Method for classifying tidal flat lidar point cloud data based on lidar reflection intensity information

Abstract

The present invention relates to the technical field of lidar point cloud data classification, and particularly relates to a method for classifying beach lidar point cloud data based on lidar reflection intensity information. Step 1: Determine the beach collection range and collection time; Step 2: Collect the lidar point cloud data of the flight area to obtain the initial lidar point cloud data; Step 3: Construct an initial triangular sliding window for the initial lidar point cloud data under a single UAV flight strip; Step 4: Dynamically shrink the side length of the sliding window to adapt to the actual distribution characteristics of the sea surface and beach areas; Step 5: Optimize the classification processing of the point cloud data; Step 6: Obtain accurate beach point cloud data; Step 7: Perform iteration of the sliding window; Step 8: Process Steps 3 to 7 for each flight strip to generate the lidar point cloud data of the beach area. Through the analysis of the intensity information difference within the window, the lidar point cloud data noise brought by the residual sea water is extracted to obtain pure beach lidar data.

Description

Technical Field

[0001] The present invention relates to the technical field of lidar point cloud data classification, and particularly to a method for classifying beach lidar point cloud data based on lidar reflection intensity information. Background Art

[0002] In the beach area, due to the variable terrain in the beach area and the relatively soft foundation in the muddy area, for large areas of beaches, traditional manual measurement methods rely on fixed-point measurement. For large areas of beaches, obtaining comprehensive and detailed data not only requires a large amount of manpower, time, and resources, and it is difficult to cover all the detailed features, but also there are safety hazards for the surveyors on the beach.

[0003] An unmanned aerial vehicle (UAV) equipped with a lidar can obtain high-precision beach elevation data when the sea water ebbs. Compared with traditional measurement methods, the UAV equipped with a lidar for measuring the beach has the characteristics of accurately obtaining complex terrains, quickly covering large areas, reducing the time and risk of manual measurement, and having low cost and high safety. However, the beach terrain is uneven, and there will be some residual sea water after the ebb tide. The reflection intensity information contains the reflection intensity information of the residual sea water. The water quality of the sea water is different in each period or each area. Therefore, the sea surface reflection intensity information is also inconsistent. The existing technology lacks a simple method to distinguish the lidar reflection data of the beach and the residual sea surface, which affects the accuracy of the elevation data. Summary of the Invention

[0004] Aiming at the problems existing in the prior art, in order to explore a simple and fast method to distinguish the lidar point cloud data of the beach terrain and the residual sea water, the purpose of the present invention is to propose a method for classifying beach and sea water point cloud data based on lidar reflection intensity, using the difference in lidar reflection intensity to classify and distinguish the lidar point cloud data of the beach and sea water areas, removing the reflection points of the residual sea water, and obtaining more accurate beach elevation data.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is: A method for classifying beach lidar point cloud data based on lidar reflection intensity information, comprising the following steps:

[0006] Step 1: Obtain the maximum range of the beach exposed after the sea water ebbs theoretically according to the isobath and the coastline, obtain the operation time range according to the predicted tide level at the lowest tide, and determine the beach collection range and collection time;

[0007] Step 2: Set the collection flight path. The UAV equipped with a lidar flies over the beach collection range area during the ebb tide of the sea water, collects the lidar point cloud data of the flight area, and obtains the initial lidar point cloud data according to the data calculation software supporting the UAV and the lidar equipment.

[0008] Step 3: Construct an initial triangular sliding window for the initial lidar point cloud data under a single UAV flight strip and set the initial side length.

[0009] Step 4: According to the lidar point cloud data and the lidar reflection intensity information, dynamically shrink the side length of the sliding window to adapt to the actual distribution characteristics of the sea surface and beach areas.

[0010] Step 5: When point cloud data is detected on both sides of the 90° angle of the sliding window, expand along the hypotenuse direction to form a quadrilateral window with the same size as the adjusted sliding window, and optimize the classification processing of the point cloud data.

[0011] Step 6: Inside the quadrilateral window, analyze the difference in reflection intensity between the tidal flat and the sea water based on the lidar reflection intensity information, calculate and extract the boundary line between the tidal flat and the sea surface, classify the point cloud data and filter out noise to obtain accurate tidal flat point cloud data.

[0012] Step 7: After completing the classification of the point cloud data in the current quadrilateral window, the quadrilateral window moves to the next area along the flight strip direction at a preset step length, and repeats the boundary line detection and data classification process until the point cloud data of the entire flight strip is covered, and the sliding window is iterated.

[0013] Step 8: Perform the processing of Steps 3 to 7 for each flight strip. After separately classifying and filtering out impurity points for each flight strip, integrate the point cloud data of all flight strips to generate a single-line strip lidar point cloud dataset of the tidal flat area without impurities, and integrate the single-line strip lidar point cloud dataset of the tidal flat area to generate the lidar point cloud data of the tidal flat area.

[0014] In the above-mentioned method for classifying tidal flat lidar point cloud data based on lidar reflection intensity information, in Step 1, the acquisition time is 80 minutes before to 10 minutes after the lowest tide moment, and the total time is 90 minutes.

