CN114779215A - Data denoising method for spaceborne photon counting laser radar in planting coverage area - Google Patents

Data denoising method for spaceborne photon counting laser radar in planting coverage area Download PDF

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CN114779215A
CN114779215A CN202210427314.4A CN202210427314A CN114779215A CN 114779215 A CN114779215 A CN 114779215A CN 202210427314 A CN202210427314 A CN 202210427314A CN 114779215 A CN114779215 A CN 114779215A
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王虹
何光辉
张永安
方强
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Kunming University of Science and Technology
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    • G—PHYSICS
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    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

The invention discloses a data denoising method for a satellite-borne photon counting laser radar in a plant covered area. And then, fine denoising is carried out, the circular neighborhood in the original algorithm is replaced by an elliptical neighborhood with automatically adjusted ellipticity, a slope angle range is calculated by a method of connecting lines of photon points at the center of the maximum density of the small window, the number of photon points contained in the elliptical neighborhood of each photon point is determined according to the slope angle range and is used as the density of the photon points, and noise photons are filtered according to a threshold after a histogram calculation threshold is constructed. The invention has higher accuracy in plant covered areas, including mountain areas with complex terrains, and has better effect in removing background noise and system noise. The algorithm lays a good foundation for acquiring subsequent data classification, tree height measurement, vegetation coverage, biomass and the like.

