A method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system

Abstract

This invention provides a method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system, comprising: establishing an index sharing mechanism; segmenting the area to be scanned to obtain land point clouds, water surface point clouds, and underwater point clouds; removing coordinate data, intensity data, and emission angle parameters corresponding to the water surface point clouds based on the index sharing mechanism to obtain the removed data; segmenting the coordinate data and intensity data corresponding to the removed point clouds according to the emission angle parameters, based on the index sharing mechanism, to determine the intensity compensation anomaly scanning areas; and correcting the intensity of each scan line in each intensity compensation anomaly scanning area according to a neighborhood joint weighting method to obtain the corrected image. This invention solves the problem in the prior art of local intensity anomalies in the intensity data of ground features in coastal areas with many high-return and low-return targets, such as exposed rocks and water bodies.

Description

Technical Field

[0001] This invention relates to the field of optical depth sounding technology, and in particular to a method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system. Background Technology

[0002] Airborne laserbathymetry (ALB) systems emit green lasers with unique water-penetrating capabilities and are widely used for water depth measurement. In addition to topographic data, ALBs also record the backscattering intensity of ground features, which can be used for strip registration, seabed classification, and advanced geometric modeling.

[0003] However, due to design limitations of AGC, gain adjustment is delayed. In coastal areas, particularly islands and waterways with numerous high-return and low-return targets, the intensity compensation anomaly is particularly pronounced, resulting in localized intensity anomalies in ground feature intensity data and unnatural transitions with surrounding data. Existing research on airborne laser bathymetry intensity data correction and applications has yet to propose an effective solution to this problem. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a method for correcting intensity anomalies in the automatic gain control of an airborne laser depth sounding system. This invention addresses the problem in existing technologies where, in coastal areas with numerous high-return and low-return targets such as exposed rocks and water bodies, localized intensity anomalies exist in the ground feature intensity data.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for correcting intensity anomalies in the automatic gain control of an airborne laser depth sounding system includes:

[0007] An index sharing mechanism is established, which includes: coordinate data, intensity data, and emission angle parameters of each point in the area to be scanned;

[0008] The area to be scanned is segmented into land and water point clouds to obtain land point clouds and water point clouds.

[0009] The water area point cloud is segmented to obtain surface point cloud and bottom point cloud;

[0010] Based on the index sharing mechanism, the coordinate data, intensity data and emission angle parameters corresponding to the water surface point cloud are removed to obtain the coordinate data, intensity data and emission angle parameters corresponding to the point cloud after removal.

[0011] Based on the emission angle parameters corresponding to the removed point cloud, the coordinate data and intensity data corresponding to the removed point cloud are divided into scan cycles based on the index sharing mechanism, wherein one scan cycle corresponds to one scan line;

[0012] Correlation determination is performed on each adjacent scan line to identify the intensity compensation abnormal scan area;

[0013] The intensity of each scan line in each of the intensity compensation abnormal scan regions is corrected according to the neighborhood joint weighting method to obtain the corrected image.

[0014] Preferably, the establishment of the index sharing mechanism includes:

[0015] The laser depth sounding system is used to obtain the LAS file of the area to be scanned.

[0016] The LAS file is parsed to obtain the coordinate data, intensity data, and emission angle parameters of each point in the area to be scanned;

[0017] An index sharing mechanism is established using the coordinate data, intensity data, and emission angle parameters.

[0018] Preferably, the step of segmenting the area to be scanned into land and water point clouds to obtain land point clouds and water point clouds includes:

[0019] The location of the deepest water area was determined by searching for the lowest elevation.

[0020] The location of the deepest water area is set as the center, and the highest elevation within the first area of ​​this center is searched to obtain the first threshold.

[0021] The first threshold is used to segment the water and land point clouds to obtain land point clouds and water point clouds.

[0022] Preferably, the segmentation of the water area point cloud to obtain surface point cloud and bottom point cloud includes:

[0023] The water area point cloud is divided into grids, and the average elevation of the highest and lowest points within each grid is calculated to obtain the second threshold.