[0015] In the above-mentioned method for classifying tidal flat lidar point cloud data based on lidar reflection intensity information, in Step 2, the UAV acquisition route is arranged parallel to the isobath. The length of the acquisition route does not exceed the flight range that the UAV can fly in 90 minutes. The acquisition route is set in the manner from near the coastline to far from the coastline, and then the acquisition route near the isobath is acquired at the lowest tide moment.

[0016] In the above-mentioned method for classifying tidal flat lidar point cloud data based on lidar reflection intensity information, in Step 3, the initial sliding window is an isosceles right triangle. One of the two right sides is perpendicular to the flight strip direction, and the other is parallel to the flight strip direction. The initial side length is set to twice the flight height according to the width of the best point quality point cloud data obtained by the lidar in the ideal field of view angle, L0 = 2h, where h is the flight height.

[0017] The above-mentioned method for classifying tidal flat lidar point cloud data based on lidar reflection intensity information, step 4 includes:

[0018] Step 4-1: Place the initial sliding window at the end of the flight strip, with one 45° angle facing the land side of the flight strip, another 45° angle perpendicular to the flight strip direction, and a 90° right angle facing the seawater side. The initial right-angled side L 0 = 2h;

[0019] Step 4-2: According to the width of the sea surface reflection point cloud, gradually shrink the side length of the sliding window. Through the shrunk side length formula: Calculate the shrunk side length, where is the shrinkage factor, is the width of the seawater point cloud;

[0020] Step 4-3: Stop shrinking until seawater point cloud data is detected on both right-angled sides of the sliding window. Record the actual lengths of the two right-angled sides at this time and , take the larger value and compare it with , and finally determine .

[0021] In the above-mentioned method for classifying tidal flat lidar point cloud data based on lidar reflection intensity information, in step 4-2, define the point cloud range where the reflection intensity is lower than the threshold along the direction perpendicular to the flight strip, , the point cloud data of the single flight strip being processed currently, is the demarcation threshold of the reflection intensity between seawater and tidal flat.

[0022] The above-mentioned method for classifying tidal flat lidar point cloud data based on lidar reflection intensity information, step 5 includes:

[0023] Step 5-1: Based on the triangular window when shrinking stops, copy an identical triangle along the hypotenuse so that the hypotenuses of the two triangles coincide to form a parallelogram window;

[0024] Step 5-2: Calculate the total area of the parallelogram window: , and expand the window coverage range by using the parallelogram window.

[0025] The above-mentioned method for classifying tidal flat lidar point cloud data based on lidar reflection intensity information, step 6 includes:

[0026] Step 6-1: Reflection intensity difference analysis, lidar point cloud data , where is the three-dimensional coordinate of the point cloud, is the reflection intensity, N is the total number of point clouds, and the reflection intensity threshold is set based on the fact that the reflection intensity of the dry tidal flat area is higher and the reflection intensity of the tidal flat area is lower. As a basis for preliminary classification;

[0027] Step 6-2: Boundary extraction, within the sliding window, according to the reflection intensity The point cloud data is divided into two categories: mudflats and seawater:

[0028] , dividing line Defined as and The spatial boundary of can be calculated by the gradient change of the reflection intensity in the window: ,in It represents the spatial gradient of reflection intensity, reflecting the sudden change in intensity at the interface between the mudflat and the sea water;

[0029] Step 6-3: Filtering the point cloud on the seawater side: The point cloud data in the For subsequent processing;

[0030] Step 6-4: Classification and filtering of residual noise in the tidal flat area. In the process, we further classify and remove the residual seawater points, and set the secondary filtering conditions based on the elevation and reflection intensity:

[0031] ,in, For the window The average elevation of the tidal flats is used to distinguish low-lying tidal pools from dry mudflats; Secondary reflection intensity threshold;

[0032] Step 6-5: Elimination Then we get an accurate local point cloud .

[0033] In the above-mentioned method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information, step 7 comprises:

[0034] Step 7-1: Step size definition, the parallelogram sliding window moves along the extension direction of the flight strip, and the moving step size for: ,in, is the step size scaling factor;

[0035] Step 7-2: Movement process: The window moves along the direction of the flight path Distance, the new window center position is updated as:

[0036] ,in, is the central coordinate of the current window; Unit vector in the flight strip direction; Central coordinate of the next window;

[0037] Step 7-3: Repeat Step 6 at the new window position to continuously generate new local fine point clouds;

[0038] Step 7-4: Move the parallelogram window to the end of the flight strip to obtain point cloud data covering the entire flight strip .

[0039] In the above-mentioned method for classifying beach lidar point cloud data based on lidar reflection intensity information, Step 8 includes: During the process of integrating the single-line strip lidar point cloud data set in the beach area, it is necessary to keep the point cloud plane coordinate system consistent, the elevation datum consistent, and the data in the overlapping area between flight strips. Select to smooth or interpolate the overlapping area to maintain the continuity of the data set, , where is the point cloud in the overlapping area, and the point cloud data after optimized smoothing processing is the final point cloud data , where .

[0040] The beneficial effect of a method for classifying beach lidar point cloud data based on lidar reflection intensity information according to the present invention is: The method of the present invention accurately extracts the lidar point cloud data noise brought by the residual sea water through the analysis of the intensity information difference within the window, ensuring the obtained pure beach lidar data. This method can adaptively adjust the classification parameters according to the changes in the reflection intensity of the sea water in different beach areas and different water quality conditions, so as to effectively adapt to the complex beach environment. Compared with the traditional method, the present invention significantly improves the accuracy of the coastal beach DEM data, providing accurate elevation basic data for subsequent terrain analysis and coastal engineering.

