the satellite-borne photon counting lidar point cloud denoising method of embodiment 1.

[0139] Those skilled in the art can clearly understand that the technical solution of the present invention, in essence or in other words, the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., various media that can store program codes.

[0140] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. A number of improvements and optimizations made by those skilled in the art without departing from the concept of the present invention should be considered as within the scope of protection of the present invention.

Claims

1. A method for denoising point cloud of spaceborne photon counting lidar based on dynamic signal-to-noise ratio and data distribution characteristics, characterized in that: The following steps are involved: Step 1: along the track direction, according to the set distance as the calculation unit, segment and delineate the window in the calculation unit, and calculate the maximum difference window. The maximum difference window label is ; Step 2. In the window labeled Signal window Take a window above and below and , these three windows are defined as potential signal windows, and the noise removal outside the potential signal windows is taken as the first step of coarse denoising; Step 3 uses the calculated average noise density The product of the circle area is used as the minimum number of neighborhood points for the DBSCAN algorithm. , perform the second step of coarse denoising on the potential signal window photons; Step 4: Normalize the signal-to-noise ratio and construct the minimum number of neighborhood points under different signal-to-noise ratios , and use density-based spatial clustering method for fine denoising; In step 1, the method for calculating the maximum difference window is: Calculate the difference in the number of photons in adjacent windows. and , the difference in the number of photons in adjacent windows is expressed as: In the formula, and The windows are and The number of photons in Represents the absolute value of the difference in the number of photons in adjacent windows; Calculate the difference in the number of photons for each window and find the largest difference: In the formula, Represents the maximum absolute value of the difference in the number of photons in adjacent windows, Indicates the window label corresponding to the maximum absolute value of the difference in the number of photons in adjacent windows.

2. The method for denoising a point cloud of a spaceborne photon counting laser radar according to claim 1, characterized in that: In step 1, the distance set in the calculation unit is 1 km; the number of segments in the calculation unit is 5, which is recorded as ,in .

3. The method for denoising a point cloud of a spaceborne photon counting laser radar according to claim 2, characterized in that: The number of windows in the computational unit is: In the definition window is ,in , and the height interval of each window is 50 meters.

4. The method for denoising a point cloud of a spaceborne photon counting laser radar according to claim 1, characterized in that: The step 2 comprises: In the window labeled Signal window Take a window above and below and , these three windows are defined as potential signal windows; the windows outside the potential signal windows are regarded as potential noise windows: In the formula, is the potential signal window, is the potential noise window; Assume that the total number of photons in the first segment is , the total number of photons in the potential signal window is , the total number of photons in the potential noise window is : In the formula, This is the number of remaining photons after the first step of coarse denoising. This is the number of noise photons removed in the first step.

5. The method for denoising a point cloud of a spaceborne photon counting laser radar according to claim 1, characterized in that: The step 3 comprises: Step 31, set Potential noise window The number of noise photons in , the area of ​​the window is ; Then, the window The noise density is the number of noises per unit area. for: For all potential noise windows, assume that the total number of potential noise windows is , the average value of the noise density It is expressed as: Similarly, Potential signal window The number of signals per unit area is the signal density , the area of ​​the window is ; Then, the window The number of signals per unit area is the signal density for: For all potential signal windows, the average value of the signal density It is expressed as: In the formula, They are the labels of the potential signal windows; Step 32: The number of noise points obtained by multiplying the average noise density by the circle area is used as the minimum number of neighborhood points. , minimum number of neighborhood points for: In the formula, is the area of ​​the circle; Step 33, with the minimum number of neighborhood points As the clustering parameter, the DBSCAN clustering algorithm is used for the second step of coarse denoising, and the amount of noise removed is ; After the second step, the remaining number of photons is obtained: In the formula, It is the number of remaining photons after the second step of coarse denoising using the DBSCAN clustering algorithm.

6. The method for denoising a point cloud of a spaceborne photon counting laser radar according to claim 1, characterized in that: The step 4 comprises: Step 41, by continuously adjusting the direction angle of the elliptical search domain, count each photon point The maximum number of neighboring photons , divide the quantity into intervals and count the frequency ; The maximum number of neighborhood photons that will be obtained Sum frequency Draw a number-frequency histogram and perform Gaussian fitting on the drawn histogram data to obtain the best mean and standard deviation ; Step 42, calculate the signal-to-noise ratio of each 200-meter segment interval: In the formula, is the signal-to-noise ratio, and are the average values ​​of signal and noise density in the segmented interval, respectively; Step 43, consider the minimum number of neighborhood points of the clustering parameter based on the signal-to-noise ratio The influence of Scaling to between 0 and 1, the calculation formula is as follows: In the formula, and They represent the minimum and maximum values ​​of the signal-to-noise ratio in each 1km calculation unit respectively; Step 44, use the normalized signal-to-noise ratio to adjust the multiple: In the formula, is the base value, To control the parameters of the influence of the signal-to-noise ratio, the fold change is proportional to the signal-to-noise ratio and remains within a reasonable range; Step 45, Keep Adjust within a reasonable range and calculate the dynamic minimum number of neighborhood points using the following formula : ; Step 46, based on the coarse denoising in the second step, the minimum number of neighborhood points As the clustering parameter, the DBSCAN algorithm is used for fine denoising, and the amount of noise removed is ; After fine denoising, the final number of remaining photons is obtained: In the formula, It is the number of remaining photons after the third step of fine denoising using the DBSCAN clustering algorithm.

7. The method for denoising a point cloud of a spaceborne photon counting laser radar according to claim 6, characterized in that: The maximum number of neighboring photons in step S41 The method of determining is: The slopes between adjacent segment intervals are defined by : In the formula, and The signal windows corresponding to two adjacent segment intervals are The center height of Indicates the horizontal distance between two adjacent segment intervals; According to the defined interval slope, determine the search angle range of the optimization ellipse domain: In the formula, is the search angle range; Based on the DBSCAN clustering algorithm, is the center of the ellipse, and its The ellipse distance is defined as: in, In the formula, and are the major and minor axes of the ellipse, is the direction angle of the elliptical search domain, and Points The horizontal and vertical coordinates of and Points The horizontal and vertical coordinates of By continuously rotating the ellipse and calculating the number of photon points at each angle, the maximum number of neighboring photon points of each photon point in the search interval is explored. The number of photons in the elliptical search domain The calculation formula is: In the formula, represents the photon point in the ellipse domain, Indicated in lower point of angle The number of photons in the elliptical search domain; Define the range of direction angles: In the formula, Indicates the minimum value to round down to. Indicates the maximum value rounded up; In Angle Points within range The maximum number of neighboring photons in the elliptical search domain The calculation of is as follows: In the formula, Indicated in lower point of angle The number of photons in the elliptical search domain.

8. A point cloud denoising device for a spaceborne photon counting laser radar, characterized in that: It includes a processor and a memory; the memory stores a program or instruction, and the program or instruction is loaded and executed by the processor to implement the steps of the satellite-borne photon counting lidar point cloud denoising method as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a program or instruction, wherein the program or instruction, when executed by a processor, implements the steps of the method for denoising a space-borne photon counting lidar point cloud as claimed in any one of claims 1 to 7.

Images