[0024] Determine whether the elevation of the water point cloud within each grid range is greater than the second threshold. If it is, it is a surface point cloud; otherwise, it is a bottom point cloud.

[0025] Preferably, the step of determining the correlation between adjacent scan lines to identify intensity-compensated abnormal scan areas includes:

[0026] Extract the intensity data of each adjacent scan line;

[0027] Calculate the Pearson correlation coefficient of adjacent scan lines based on the intensity data of each adjacent scan line;

[0028] Determine whether the Pearson correlation coefficient is greater than the third threshold. If it is, the (N+1)th scan line is a normal scan line; if not, the (N+1)th scan line is a compensated abnormal scan line.

[0029] Based on the aforementioned abnormal intensity scan lines, the scanning area between every two abnormal intensity scan lines is defined as the abnormal intensity scan area.

[0030] Preferably, the step of calculating the Pearson correlation coefficient of adjacent scan lines based on the intensity data of each adjacent scan line includes:

[0031] Calculate the mean value of the intensity data for the Nth scan line;

[0032] Calculate the mean value of the intensity data for the (N+1)th scan line;

[0033] The Pearson correlation coefficient between adjacent scan lines is calculated based on the mean intensity data of the Nth scan line and the mean intensity data of the (N+1)th scan line.

[0034] Preferably, the step of correcting the intensity of each scan line in each of the intensity compensation abnormal scan regions according to the neighborhood joint weighting method to obtain the corrected image includes:

[0035] The sampling data of the Nth and N+1th scan lines in the abnormal region are collected using a uniform sampling method;

[0036] Calculate the mean intensity and the deviation between the means of the two sets of sampling data respectively;

[0037] Based on the mean intensity of the two sets of sampled data and the deviation between the mean intensity, the neighborhood inverse distance weighted mean of each sampled data in the abnormal scan line is calculated sequentially.

[0038] Based on the neighborhood inverse distance weighted mean of each sampled data, construct the neighborhood joint weighted strength correction equation for that point and define the objective function. Determine the parameters of the neighborhood joint weighted strength correction equation using the least squares method.

[0039] The intensity data of the current AGC compensation abnormal scan line are corrected sequentially according to the neighborhood joint weighted intensity correction equation to obtain the corrected image.

[0040] Preferably, the step of sequentially calculating the neighborhood inverse distance weighted mean of each sampled data in the abnormal scan line based on the mean intensity of the two sampled data and the deviation between the mean intensity values ​​includes:

[0041] The neighborhood of a point is defined by taking the sampling point as the center, a radius of 2.5 times the spatial resolution of the point cloud, a central angle of 100 degrees, and a direction perpendicular to the scan line and pointing towards the normal intensity area. The inverse distance weighted average of the intensity within this neighborhood is then calculated.

[0042] Preferably, the expression for the correction equation is:

[0043]

[0044] Where w1 and w2 are weights, For scan line t1, the corrected intensity value at the j-th point. To correct the previous strength, Δi is the inverse distance weighted average of the neighborhood strengths. t1-1,t1 This represents the strength deviation between strip t1-1 and strip t1.

[0045] Preferably, the expression for the objective function is:

[0046] in,

[0047] in, The corrected intensity corresponds to the t-th point among 30 points uniformly sampled in the tn strip. Δi is the inverse distance weighted average of the neighborhood strengths of this point. tn-1,tn The mean intensity deviation between strip tn-1 and tn. For tn-1 stripe and The intensity of the point closest to the point.