[0041] The geometric characteristics of the triangular window facilitate sliding along the flight strip direction, and at the same time, the spatial distribution of the flight strip data is adapted through the directionality of the right-angle sides; the initial size ensures sufficient data coverage, providing an initial reference for the subsequent window contraction based on the reflection intensity. The window size is adaptively adjusted through the reflection intensity to ensure that the window is neither too large nor too small, avoiding including too much irrelevant data while avoiding missing key features; The formula of balances the constraints of the initial setting and the actual data, improving the classification accuracy. Description of the Drawings

[0042] Figure 1 is the tide timetable for querying tide gauges provided in the embodiment of the present invention;

[0043] Figure 2 is the schematic diagram of the route layout direction for obtaining UAV data provided in the embodiment of the present invention;

[0044] Figure 3 Schematic diagram of the initial sliding window size for classifying the intensity information of lidar data on the beach and sea surface provided in the embodiment of the present invention;

[0045] Figure 4 Schematic diagram of the sliding window shrinkage factor for classifying the intensity information of lidar data on the beach and sea surface provided in the embodiment of the present invention;

[0046] Figure 5 Schematic diagram of the sliding window determined when classifying the intensity information of lidar data on the beach and sea surface provided in the embodiment of the present invention;

[0047] Figure 6 Schematic diagram of the lidar intensity information on the beach and sea surface provided in the embodiment of the present invention;

[0048] Figure 7 Schematic diagram of the intensity information of the beach lidar data after classification provided in the embodiment of the present invention. Detailed implementation manners

[0049] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be described below in combination with the detailed implementation manners and the accompanying drawings.

[0050] Embodiment 1

[0051] As Figures 1-7 shown, a method for classifying tidal flat lidar point cloud data based on lidar reflection intensity information includes the following steps.

[0052] Step 1: Obtain the maximum range of the beach exposed after the seawater ebbs based on the isobaths and the coastline, obtain the operation time range according to the predicted tide level at the lowest tide, and determine the tidal flat acquisition range and acquisition time. The acquisition time is 80 minutes before to 10 minutes after the lowest tide moment, and the total time is 90 minutes.

[0053] Step 2: Set the acquisition flight path. The unmanned aerial vehicle (UAV) carries the lidar and flies over the tidal flat acquisition range area during the seawater ebb, collects the lidar point cloud data of the flight area, and calculates the initial lidar point cloud data according to the data calculation software supporting the UAV and lidar equipment.

[0054] The UAV acquisition flight path is arranged parallel to the isobaths. The length of the acquisition flight path does not exceed the flight range that the UAV can fly in 90 minutes. The acquisition flight path is set in the manner from near the coastline to far from the coastline, and then the acquisition flight path near the isobaths is collected at the lowest tide moment.

[0055] Step 3: Construct an initial triangular sliding window for the initial lidar point cloud data under a single UAV flight strip and set the initial side length. The initial sliding window is an isosceles right triangle. One of the two right sides is perpendicular to the flight strip direction, and the other is parallel to the flight strip direction. Set the initial side length to twice the flight height according to the width of the point cloud data with the best point quality obtained by the lidar under the ideal field of view angle, L0 = 2h, where h is the flight height.

[0056] Step 4: According to the lidar point cloud data and the laser reflection intensity information, shrink the side length of the sliding window. The initial sliding window is placed at the end of the flight strip. According to the width of the sea surface reflection point cloud, gradually shrink the side length of the sliding window until seawater point cloud data is detected on both sides of the 90° angle of the sliding window. The formula for the shrunk side length is L = max(aL0, Tseawater), where a is the shrinkage factor, Tseawater is the laser reflection intensity of the seawater point cloud, and L0 is the UAV flight height.

[0057] Step 5: When point cloud data is detected on both sides of the 90° angle of the sliding window, copy the sliding window along the hypotenuse direction to form a quadrilateral window with the same size as the adjusted sliding window.

[0058] Step 6: Inside the quadrilateral window, analyze the difference in reflection intensity between the tidal flat and the seawater based on the lidar reflection intensity information, calculate and extract the boundary line between the tidal flat and the sea surface, filter out the seawater side point cloud data, and further classify the remaining seawater and tidal flat point cloud data within the tidal flat area to eliminate the seawater noise points in the tidal flat area and obtain accurate tidal flat point cloud data.

[0059] Step 7: After completing the classification of the point cloud data in the current quadrilateral window, move the quadrilateral window along the flight strip direction to the next area at a preset step length, and repeat the boundary line detection and data classification process until the point cloud data of the entire flight strip is covered, and perform iteration of the sliding window.

[0060] Step 8: Process Steps 3 to 7 for each flight strip one by one. After separately classifying and filtering out the impurity points for each flight strip, integrate the point cloud data of all flight strips to generate a lidar point cloud dataset of the tidal flat area without impurities, and integrate the lidar point cloud dataset of the tidal flat area to generate the lidar point cloud data of the tidal flat area.