Description

Data denoising method for spaceborne photon counting laser radar in planting coverage area
Technical Field
The invention relates to a data denoising method for a satellite-borne photon counting laser radar in a planting covered area, and belongs to the technical field of satellite-borne laser radars.
Background
The ATLAS (The Advanced positioning satellite-2) carries an ATLAS (The Advanced positioning satellite Altimeter system) height measuring system in 9 and 15 months in 2018, The ATLAS adopts a range finding mode, and has higher detection precision than a full-waveform recovery radar due to extremely high detection frequency and sensitive photon response, wherein The ATLAS carries The ICESat-2(The ice closed and The land elevation satellite-2) satellite which carries The single-photon height measuring system successfully transmits, is different from a full-waveform recovery height measuring system of a previous generation satellite ICESat (The ice closed and The land elevation satellite) at present. The application range of the current ATLAS system data mainly includes: the method comprises the steps of shallow water depth measurement, inland water level monitoring, forest vegetation height measurement, biomass inversion, south-north two-stage ice cover and sea ice change monitoring.
The data products of ATLAS are totally 4-grade 21, and are currently available for download in the national Ice and snow data center. The data products used in the method mainly comprise ATL03 and ATL08, ATL03 is global positioning photon data, ATL08 is land and vegetation elevation data, and the data is processed by ATL03 data through official filtering and classification algorithms, and the data processed by the official algorithms can be obtained through internal data correlation.
Because the single photon measurement mechanism is used in the carried height measuring system, the sensitivity is high, and a large amount of noise can be generated while high precision is obtained. In order to obtain usable data, the original point cloud data is first subjected to noise reduction processing. The current main filtering algorithms include a local distance statistical algorithm, a clustering algorithm and an edge extraction algorithm of a grid image. The filtering algorithm provided by the official part is a DRAGANN (differential regression and Gaussian Adaptive New noise) algorithm, the algorithm is realized based on a local distance statistical algorithm, a density frequency histogram is constructed by counting the density of the center of a circular domain where each point is located, and a threshold value is searched by Gaussian fitting and an EM (effective magnetic) algorithm, so that noise is removed. From the data of ATL08, it can be seen that the filtering effect of the algorithm is not good in the covered area, especially in rugged and complex mountain forest areas, and the invention solves the problem well due to the characteristic of adaptability to the slope.
Disclosure of Invention
In order to realize the denoising of photon point cloud data in a forest region, the invention provides a method for carrying out rough denoising by using a maximum density center elevation search method and gradient adaptive denoising based on DBSCAN, which specifically comprises the following steps:
s1, coarse denoising by a maximum density center elevation search method:
s1.1, dividing the photon point cloud data into a plurality of equal-distance segments along the track, and selecting the size of a filtering window according to the terrain change condition; the forest region is rugged and has large fluctuation, so that the window is kept as small as possible, and a window of 30m is preferably used as a filtering window; the number of windows is M.
S1.2, calculating the maximum density photon point of each photon segment, wherein the calculation method is shown as the following formula.
Figure BDA0003608895470000021
In the formula NR(pi) Is a photon point pi(xi,yi) Number of photon points in the vicinity of a circle of radius R, x, y being the coordinates of the photon points, pi,pjIs a photonic dot, distance (p)i,pj) Is a photon point pi,pjDistance between, DesmaxIs NR(pi) Of (c) is calculated.
S1.3, recording the coordinates (x, y) of the center point of each density in the data set C.
S1.4, obtaining the elevation H of the maximum density photon point of each windowm＝ym(m < N), setting the elevation range as [ H [m-50,Hm+50]。
And S1.5, calibrating photons outside the elevation range into noise and eliminating the noise, wherein the photons within the elevation range are signals.
S2, denoising by using a gradient adaptive density clustering algorithm based on DBSCAN:
and S2.1, re-determining a filtering window, and defining the window size to be 500 m.
S2.2, extracting a data set C and calculating a slope angle range as shown in the formula (2).
Figure BDA0003608895470000022
And S2.3, determining the major and minor axes a and b of the elliptical domain.
S2.4, calculating the photon point number of the ellipse neighborhood of each point in the window, wherein the calculation formula is (4), in order to determine the photon point number of the ellipse neighborhood in each photon point cloud data, firstly, traversing all the photon points to judge whether a certain photon is a neighborhood photon point, and using the photon point p (x)p,yp),q(xq,yq) To determine whether photon point q is in the elliptical neighborhood of photon point p, for example, dis can first be calculated by equation (3),
Figure BDA0003608895470000031
Figure BDA0003608895470000032
when dis < 1, point q is within the elliptical domain of point p; den (theta)i) Expressed as the angle theta between the major axis of the ellipse and the horizontal axisiThe number of photon points in the elliptic domain of the point p, theta epsilon [ min (phi), max (phi) ]]。
S2.5, establishing a frequency histogram for the Den of all the points in the window.
And S2.6, forming two Gaussian peaks by a method of fitting a Gaussian function, wherein the abscissa of the intersection point of the Gaussian peaks is the filtering threshold.