[0048] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0049] This invention provides a method for correcting intensity anomalies in an airborne laser bathymetry (AGC) system. It obtains intensity attribute values, emission angle parameters, and point cloud coordinate data from the airborne laser bathymetry record by parsing the LAS file and establishes an index sharing mechanism. Water and land point clouds are segmented using point cloud coordinate information. A grid mean segmentation method is then used to segment the water surface and seabed point clouds. Based on the index sharing mechanism, surface point coordinates, intensity, and emission angle are removed, and the coordinates and intensity are then merged. Scan line segmentation is performed based on the emission angle, thus segmenting the intensity and coordinate data into scan lines. The Pearson correlation coefficient of adjacent scan lines is calculated on a per-scan-line basis, and a threshold is set to determine the interval of anomalous scan lines for AGC compensation. A neighborhood joint weighting method is designed to correct anomalous scan lines for AGC compensation, ultimately resulting in airborne laser bathymetry system intensity data with natural transitions that effectively express the attributes of ground features. This data is helpful for further classification of seabed sediments and improvement of marine spatial information. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart of an intensity anomaly correction method for automatic gain control of an airborne laser depth sounding system provided in an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of an intensity anomaly correction method for automatic gain control of an airborne laser depth sounding system provided in an embodiment of the present invention;

[0053] Figure 3 This is an ALB water and land point cloud segmentation display diagram provided in an embodiment of the present invention;

[0054] Figure 4 ALB water surface and bottom point cloud segmentation map provided in this embodiment of the invention;

[0055] Figure 5 This is a diagram showing the identification results of the ALB intensity AGC intensity compensation anomaly region provided in an embodiment of the present invention.

[0056] Figure 6The images shown are the intensity images obtained after removing the water surface return intensity and the intensity images obtained after AGC intensity compensation correction, provided in the embodiments of the present invention. Among them, (a) is the intensity image after removing the sea surface echo intensity, (b) is the intensity image after AGC compensation anomaly area intensity correction, (c) is the comparison image of the intensity before and after AGC compensation anomaly area in region of interest 1, and (d) is the comparison image of the intensity before and after AGC compensation anomaly area in region of interest 2. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] The purpose of this invention is to provide a method for correcting intensity anomalies in the automatic gain control of an airborne laser depth sounding system. This invention addresses the problem in existing technologies where, in coastal areas with numerous high-return and low-return targets such as exposed rocks and water bodies, localized intensity anomalies exist in the intensity data of ground features.

[0059] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] like Figure 1 As shown, this invention provides a method for correcting intensity anomalies in the automatic gain control of an airborne laser depth sounding system, comprising:

[0061] Step 100: Establish an index sharing mechanism, which includes: coordinate data, intensity data and emission angle parameters of each point in the area to be scanned;

[0062] Step 200: Perform water and land point cloud segmentation on the area to be scanned to obtain land point cloud and water point cloud;

[0063] Step 300: Segment the water area point cloud to obtain surface point cloud and bottom point cloud;

[0064] Step 400: Based on the index sharing mechanism, the coordinate data, intensity data and emission angle parameters corresponding to the point cloud are removed to obtain the coordinate data, intensity data and emission angle parameters corresponding to the point cloud after removal. The coordinate data, intensity data and emission angle parameters corresponding to the point cloud after removal are the coordinate data, intensity data and emission angle parameters corresponding to the land point cloud and the underwater point cloud, respectively.

[0065] Step 500: Based on the emission angle parameters corresponding to the removed point cloud, the coordinate data and intensity data corresponding to the removed point cloud are divided into scan cycles according to the index sharing mechanism, wherein one scan cycle corresponds to one scan line;

[0066] Step 600: Perform correlation judgment on each adjacent scan line to determine the intensity compensation abnormal scan area;

[0067] Step 700: Correct the intensity of each scan line in each of the intensity compensation abnormal scan regions according to the neighborhood joint weighting method to obtain the corrected image.

[0068] Furthermore, such as Figure 2 As shown, the index sharing mechanism includes:

[0069] The laser depth sounding system is used to obtain the LAS file of the area to be scanned.

[0070] The LAS file is parsed to obtain the coordinate data, intensity data, and emission angle parameters of each point in the area to be scanned;

[0071] An index sharing mechanism is established using the coordinate data, intensity data, and emission angle parameters.