[0061] Example 2

[0062] This example proposes a method for classifying beach and sea surface lidar point cloud data based on lidar reflection intensity information, which can automatically adjust the size of the sliding window to improve the accuracy of the elevation data in the tidal flat area. In the specific method, the side length of the initial sliding window is 2 times the flight height. By shrinking the sliding window and adjusting it according to the laser reflection intensity information, the sliding window with the best size is finally obtained to adapt to the characteristics of the actual data.

[0063] As shown Figures 1-7 below, specifically including:

[0064] (1) Determination of the beach collection range.

[0065] According to the topographic map of the area where the beach is located, obtain the positions of the isobaths and the coastline in the topographic map, and determine the maximum range of the beach that is theoretically exposed after the lowest tide according to the isobaths and the coastline.

[0066] (2) Determination of the beach collection time.

[0067] According to the location of the beach, query the tidal schedule of the nearby tide gauge station to determine the lowest tide time within a month, and determine the operation time range according to the predicted tide level at the lowest tide.

[0068] Among them, the determination of the operation time is based on the lowest tide time in the tidal schedule. The operation time is 80 minutes before the lowest tide time to 10 minutes after, and the total time is 90 minutes.

[0069] (3) Collection of beach lidar point cloud data.

[0070] The drone carries the lidar and flies over the beach area during the ebb tide to collect the lidar point cloud data of the flight area, including the reflected data of the dry beach, tide pools and the sea surface.

[0071] Among them, the layout of the drone flight path is parallel to the isobath, and the length of the flight path does not exceed the flight range that the drone can fly in 90 minutes. The flight path close to the coastline is preferentially collected, and then the flight path close to the isobath is collected at the lowest tide time.

[0072] (4) Calculation of lidar point cloud data.

[0073] According to the data calculation software supporting the drone and the lidar equipment, calculate the initial lidar point cloud data, including the point cloud data of the dry beach, tide pools and part of the sea surface.

[0074] (5) Construction of a sliding window for a single flight strip.

[0075] Construct an initial sliding window for the point cloud data under a single flight strip. This window is an equilateral triangle, where two long sides are perpendicular and parallel to the flight strip direction respectively, and the included angle is 90°. The initial side length is set to 2 times the flight height L0 = 2h flight height.

[0076] Among them, the initial sliding window is an equilateral triangle, where two long sides are perpendicular and along the flight strip direction respectively, and the included angle is 90°. The initial side length is 2 times the flight height L0 = 2h flight height. This is based on the assumption of the width of the point cloud data with the best point quality obtained by the lidar in the ideal field of view (-45° to +45°).

[0077] (6) Adjustment and contraction of the sliding window.

[0078] According to the lidar point cloud data and the laser reflection intensity information, contract the side length of the sliding window. The initial equilateral triangle sliding window is placed at the end of the flight strip, with one 45° angle facing the land end of the flight strip and the other 45° angle perpendicular to the flight strip direction; according to the width of the sea surface reflection point cloud, gradually contract the side length of the window until seawater point cloud data is detected on both sides of the 90° angle of the window; the contracted side length is adjusted by the formula L = max(aL0, T_seawater), where a is the contraction factor and T_seawater is the laser reflection intensity of the seawater point cloud.

[0079] For the contraction of the sliding window, after the point cloud data is detected on both right-angled sides, the side lengths of the two right-angled sides are obtained, and the adjusted side length L = max(aL0, T_seawater), where a is the contraction factor.

[0080] (7) Transformation of the sliding window into a quadrilateral window.

[0081] When point cloud data is detected on both sides of the 90° angle of the sliding window, copy the sliding window along the hypotenuse direction to form a quadrilateral window. The size of the quadrilateral window is the same as that of the adjusted sliding window.

[0082] For the transformation of the sliding window into a quadrilateral window, after the triangular window is contracted, the quadrilateral window is formed by copying and expanding along the hypotenuse of the original equilateral triangle sliding window, and is used for subsequent point cloud data classification operations. Its side length is: L_quadrilateral = L_adjusted.

[0083] (8) Classification of point cloud data for a single flight strip.

[0084] Within the sliding window, analyze the difference in reflection intensity between the tidal flat and the seawater based on the lidar reflection intensity information, calculate and extract the boundary line between the tidal flat and the sea surface. Filter the seawater-side point cloud data; within the tidal flat area, further classify the remaining seawater and tidal flat point cloud data to eliminate the seawater noise points within the tidal flat area and obtain accurate tidal flat point cloud data.

[0085] (9) Iteration of the sliding window.

[0086] After completing the classification of the point cloud data for the current sliding window, the sliding window moves to the next area along the flight strip direction at a preset step size, and repeats the boundary line detection and data classification process until the point cloud data of the entire flight strip is covered.

[0087] (10) Classification of point cloud data for each flight strip.

[0088] Perform the processing from step (5) to step (9) for each strip. After classifying each strip separately and filtering out impurity points, integrate all strip point cloud data to generate a laser point cloud dataset of the tideland area without impurities.

[0089] (11) Data integration.

[0090] Integrate the tideland point cloud data obtained after processing all strips to generate laser point cloud data of the tideland area.