S2.7, comparing the Den of all photon points with a threshold, if the Den is larger than the threshold, determining the Den as a signal photon, otherwise, determining the Den as noise, and removing the noise.
The invention has the beneficial effects that: compared with the traditional DBSCAN denoising algorithm, the algorithm uses a multilayer denoising structure, utilizes the maximum density center elevation search method for rough denoising, can achieve quick rough denoising, and reduces the calculation amount of the subsequent clustering algorithm; meanwhile, the slope adaptive DBSCAN clustering denoising algorithm uses an elliptical domain adaptive to the slope direction as a clustering neighborhood, so that signal photons can be well reserved when filtering data of steep terrain, and excessive denoising is avoided.
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Fig. 1 is a cloud of original ATL03 data points.
Fig. 2 is a point cloud image after coarse denoising through maximum density center search.
FIG. 3 is a point cloud image after being denoised by the gradient adaptive denoising algorithm based on DBSCAN.
FIG. 4 is a flowchart of a maximum density center coarse denoising algorithm.
FIG. 5 is a flow chart of a gradient adaptive denoising algorithm based on DBSCAN.
Detailed Description
The following description of the present invention will be made in detail, with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown.
The method for denoising satellite-borne laser radar data in vegetation coverage areas comprises the following steps.
And S1, logging in the national ice and snow data center, and selecting ATL03 data to download.
S2, extracting the required data, which are the photon elevation h _ ph and the along-track distance along _ track, corresponding to the photon coordinate information (x, y), and the initial point cloud data map is shown in fig. 1.
S3, roughly denoising by a maximum density center elevation search method, wherein the denoising process is shown in FIG. 4:
s3.1, dividing the photon point cloud data into a plurality of equal-track distance segments, and selecting the size of a filtering window according to the terrain change condition; for mountain forests with rugged terrain, the terrain relief is large, the window is selected to be small, and a window of 30m is divided to be used as a filtering window; for a large-area planting area mainly comprising crops, the window can be a little larger to reduce repeated calculation, the window is divided into 50m serving as a filtering window, and the number of the windows is N.
S3.2, calculating the maximum density photon point of each photon fragment, wherein the calculation method is shown as the formula (1)
Figure BDA0003608895470000041
S3.3, recording the coordinates (x, y) of the center point of each density is stored in the data set C.
S3.4, obtaining the elevation H of the maximum density photon point of each windowm＝ym(m < N), setting the elevation range as [ H ]m-50,Hm+50]Photons outside the elevation range are marked as noise and removed, and those within the elevation range are signals, and the denoising result is shown in fig. 2.
S4, denoising by using a gradient adaptive density clustering algorithm based on DBSCAN, wherein the denoising process is shown in figure 5:
and S4.1, re-determining a filtering window, and defining the window size to be 500 m.
And S4.2, extracting a data set C to calculate the range of the slope angle, wherein the calculation formula is shown as the formula (2).
Figure BDA0003608895470000042
And S4.3, defining the major axis and the minor axis a and b of the elliptical domain denoising, wherein a is generally 6b for the covered area of forest planting, and a is generally 9b for the crop growing area.
S4.4, calculating the photon point number Den of the ellipse neighborhood for each point in the window
S4.5, establishing a frequency histogram for the Den at the point in the window.
And S4.6, forming two Gaussian peaks by a method of fitting a Gaussian function, wherein the abscissa of the intersection point of the Gaussian peaks is the filtering threshold.
S4.7, the Den of all photon points is a signal if the Den is larger than a threshold value, and is noise if the Den is not larger than the threshold value.
The point cloud data filtering of the satellite-borne photon counting laser radar can be completed through the steps, and a final denoising result is shown in fig. 3.
According to the invention, aiming at the density distribution characteristics of data noise and signal photons of the satellite-borne single photon laser radar, a maximum photon density center based denoising method is utilized, a gradient adaptive algorithm based on a DBSCAN algorithm is utilized for denoising, and finally a better result is obtained, and the following is an application example of the invention.
In the embodiment 1, useful forest vegetation information is extracted by processing satellite-borne photon counting radar data in the northeast great Xingan ridge region by using the algorithm, and the information has important reference values for preventing forest fires, understanding forest ecosystem processes, formulating forest management and recovery policies to reduce global climate warming and the like.
Example 2
The algorithm of the invention is used for processing satellite-borne photon counting radar data of a large apple plantation in Yunnan, extracting vegetation information of the apple plantation, and analyzing the vegetation information of different time periods, thereby knowing the growth situation of the apple plantation and making a planting plan. Saving a large amount of manpower and material resources.
Example 3
The algorithm of the invention is used for processing satellite-borne photon counting radar data of a large wheat planting field in a plain area, so as to obtain vegetation information of a planting park, and realize real-time monitoring, yield prediction and pest control of wheat growth by referring to the vegetation information.