[0072] Specifically, based on the specific format (1.1–1.4, etc.) of the Airborne Laser Bathymetry (ALB) data file Las, analytical code is written to process the coordinate data XYZ={x1,x2,…x n ;y1,y2,…y n ;z1,z2,…z n Let I be an n×3 matrix, where n is the number of points, and the intensity data be I = {i1, i2, ..., i...}. n Let be an n×1 matrix, and let the emission angle data be A={a1,a2,…a ... n} is an n×1 matrix, and it involves reading and storing three types of data;

[0073] Add an index column Index = {1, 2, ..., n} to each of the three types of data. Each target point has its own index number, unique coordinates, intensity, and emission angle.

[0074] Furthermore, such as Figure 3 As shown, the step of segmenting the area to be scanned into land and water point clouds to obtain land point clouds and water point clouds includes:

[0075] The location of the deepest water area was determined by searching for the lowest elevation.

[0076] The location of the deepest water area is set as the center, and the highest elevation within the first area of ​​this center is searched to obtain the first threshold.

[0077] The first threshold is used to segment the water and land point clouds to obtain land point clouds and water point clouds.

[0078] The location of the deepest water area is determined by searching the lowest elevation. A certain neighborhood range (2m×2m) is set with this point as the center. The highest elevation within this range is searched to obtain the first threshold. It is assumed that the first threshold should correspond to the water surface. Water and land point cloud segmentation is performed using the elevation of this point as the threshold.

[0079] Furthermore, such as Figure 4 As shown, the segmentation of the water area point cloud to obtain surface point cloud and bottom point cloud includes:

[0080] The water area point cloud is divided into grids, and the average elevation of the highest and lowest points within each grid is calculated to obtain the second threshold.

[0081] Determine whether the elevation of the water point cloud within each grid range is greater than the second threshold. If it is, it is a surface point cloud; otherwise, it is a bottom point cloud.

[0082] The water area is divided into grids at a certain distance (5m), and the average elevation of the highest and lowest points within each grid (second threshold) is calculated to achieve point cloud segmentation of the water surface and bottom within each grid.

[0083] By using an index sharing mechanism, intensity data, coordinate data, and emission angle parameters corresponding to water surface point clouds are removed, thus eliminating the hybrid effect of multiple echoes (water surface echoes).

[0084] The remaining coordinate and intensity data are merged.

[0085] Specifically, search for the minimum value in z, determine its index and coordinate data, let's say k, and consider (x... k ,y k (x) is the bottom point, and (x) is the bottom point. k ,y k Search for a point cloud set Q with a 2m × 2m radius centered on it. k ={(x1,y1,z1),(x2,y2,z2),…(x num ,y num ,z num )}, where num is the set Q k The number of points;

[0086] Search collection Q k Maximum value of z and minimum value think The corresponding point is the water surface point, with The entire coordinate data XYZ is divided into two parts, XYZ, using a threshold. land and XYZ water XYZ land The z values ​​are all greater than XYZ water The z values ​​are all less than

[0087] For XYZ water The data is divided into grids of size 5m × 5m. Let Q be the set of coordinate data within the j-th grid. j ={(x1,y1,z1),(x2,y2,z2),…(x m ,y m ,z m )}, m is a set Q j The number of points, calculate the set Q j The maximum value of z in and minimum value mean by Q is the threshold j Perform point cloud segmentation on the water surface and bottom, Q j_surface For points on the water surface, z is always greater than 1. Q j_bottom For the bottom point, z is always less than

[0088] The point cloud in each grid is segmented into land area, water surface, and seabed point cloud in sequence. The water surface point cloud is removed. According to the index number, the corresponding intensity data and emission angle data are removed to obtain new coordinate data XYZ', intensity data I', and emission angle data A'. XYZ' and I' are merged to obtain XYZI.

[0089] Furthermore, such as Figure 5 As shown, the step of determining the correlation between adjacent scan lines to identify intensity-compensated abnormal scan areas includes:

[0090] Extract the intensity data of each adjacent scan line;

[0091] Calculate the Pearson correlation coefficient of adjacent scan lines based on the intensity data of each adjacent scan line;

[0092] Determine whether the Pearson correlation coefficient is greater than the third threshold. If it is, the (N+1)th scan line is a normal scan line; otherwise, the (N+1)th scan line is a compensated abnormal scan line.