[0091] Embodiment 3

[0092] As Figures 1-7 shown, this embodiment proposes a method for classifying beach and sea surface point cloud data based on lidar reflection intensity information, which improves the accuracy of elevation data in the tideland area by dynamically adjusting the size of the sliding window. The initial side length of the sliding window is 2 times the flight height, and then the window size is adaptively adjusted according to the reflection intensity information to adapt to the characteristics of the actual terrain and data.

[0093] (1) Determination of the beach acquisition range.

[0094] Based on the national elevation standard, obtain the contour lines of the beach from the topographic map of the beach area. Combine the tide table published by the tide gauge near the beach at the time of the lowest tide. According to the relationship between the tide height, the depth datum plane, and the national elevation datum, calculate the lowest elevation that the beach can expose under the national elevation datum theoretically at the time of the lowest tide:

[0095] , where is the lowest elevation that can be exposed under the national elevation datum at the time of the lowest tide; is the tide height at the time of the lowest tide; is the difference between the tide height datum plane and the national elevation datum (as in Figure 1 , ).

[0096] According to and the contour lines and coastline positions in the topographic map, determine the theoretically exposed beach range after the lowest tide.

[0097] Example: From the Figure 1 tide schedule, it can be seen that at 21:46, the tide height at the lowest tide is 85 cm, and the tide height datum plane is 254 cm below the mean sea level. Then =85 cm + (-254 cm)=-169 cm, that is, the lowest elevation of the beach that can be exposed at 21:46 is -1.69 meters under the elevation datum. Query the contour lines in the topographic map and calculate the =-1.69 meters position (which is a continuous line). The flood and ebb tide times of the acquisition area are shown in Table 1, and the high and low tide times are shown in Table 2. Figure 1The tidal timetable shown does not include the data of astronomical tides and does not contain the impacts caused by meteorological reasons. The tidal datum plane: 254 cm below the mean sea level. The datum plane of the tidal table adopts the theoretical depth datum plane.

[0098] Table 1: Figure 1 The flood and ebb tide timetable of the tide gauge station shown

[0099] ,

[0100] Table 2: Figure 1 The high and low tide timetable of the tide gauge station shown

[0101] .

[0102] (2) Determination of the beach collection time.

[0103] According to the location of the beach, query the tidal timetable of the adjacent tide gauge station to determine the lowest tide time within a month , and determine the operation time range according to the predicted tide level at the lowest tide time.

[0104] , where the operation time is determined based on the lowest tide time in the tidal timetable. The operation time is 80 minutes before to 10 minutes after the lowest tide time, and the total time is 90 minutes.

[0105] (3) Collection of beach lidar point cloud data.

[0106] The drone is equipped with lidar and flies over the beach area during the ebb tide to collect the lidar point cloud data of the flight area, including the reflection data of the emerged beach, tide pools and sea surface.

[0107] Among them, the drone flight path is arranged parallel to the isobath. The length of the flight path does not exceed the flight range that the drone can fly in 90 minutes. Priority is given to collecting the flight path close to the coastline, and then collecting the flight path close to the isobath at the lowest tide time. The length of the flight path , where is the drone flight speed (unit: m / min); 90 is the operation time (unit: min).

[0108] (4) Calculation of lidar point cloud data.

[0109] The original lidar data collected by the drone is processed using the data calculation software supporting the lidar device to obtain the initial lidar point cloud data, providing a global initial data set for subsequent classification and processing . , where is the laser emission time, is the laser beam angle, Ranging value (unit: meter), Reflection intensity, N total number of points. It is in the 3D point cloud format directly collected from the drone in step (3) without being processed by the supporting data solution software. , where is the 3D coordinate of the point cloud, is the reflection intensity, and N is the total number of point clouds.

[0110] (5) Construction of a single strip sliding window.

[0111] From Select the point cloud data of one strip , which is the point cloud data of the single strip being currently processed, .

[0112] Among them is the two-dimensional projection area of the strip , which is determined by the strip boundary coordinates and .

[0113] During the processing of the point cloud data under a single strip, an initial sliding window needs to be constructed first. The initial sliding window is designed as an isosceles right triangle.

[0114] The definition and side length of the initial window are as follows:

[0115] Geometric definition of the window: One right side is defined as perpendicular to the strip direction, that is, forming a 90° angle with the extension direction of the strip; the other right side is parallel to the channel strip direction, along the extension direction of the channel strip; the two right sides are equal, forming an isosceles right triangle.

[0116] Calculation of the initial side length: The initial right side length L 0 is 2 times the flight height, that is, L 0 = 2h, where h represents the flight height. The side length L 0 = 2h is the point cloud coverage width calculated based on the ideal field of view angle of the lidar (-45° to +45°). Within this field of view angle, the lidar can obtain the best point cloud quality and density. Assuming the field of view angle = 90° (i.e., ±45°), the point cloud coverage width , so the initial side length L 0 = 2h can cover the best point cloud data range within the strip, effectively capturing local terrain features (changes at the junction of the tidal flat and the sea), while avoiding excessive window range and reducing the calculation efficiency.

[0117] The geometric characteristics of the triangular window facilitate sliding along the flight strip direction, while adapting to the spatial distribution of the flight strip data through the directionality of the right-angled sides; the initial size ensures sufficient data coverage, providing an initial reference for subsequent window shrinkage based on the reflection intensity.

[0118] (6) Adjustment and shrinkage of the sliding window.