Claims (2)

1. A data denoising method for a satellite-borne photon counting laser radar of a plant covered area is characterized by comprising the following steps:
s1, coarse denoising by a maximum density center elevation search method:
s1.1, dividing the photon point cloud data into a plurality of equal-distance segments along the track, and selecting the size of a filtering window according to the terrain change condition, wherein the number of the windows is M;
s1.2, calculating the maximum density photon point of each photon segment, wherein the calculation method is shown as the following formula:
Figure FDA0003608895460000011
in the formula NR(pi) Is a photon point pi(xi,yi) Number of photon points in the neighborhood of a circle of radius R, x, y being the coordinates of the photon points, pi,pjIs a photon spot, distance (p)i,pj) Is a photon point pi,pjDistance between, density of photon spots DesmaxIs NR(pi) Maximum value of (d);
s1.3, recording coordinates (x, y) of the maximum density central point of each photon fragment, and collecting the coordinates into a data set C;
s1.4, obtaining the elevation H of the maximum density photon point of each windowm＝yi(y belongs to C), and setting an elevation range as [ H ]m-50,Hm+50](ii) a Is the window size 30-30?
S1.5, calibrating photons outside an elevation range into noise and eliminating the noise, wherein the photons within the elevation range are signals;
s2, denoising by using a gradient adaptive density clustering algorithm based on DBSCAN:
s2.1, re-determining a filtering window, and defining the window size to be 500 m;
s2.2, extracting a data set C and calculating a slope angle range as shown in the formula (2).
Figure FDA0003608895460000012
S2.3, determining the major and minor axes a and b of the elliptical domain;
s2.4, calculating the photon point number of the ellipse neighborhood of each point in the window, wherein the calculation formula is (4), in order to determine the photon point number of the ellipse neighborhood in each photon point cloud data, firstly, traversing all the photon points to judge whether a certain photon is a neighborhood photon point, and determining a photon point q (x)q,yq) Whether or not at photon point p (x)p，yp) Ellipse of (2)Neighborhood, dis can be first calculated by equation (3):
Figure FDA0003608895460000021
Figure FDA0003608895460000022
wherein a and b are long and short axes of an elliptical domain, and when dis is less than 1, a point q is in the elliptical domain of a point p; den (theta)i) Expressed as the angle theta between the major axis of the ellipse and the horizontal axisiIn time, the number of photon points in the elliptical domain of point p, θ ∈ [ min (φ), max (φ)](ii) a Phi is a slope angle;
s2.5, establishing a frequency histogram for the Den of all the points in the window;
s2.6, forming two Gaussian peaks by a method of fitting a Gaussian function, wherein the abscissa of the intersection point of the Gaussian peaks is the filtering threshold;
s2.7, comparing the Den of all photon points with a threshold, if the Den is larger than the threshold, determining the Den as a signal photon, otherwise, determining the Den as noise, and removing the noise.
2. The method for denoising the satellite-borne photon counting laser radar data of the vegetation coverage area according to claim 1, wherein: and selecting a window of 30-50 m as a filtering window.
CN202210427314.4A 2022-04-21 2022-04-21 Data denoising method for spaceborne photon counting laser radar in planting coverage area Pending CN114779215A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115825920A (en) * 2023-02-10 2023-03-21 中国科学院精密测量科学与技术创新研究院 ICESat-2 photon denoising method considering glacier morphology
CN116243273A (en) * 2023-05-09 2023-06-09 中国地质大学（武汉） Photon counting laser radar data filtering method and device
CN116953661A (en) * 2023-09-20 2023-10-27 中国地质大学（武汉） Photon counting laser radar self-adaptive nuclear density estimation filtering method and device

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115825920A (en) * 2023-02-10 2023-03-21 中国科学院精密测量科学与技术创新研究院 ICESat-2 photon denoising method considering glacier morphology
CN116243273A (en) * 2023-05-09 2023-06-09 中国地质大学（武汉） Photon counting laser radar data filtering method and device
CN116243273B (en) * 2023-05-09 2023-09-15 中国地质大学（武汉） Photon counting laser radar data filtering method for vegetation canopy extraction
CN116953661A (en) * 2023-09-20 2023-10-27 中国地质大学（武汉） Photon counting laser radar self-adaptive nuclear density estimation filtering method and device
CN116953661B (en) * 2023-09-20 2023-12-15 中国地质大学（武汉） Photon counting laser radar self-adaptive nuclear density estimation filtering method and device

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