[0093] Based on the aforementioned abnormal intensity scan lines, the scanning area between every two abnormal intensity scan lines is defined as the abnormal intensity scan area.

[0094] Based on the reflection angle parameter, the emission cycle is segmented, and a scan cycle is considered to be a gradual increase from the minimum angle to the maximum angle or a gradual decrease from the maximum angle to the minimum angle. Based on the index sharing mechanism, the intensity data and coordinate data are segmented by scan lines.

[0095] Along the scanning direction, the correlation between adjacent scan lines is judged sequentially, and a certain number of sampling points (30) are selected for the current scan line according to the uniform sampling principle. The corresponding points of the adjacent scan line (previous line) are extracted according to the nearest distance principle.

[0096] Calculate the Pearson correlation coefficient between two adjacent scan lines and set a threshold (third threshold). If the correlation coefficient exceeds the threshold, it is considered to be normal. If the correlation coefficient is lower than the threshold, the adjacent scan line is considered to be an AGC-compensated abnormal scan line.

[0097] Along the scan line direction, the correlation of adjacent scan lines is judged sequentially, and the scan line between every two AGC compensation abnormal scan lines is the AGC compensation abnormal scan area.

[0098] Furthermore, the step of calculating the Pearson correlation coefficient of adjacent scan lines based on the intensity data of each adjacent scan line includes:

[0099] Calculate the mean value of the intensity data for the Nth scan line;

[0100] Calculate the mean value of the intensity data for the (N+1)th scan line;

[0101] The Pearson correlation coefficient between adjacent scan lines is calculated based on the mean intensity data of the Nth scan line and the mean intensity data of the (N+1)th scan line.

[0102] Specifically, the emission angle within a scan cycle is typically determined by the minimum angle α. min Gradually increase to the maximum angle α max Or from the maximum angle a max Gradually decrease to the minimum angle α min For one scan cycle, according to a max and a max Scan cycles are segmented to determine the launch angle. Based on an index-sharing mechanism, coordinate and intensity data are segmented into scan cycles. Data within one scan cycle is called a scan line. Scan lines are numbered along the heading: Line = {l1, l2, ..., l...} m1}, where m1 is the total number of scan lines;

[0103] Based on the indexing mechanism, the merged coordinate and intensity data XYZI is segmented by scan lines, XYZI = {xyzi} 1 ,xyzi 2 ,…,xyzi m1}, xyzi t The set of coordinate intensities corresponding to scan line t n3 is the number of data points at the midpoint of scan line t;

[0104] The correlation between adjacent scan lines is determined sequentially, with each scan line as a unit. Let the data set for the t-th scan line be xyzi. t Based on the principle of uniform sampling, 30 sampled data points were selected to form a set. Extract the 30 sampling points corresponding to the nearest scan line t+1 according to the nearest distance principle.

[0105] Calculate Q t_30 Intensity i t and Q t+1_30 Intensity i t+1 The Pearson correlation coefficient between them is calculated by the following steps:

[0106] Step 1: Calculate Q t_30 Intensity data i t mean

[0107]

[0108] Step 2: Calculate Q t+1_30 Intensity data i t+1 mean

[0109]

[0110] Step 3: Calculate i t and i t+1 Pearson correlation coefficient r t,t+1

[0111]

[0112] Set a threshold. If the threshold is exceeded, the scan line t+1 is considered to be normal. If the correlation coefficient is lower than the threshold, the adjacent scan line t+1 is considered to be an abnormal scan line for AGC compensation.

[0113] Along the scan line direction, the correlation of adjacent scan lines is judged sequentially, and the scan line between every two AGC intensity compensation abnormal scan lines is the AGC intensity compensation abnormal scan area.

[0114] Furthermore, such as Figure 6 As shown, (a) is the intensity image after removing the sea surface echo intensity, (b) is the intensity image after correcting the intensity of the AGC compensation anomaly area, (c) is the comparison image of the intensity before and after correction of the intensity of the AGC compensation anomaly area in region of interest 1, and (d) is the comparison image of the intensity before and after correction of the intensity of the AGC compensation anomaly area in region of interest 2.