[0119] According to the laser point cloud data and the laser reflection intensity information, dynamically shrink the side lengths of the sliding window to adapt to the actual distribution characteristics of the sea surface and beach areas.

[0120] The adjustment process is as follows:

[0121] Initial window placement: The initial sliding window is an isosceles right triangle, placed at the end of the flight strip, with one 45° angle facing the land side of the flight strip, the other 45° angle perpendicular to the flight strip direction, and the 90° right angle facing the seawater side. The initial right-angled side L 0 = 2h (h is the flight height, unit: meter).

[0122] Shrinking basis: Gradually shrink the side lengths of the window according to the width of the sea surface point cloud in . The seawater point cloud is identified by the laser reflection intensity I. Usually, the seawater reflection intensity is lower than that of the tidal flat; the shrinking target is that seawater point cloud data is detected on both right-angled sides at the 90° angle of the window, that is, the window boundary just covers the demarcation area between the seawater and the tidal flat.

[0123] Calculation of the adjusted side length: The shrunk right-angled side is determined by the following formula , where is the shrinking factor, 0 < < 1, adjusted according to the actual point cloud density, and the recommended value range is 0.5 - 0.9, adjusted according to the density and terrain complexity of the point cloud; is the width of the seawater point cloud (unit: meter). It is defined as the point cloud range where the reflection intensity is lower than the threshold along the direction perpendicular to the flight strip, , is the point cloud data of the single flight strip being processed currently, is the demarcation threshold between the seawater and the tidal flat reflection intensities, calibrated through experiments, for example, taking the statistical upper limit of the seawater reflection intensity.

[0124] The shrinking process starts from the initial side length L 0 and gradually reduces the length of the right-angled side. When seawater point cloud is detected on two of the sides, that is, there exists a point P satisfying , stop shrinking, and record the actual lengths of the two right-angled sides at this time and , take the larger value and Compare and finally determine 。

[0125] Adaptive adjustment of the window size based on the reflection intensity ensures that the window is neither too large to avoid including too much irrelevant data nor too small to avoid missing key features; The formula of...balances the constraints of the initial setting and the actual data, improving the classification accuracy.

[0126] (7) Transform the sliding window into a quadrilateral window.

[0127] After the sliding window adjustment is completed, when seawater point cloud data is detected on both right-angled sides at the 90° angle of the window, the triangular window is extended along the hypotenuse direction to transform into a quadrilateral window to further optimize the classification processing of the point cloud data.

[0128] The specific process is as follows:

[0129] Transformation condition: The initial sliding window is an isosceles right triangle, and after contraction, the length of the right-angled side is When seawater point cloud is detected on two of its sides, that is, there exists a point satisfying It indicates that the window has adapted to the seawater-tidal flat boundary area, and at this time, the transformation is triggered.

[0130] Quadrilateral window construction: Based on the adjusted triangular window, copy and extend it along its hypotenuse direction to form a quadrilateral window. The set characteristics of the quadrilateral window are that the lengths of the two parallel sides are equal to the length of the right-angled side of the triangle and the lengths of the other two parallel sides (in the original hypotenuse direction) are equal to the length of the hypotenuse of the triangle, that is Based on the properties of an isosceles right triangle, the length of the hypotenuse is times the length of the right-angled side.

[0131] Definition of the side length of the quadrilateral window: The size of the quadrilateral window is the same as that of the adjusted triangular window, and its right-angled side length is 。

[0132] The transformation process is as follows:

[0133] Keep the 90° position of the adjusted triangular window unchanged, copy an identical triangle along the hypotenuse so that the hypotenuses of the two triangles coincide, forming a parallelogram. The total area of the quadrilateral window is:

[0134] 。

[0135] The purpose of the transformation is: The quadrilateral window enhances the ability to capture point cloud data in the seawater-tidal flat boundary area by expanding the coverage range; its regular geometric shape facilitates subsequent classification operations based on reflection intensity, improving the stability and accuracy of the algorithm.

[0136] (8) Classification of point cloud data for a single flight strip.

[0137] Within the sliding window, based on the lidar reflection intensity information, analyze the difference in reflection intensity between the tidal flat and the sea water, calculate and extract the boundary line between the tidal flat and the sea surface. Subsequently, classify and filter the noise of the point cloud data to obtain accurate tidal flat point cloud data. The specific steps are as follows.

[0138] Analysis of reflection intensity difference: Analyze the reflection intensity difference within the sliding window, for the lidar point cloud data , where is the three-dimensional coordinate of the point cloud, is the reflection intensity, and N is the total number of point clouds; in the tidal flat area, such as dry beaches, usually has a higher reflection intensity, while the sea water area, including tidal pools, has a lower reflection intensity. Based on this characteristic, set the reflection intensity threshold as the preliminary classification basis.

[0139] Boundary line extraction: Within the sliding window, according to the reflection intensity divide the point cloud data into two categories: tidal flat and sea water:

[0140]

[0141] , the boundary line is defined as the spatial boundary between and , and can be calculated by the gradient change of the reflection intensity within the window: where

[0142] represents the spatial gradient of the reflection intensity, reflecting the intensity mutation at the junction of the tidal flat and the sea water. Filtering of sea water side point cloud: Directly remove the point cloud data in and retain the

[0143] preliminary classified for subsequent processing. Classification and filtering of residual noise in the tidal flat area: In

[0144] , there may be residual sea water points, such as tidal pools or wetlands, etc., which need to be further classified and removed. Combine the elevation z and the reflection intensity I to set the secondary filtering condition: where is the average elevation of

[0145] Eliminate to obtain accurate beach beach point cloud data, .