[0115] The step of correcting the intensity of each scan line in each of the intensity-compensated abnormal scan regions according to the neighborhood joint weighting method to obtain the corrected image includes:

[0116] The sampling data of the Nth and N+1th scan lines in the abnormal region are collected using a uniform sampling method;

[0117] Calculate the mean intensity and the deviation between the means of the two sets of sampling data respectively;

[0118] Based on the mean intensity of the two sets of sampled data and the deviation between the mean intensity, the neighborhood inverse distance weighted mean of each sampled data in the abnormal scan line is calculated sequentially.

[0119] Based on the neighborhood inverse distance weighted mean of each sampled data, construct the neighborhood joint weighted strength correction equation for that point and define the objective function. Determine the parameters of the neighborhood joint weighted strength correction equation using the least squares method.

[0120] The intensity data of the current AGC compensation abnormal scan line are corrected sequentially according to the neighborhood joint weighted intensity correction equation to obtain the corrected image.

[0121] Furthermore, the step of sequentially calculating the neighborhood inverse distance weighted mean of each sampled data point in the abnormal scan line based on the mean intensity of the two sampled data points and the deviation between the mean intensity values ​​includes:

[0122] The neighborhood of a point is defined by taking the sampling point as the center, a radius of 2.5 times the spatial resolution of the point cloud, a central angle of 100 degrees, and a direction perpendicular to the scan line and pointing towards the normal intensity area. The inverse distance weighted average of the intensity within this neighborhood is then calculated.

[0123] For any AGC compensation abnormal area, a certain number of sampled data (30) are selected by uniform sampling according to the first scan line of the current abnormal area in the flight direction, and the corresponding sampled data (30) of the nearest normal scan line (the previous one) are calculated according to the closest distance.

[0124] Calculate the mean intensity and the deviation between the means of the two sets of sampling data respectively;

[0125] The neighborhood inverse distance weighted average of each sampled data in the abnormal scan line is calculated sequentially. Specifically, the neighborhood range of each sampled data is defined with the sampling point as the center, the radius as 2.5 times the spatial resolution of the point cloud, the central angle as 100 degrees, and the direction perpendicular to the scan line and pointing towards the normal intensity area. The intensity inverse distance weighted average of this range is then calculated.

[0126] Construct the neighborhood joint weighted intensity correction equation for this point, define the objective function, and determine the parameters of the neighborhood joint weighted intensity correction equation using the least squares method;

[0127] The intensity data of the current AGC compensation anomaly scan line are corrected sequentially according to the neighborhood joint weighted intensity correction equation. It is only necessary to continuously adjust the intensity deviation of adjacent strip sampling data and the neighborhood intensity inverse distance weighted average value of the point. Following this method, the intensity of different AGC compensation anomaly areas is corrected sequentially along the scanning direction.

[0128] Specifically, assuming a total of n1 AGC compensation anomaly regions, each anomaly region is corrected sequentially along the flight direction. Assume that the anomaly scan line interval contained within a certain AGC compensation anomaly region is [l t1 ,l t2 There are a total of t2-t1+1 scan lines. Based on the uniform sampling principle, the data of scan line t1 is thinned out, and 30 sampling points are selected. Extract 30 sampling data points corresponding to the nearest normal scan line t1-1 according to the principle of proximity.

[0129] Calculate Q t1_30 Mean of intensity data Calculate Q t1-1_30 Mean of intensity data Obtain the deviation between the two

[0130] calculate The inverse distance weighted average of the neighborhood intensities corresponding to each intensity data point. The specific calculation steps include:

[0131] Step 1: Using the sampling point as the center, a radius of 2.5 times the spatial resolution of the point cloud, and a central angle of 100 degrees, define the neighborhood of that point as the area perpendicular to the scan line and pointing towards the normal intensity region. Assume the j-th point on the t-th scan line... The corresponding neighborhood range contains the following data set: The set contains n4 data items;

[0132] Step 2: Calculate points Neighborhood intensity inverse distance weighted average

[0133]

[0134]

[0135] Where, d kt for From the kt-th point The planar distance;

[0136]

[0137] Design a neighborhood joint weighted strength correction equation.