[0146] Precisely locate the demarcation line through the reflection intensity threshold and gradient analysis to improve the accuracy of classification; the secondary filtering combines elevation and intensity features to effectively eliminate the seawater noise in the tidal flat area and ensure the high precision of the point cloud data.

[0147] (9) Sliding window iteration.

[0148] After completing the classification of the point cloud data within the current sliding window, the sliding window moves to the next area along the flight strip direction at a preset step length, and the demarcation line detection and data classification processes are repeated until the point cloud data of the entire flight strip is covered.

[0149] The specific steps are as follows:

[0150] Iteration trigger: After the current sliding window (quadrilateral window, side length ) completes the classification of the tidal flat and seawater point clouds, local fine point clouds are obtained .

[0151] Step length definition: The sliding window moves along the extension direction of the flight strip, and the extension direction of the flight strip is parallel to one right-angled side. The moving step length is

[0152] , where is the step length scale factor ( ), which controls the relationship between the moving distance and the window size, and takes a value between 0.3 and 0.5 to balance the coverage rate and calculation efficiency; is the adjusted right-angled side length of the window, unit: meter.

[0153] Moving process: The window translates distance along the flight strip direction, and the new window center position is updated as:

[0154] , where is the current window center coordinate; represents the unit vector in the flight strip direction, represents the center coordinate of the next window.

[0155] Iteration operation: Repeat the above steps at the new window position, that is, detect the demarcation line based on the reflection intensity I, and then classify the tidal flat point cloud and seawater point cloud , filter out the seawater noise in .

[0156] Termination condition: The window moves to the end of the strip, covering the point cloud data of a single strip being processed currently Represents the point cloud data in the iterative process, and determines the position of the point cloud data at the end of the strip through the calculation formula where, is the total length of the strip (unit: meter); ceiling function

[0157] Step size The adaptive design of is based on Ensure that the window coverage is consistent with the terrain change; the iterative process efficiently covers the strip, avoiding data omission or duplicate processing

[0158] (10) Classify the point cloud data of each strip

[0159] For each strip in execute the processing flow from step (5) to step (9) one by one to complete the classification and impurity filtering of the point cloud data. The specific steps are as follows

[0160] Single strip processing: For each strip in the strip set perform the following processing respectively, where M is the total number of strips

[0161] Execute step (5) to construct an initial sliding window (isosceles right triangle, );

[0162] Execute step (6) to adjust the side length of the window to ;

[0163] Execute step (7) to transform it into a quadrilateral window ( );

[0164] Execute step (8) to classify the beach point cloud based on the reflection intensity and filter out the seawater noise;

[0165] Execute step (9) to iteratively slide the window according to the step size until it covers all the point cloud data of

[0166] Result of a single strip: After processing each strip obtain a subset of beach point cloud without impurities:

[0167] where, is the precise beach point cloud after classification and filtering in strip

[0168] (11) Data integration ​​​​

[0169] The tidal flat point cloud data obtained after processing each flight strip are integrated to obtain a laser point cloud data set of the tidal flat area without impurities.

[0170] Merge the tidal flat point cloud subsets of all flight strips to obtain a complete tidal flat point cloud dataset for the beach area:

[0171] .

[0172] During the integration process, ensure that the point cloud plane coordinate system is consistent, the elevation datum is consistent, select the 1985 national elevation datum, and the overlapping area data between flight strips can be smoothed or interpolated to optimize the continuity of the data set.

[0173] ,in, It is the point cloud of the overlapping area.

[0174] The point cloud data after optimization and smoothing is the final point cloud data ,in .

[0175] The above embodiments are only for illustrating the inventive concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information, characterized in that: The following steps are involved: Step 1: Based on the depth contours and coastline, obtain the theoretical maximum range of the beach exposed after the sea water recedes and the high tide. According to the predicted tide level at the lowest tide, obtain the operation time range and determine the tidal flat collection range and collection time. Step 2: Set the collection route. The UAV equipped with the laser radar flies over the mudflat collection area at low tide to collect laser point cloud data in the flight area. The initial laser radar point cloud data is obtained by solving the data solving software supporting the UAV and the laser radar equipment. Step 3: Construct a triangular initial sliding window for the initial lidar point cloud data under a single UAV flight path and set the initial side length; Step 4: According to the laser point cloud data and laser reflection intensity information, dynamically shrink the side length of the sliding window to adapt to the actual distribution characteristics of the sea surface and beach area; Step 5: When point cloud data are detected on both sides of the 90° angle of the sliding window, expand along the oblique direction to form a quadrilateral window with the same size as the adjusted sliding window to optimize the classification processing of the point cloud data; Step 6: In the quadrilateral window, the difference in reflection intensity between the tidal flat and the seawater is analyzed based on the laser radar reflection intensity information, the boundary between the tidal flat and the sea surface is calculated and extracted, the point cloud data is classified and noise is filtered out, and accurate tidal flat point cloud data is obtained; Step 7: After completing the classification of the point cloud data of the current quadrilateral window, the quadrilateral window moves to the next area along the flight strip according to the preset step length, repeating the boundary line detection and data classification process until the point cloud data of the entire flight strip is covered, and the sliding window is iterated; Step 8: Perform steps 3 to 7 for each flight strip. After classifying each flight strip and filtering out impurity points, integrate the point cloud data of all flight strips to generate a single-strip laser point cloud dataset of the tidal flat area without impurities. Integrate the single-strip laser point cloud dataset of the tidal flat area to generate the laser point cloud data of the tidal flat area.