[0138] Where w1 and w2 are weights, and w1 + w2 = 1. For scan line t1, the corrected intensity value at the j-th point. To correct the previous strength, Δi is the inverse distance weighted average of the neighborhood strengths. t1-1,t1 To determine the intensity deviation between stripe t1-1 and stripe t1, an objective function g is constructed.

[0139]

[0140]

[0141] in, The corrected intensity corresponds to the t-th point among 30 points uniformly sampled in the tn strip. Δi is the inverse distance weighted average of the neighborhood strengths of this point. tn-1,tn The mean intensity deviation between strip tn-1 and tn. For tn-1 stripe and The intensity of the point closest to the point;

[0142] The optimal parameters w1 and w2 are calculated using the least squares method. Since w1 + w2 = 1, w2 is replaced by 1 - w1.

[0143] Within the same AGC intensity compensation anomaly zone, w1 and w2 remain unchanged, Δi t1-1,t1 Only for i t1 The abnormal scan line correction takes effect. The intensity correction of each point requires the calculation of the inverse distance weighted average of the intensity of the neighborhood of that point. For point-by-point intensity correction of different scan lines, it is necessary to calculate the intensity deviation between adjacent strips and the inverse distance weighted average of the neighborhood intensity of each point.

[0144] Repeat the above operation for different abnormal areas until the intensity of all areas has been corrected.

[0145] The beneficial effects of this invention are as follows:

[0146] By parsing LAS files, intensity attribute values, emission angle parameters, and point cloud coordinate data of airborne laser bathymetry records are obtained, and an index sharing mechanism is established. Water and land point clouds are segmented using point cloud coordinate information. A grid mean segmentation method is then used to segment the water surface and seabed point clouds. Based on the index sharing mechanism, surface point coordinates, intensity, and emission angle are removed, and then the coordinates and intensity are merged. Scan line segmentation is performed based on the emission angle, thereby achieving scan line segmentation of intensity and coordinate data. The Pearson correlation coefficient of adjacent scan lines is calculated on a per-scan-line basis, and a threshold is set to determine the interval of AGC-compensated abnormal scan lines. A neighborhood joint weighting method is designed to correct AGC-compensated abnormal scan lines, ultimately obtaining airborne laser bathymetry system intensity data with natural transitions that effectively express ground object attributes. This data is helpful for further seabed sediment classification and the improvement of marine spatial information.

[0147] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0148] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system, characterized in that, include: An index sharing mechanism is established, which includes: coordinate data, intensity data, and emission angle parameters of each point in the scanning area; The scanned area is segmented into land and water point clouds to obtain land point clouds and water point clouds. The water area point cloud is segmented to obtain surface point cloud and bottom point cloud; Based on the index sharing mechanism, the coordinate data, intensity data and emission angle parameters corresponding to the water surface point cloud are removed to obtain the coordinate data, intensity data and emission angle parameters corresponding to the point cloud after removal. Based on the emission angle parameters corresponding to the removed point cloud, the coordinate data and intensity data corresponding to the removed point cloud are divided into scan cycles based on the index sharing mechanism, wherein one scan cycle corresponds to one scan line; Correlation determination is performed on each adjacent scan line to identify the intensity compensation abnormal scan area; The intensity of each scan line in each of the intensity compensation abnormal scan regions is corrected according to the neighborhood joint weighting method to obtain the corrected image.

2. The method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system according to claim 1, characterized in that, The mechanism for establishing an index sharing mechanism includes: The laser depth sounding system is used to obtain the LAS file of the area to be scanned. The LAS file is parsed to obtain the coordinate data, intensity data, and emission angle parameters of each point in the area to be scanned; An index sharing mechanism is established using the coordinate data, intensity data, and emission angle parameters.