2. The method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information according to claim 1 is characterized in that: In step 1, the collection time is from 80 minutes before to 10 minutes after the lowest tide, and the total time is 90 minutes.

3. The method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information according to claim 1 is characterized in that: In step 2, the drone collection route is arranged parallel to the isobath, the length of the collection route does not exceed the flight distance that the drone can fly in 90 minutes, and the collection route is set from close to the coastline to far away from the coastline, and then the collection route close to the isobath is collected at the lowest tide.

4. The method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information according to claim 1 is characterized in that: In step 3, the initial sliding window is an isosceles right triangle, one of the two right-angled sides is perpendicular to the direction of the flight strip, and the other is parallel to the direction of the flight strip. The initial side length is set to twice the flight altitude according to the width of the point cloud data with the best point quality obtained by the laser radar under the ideal field of view angle, L0=2h, where h is the flight altitude.

5. The method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information according to claim 1 is characterized in that: The step 4 comprises: Step 4-1: Place the initial sliding window at the end of the flight strip, with one 45° angle facing the land side of the flight strip, the other 45° angle perpendicular to the flight strip direction, and a 90° right angle facing the sea side. The initial right angle side L0=2h; Step 4-2: According to the width of the sea surface reflection point cloud, gradually shrink the side length of the sliding window, and use the formula for the side length after shrinkage: Calculate the length of the side after contraction, where is the shrinkage factor, is the width of the sea water point cloud; Step 4-3: When both right-angled sides of the sliding window detect the seawater point cloud data, stop shrinking and record the actual lengths of the two right-angled sides at this time. and , take the larger value and Compare and finalize .

6. The method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information according to claim 5 is characterized in that: In step 4-2, it is defined that the reflection intensity in the vertical direction along the flight strip is lower than the threshold The point cloud range, , The point cloud data of a single route currently being processed, It is the dividing threshold of the reflection intensity between sea water and mudflats.

7. The method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information according to claim 5 is characterized in that: The step 5 comprises: Step 5-1: Take the triangle window when the contraction stops as the reference, copy an identical triangle along the hypotenuse, make the hypotenuses of the two triangles overlap, and form a parallelogram window; Step 5-2: Calculate the total area of ​​the parallelogram window: , by using parallelogram windows to expand window coverage.

8. The method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information according to claim 5 is characterized in that: The step 6 comprises: Step 6-1: Reflection intensity difference analysis, LiDAR point cloud data ,in is the 3D coordinate of the point cloud, is the reflection intensity, N is the total number of point clouds, and the reflection intensity threshold is set based on the fact that the reflection intensity of the dry tidal flat area is higher and the reflection intensity of the tidal flat area is lower. As a basis for preliminary classification; Step 6-2: Boundary extraction, within the sliding window, according to the reflection intensity The point cloud data is divided into two categories: mudflats and seawater: , , dividing line Defined as and The spatial boundary of can be calculated by the gradient change of the reflection intensity in the window: ,in It represents the spatial gradient of reflection intensity, reflecting the sudden change in intensity at the interface between the mudflat and the sea water; Step 6-3: Filtering the point cloud on the seawater side: The point cloud data in the For subsequent processing; Step 6-4: Classification and filtering of residual noise in the tidal flat area. In the process, we further classify and remove the residual seawater points, and set the secondary filtering conditions based on the elevation and reflection intensity: ,in, For the window The average elevation of the tidal flats is used to distinguish low-lying tidal pools from dry mudflats; Secondary reflection intensity threshold; Step 6-5: Elimination Then we get an accurate local point cloud .

9. The method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information according to claim 8 is characterized in that: The step 7 comprises: Step 7-1: Step size definition, the parallelogram sliding window moves along the extension direction of the flight strip, and the moving step size for: ,in, is the step size scaling factor; Step 7-2: Movement process: The window moves along the direction of the flight path Distance, the new window center position is updated as: ,in, The center coordinates of the current window; The unit vector of the flight path direction; The center coordinates of the next window; Step 7-3: Repeat step 6 at the new window position to continuously generate new local fine point clouds; Step 7-4: Move the parallelogram window to the end of the flight strip to obtain point cloud data covering the entire flight strip .

10. The method for classifying tidal flat laser point cloud data based on laser radar reflection intensity information according to claim 9, characterized in that: Step 8 includes: In the process of integrating the laser point cloud data set of a single strip in the tidal flat area, it is necessary to keep the point cloud plane coordinate system consistent, the elevation benchmark consistent, the overlapping area data between the strips, and choose to smooth or interpolate the overlapping area to maintain the continuity of the data set. ,in, The point cloud of the overlapping area, the point cloud data after optimization and smoothing is the final point cloud data ,in .

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