3. The method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system according to claim 1, characterized in that, The scanned area is segmented into land and water point clouds to obtain land point clouds and water point clouds, including: The location of the deepest water area was determined by searching for the lowest elevation. The location of the deepest water area is set as the center, and the highest elevation within the first area of ​​this center is searched to obtain the first threshold. The first threshold is used to segment the water and land point clouds to obtain land point clouds and water point clouds.

4. The method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system according to claim 1, characterized in that, The segmentation of the water area point cloud to obtain surface point cloud and bottom point cloud includes: The water area point cloud is divided into grids, and the average elevation of the highest and lowest points within each grid is calculated to obtain the second threshold. Determine whether the elevation of the water point cloud within each grid range is greater than the second threshold. If it is, it is a surface point cloud; otherwise, it is a bottom point cloud.

5. The method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system according to claim 1, characterized in that, The step of determining the correlation between adjacent scan lines to identify intensity-compensated abnormal scan areas includes: Extract the intensity data of each adjacent scan line; Calculate the Pearson correlation coefficient of adjacent scan lines based on the intensity data of each adjacent scan line; Determine whether the Pearson correlation coefficient is greater than the third threshold. If it is, the (N+1)th scan line is a normal scan line; if not, the (N+1)th scan line is a compensated abnormal scan line. Based on the aforementioned abnormal intensity scan lines, the scanning area between every two abnormal intensity scan lines is defined as the abnormal intensity scan area.

6. The method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system according to claim 5, characterized in that, The step of calculating the Pearson correlation coefficient of adjacent scan lines based on the intensity data of each adjacent scan line includes: Calculate the mean value of the intensity data for the Nth scan line; Calculate the mean value of the intensity data for the (N+1)th scan line; The Pearson correlation coefficient between adjacent scan lines is calculated based on the mean intensity data of the Nth scan line and the mean intensity data of the (N+1)th scan line.

7. The method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system according to claim 1, characterized in that, The step of correcting the intensity of each scan line in each of the intensity-compensated abnormal scan regions according to the neighborhood joint weighting method to obtain the corrected image includes: The sampling data of the Nth and N+1th scan lines in the abnormal region are collected using a uniform sampling method; Calculate the mean intensity and the deviation between the means of the two sets of sampling data respectively; Based on the mean intensity of the two sets of sampled data and the deviation between the mean intensity, the neighborhood inverse distance weighted mean of each sampled data in the abnormal scan line is calculated sequentially. Based on the neighborhood inverse distance weighted mean of each sampled data, construct the neighborhood joint weighted strength correction equation for that point and define the objective function. Determine the parameters of the neighborhood joint weighted strength correction equation using the least squares method. The intensity data of the current AGC compensation abnormal scan line are corrected sequentially according to the neighborhood joint weighted intensity correction equation to obtain the corrected image.

8. The method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system according to claim 7, characterized in that, The step of calculating the neighborhood inverse distance weighted mean of each sampled data point in the abnormal scan line based on the mean intensity of the two sampled data points and the deviation between the mean intensity values ​​includes: The neighborhood of a point is defined by taking the sampling point as the center, a radius of 2.5 times the spatial resolution of the point cloud, a central angle of 100 degrees, and a direction perpendicular to the scan line and pointing towards the normal intensity area. The inverse distance weighted average of the intensity within this neighborhood is then calculated.

9. The method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system according to claim 7, characterized in that, The expression for the corrected equation is: Where w1 and w2 are weights, and w1 + w2 = 1. For scan line t1, the corrected intensity value at the j-th point. To correct the previous strength, Δi is the inverse distance weighted average of the neighborhood strengths. t1-1,t1 This represents the strength deviation between strip t1-1 and strip t1.

10. The method for correcting intensity anomalies in automatic gain control of an airborne laser depth sounding system according to claim 7, characterized in that, The expression for the objective function is: in, in, The corrected intensity corresponds to the t-th point among 30 points uniformly sampled in the tn strip. Δi is the inverse distance weighted average of the neighborhood strengths of this point. tn-1,tn The mean intensity deviation between strip tn-1 and tn. For tn-1 stripe and The intensity of the point closest to the point.

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