CN111047704A - Multi-beam sounding data gross error automatic clearing method for improving region growing algorithm - Google Patents
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Abstract
The invention belongs to the field of marine surveying and mapping data post-processing, in particular to a multi-beam sounding data gross error automatic clearing method for improving a region growing algorithm, which comprises the following steps of 1: analyzing data: analyzing original multi-Beam data, outputting standardized data of Ping number, Beam number, longitude, latitude and water depth, and setting a threshold value for data rejection; step 2: constructing a Delaunay triangulation network: constructing an irregular triangular network TIN by adopting a Delaunay growth method according to the longitude, the latitude and the water depth; and step 3: selecting a seed point: the first step of the regional growth is the selection of seed points, the method takes the submarine topography as a standard when selecting the seed points, and selects the most reliable water depth point as the seed point; and 4, step 4: and (3) removing the depth sounding data gross error by using an improved region growing algorithm: starting to carry out regional growth from the seed points, and judging whether the water depth is an abnormal value or not according to a judging method; and 5: outputting a normal water depth point: and deleting the data marked as the abnormal water depth point and outputting the normal water depth point.
Description
Technical Field
The invention belongs to the field of marine surveying and mapping data post-processing, and particularly relates to a multi-beam sounding data gross error automatic clearing method for improving a region growing algorithm.
Background
The traditional multi-beam outlier clearing method mainly comprises two modes of manual interaction and automatic algorithm.
Mature commercial software such as Caris and Fledermaus mostly adopt a manual interaction method to remove abnormal values, and the process usually needs to consume a large amount of manpower and time, so that the efficiency is low; the currently applied automatic algorithm mainly comprises median filtering, wavelet analysis, trend surface filtering and the like, wherein a part of filtering methods are easy to delete real terrain data by mistake, most of filtering methods are based on a coordinate grid and carry out filtering along or perpendicular to a trajectory line direction, the correlation of each water depth point is not fully considered, and the method cannot be well adapted to the complexity of submarine topography.
Therefore, a method for automatically clearing the data gross should be designed to overcome the above disadvantages.
Disclosure of Invention
The invention aims to provide a multi-beam depth sounding data gross error automatic clearing method for an improved region growing algorithm by applying a region growing algorithm and a depth sounding data point topological relation established by Delaunay to overcome the defects of the prior art.
The technical scheme adopted by the invention is as follows:
a multi-beam sounding data gross error automatic clearing method for improving a region growing algorithm is characterized by comprising the following steps:
step 1: analyzing data: analyzing original multi-Beam data, outputting standardized data of Ping number, Beam number, longitude, latitude and water depth, and setting a threshold value for data rejection;
step 2: constructing a Delaunay triangulation network: constructing an irregular triangular network TIN by adopting a Delaunay growth method according to the longitude, the latitude and the water depth;
and step 3: selecting a seed point: the first step of the regional growth is the selection of seed points, the method takes the submarine topography as a standard when selecting the seed points, and selects the most reliable water depth point as the seed point;
and 4, step 4: and (3) removing the depth sounding data gross error by using an improved region growing algorithm: starting to carry out regional growth from the seed points, and judging whether the water depth is an abnormal value or not according to a judging method;
and 5: outputting a normal water depth point: and deleting the data marked as the abnormal water depth point, and only outputting the normal water depth point.
Further, in step 1, the threshold for data elimination is preferably set to be 2.5% of the water depth value.
Further, in the step 2, the constructing of the irregular triangulation network TIN includes the following steps:
2.1 optional Point P of concentration at discrete Water depth points1；
2.2 selecting distance P in a set of discrete water depth points1Nearest point P2Forming a first reference edge;
2.3 on the right side of the first reference edge, finding a third point P according to a judgment rule that the maximum opening angle criterion of Delaunay triangulation is taken as the third point3I.e. PiAs shown in formula (1), the first two points are connected to form a first Delaunay triangle;
and 2.4, searching other points capable of constructing the Delaunay triangle by taking three edges of the initial triangle as a reference until all discrete sampling points are traversed, and thus forming the irregular triangular network TIN.
Further, in step 3, the seed points are selected by the following method, including:
3.1 traversing all triangles of which each point participates in construction in the Delaunay triangle, and determining all vertexes of the triangle of which the point participates in construction;
3.2 calculating the standard deviation of the water depth value of the point and all the vertexes of the triangle participating in construction, as shown in formula (2), wherein n is the number of vertexes, and x isiThe value of the water depth of the vertex is,
the water depth average of the vertex is taken. The smaller the standard deviation is, the more continuous the topographic variation of the point is, the point is the real water depth value and can be used as a seed point for regional growth
Further, in step 4, the method for determining the abnormal water depth value includes:
4.1 in the discrete depth point set, finding the point P nearest to the seed point P1Forming a first reference edge;
4.2 on the right side of the reference edge, finding the third vertex P of the triangle according to the maximum opening angle criterion2；
4.3 calculating points P separately2Water depth value of (1) and P1Whether the difference value of the water depth values exceeds a threshold value or not, if both the water depth values exceed the threshold value, the point is considered to be an abnormal value and should be deleted, and the point is marked as a gross difference point; otherwise, the point is considered to be a normal water depth point and should be reserved;
4.4 at P P2Or P1P2Continuing the region growing for the reference, if P2If the water depth is a normal water depth point, judging according to the step 4.3; if P2The abnormal water depth point is obtained according to P and P1The depth value of the water is calculated by inverse distance weighted interpolation2The water depth value of (2) is shown in formula (3), wherein ZiIs the water depth value of the abnormal water depth point, diIs an abnormal pointDistance from the other two vertices of the triangle. When the name is P P2Finding the third point P of the triangle for reference to continue region growing3According to the modified P2And judging whether the water depth value is an abnormal water depth point or not according to the water depth value and the water depth value at the point P.
4.5 repeating the steps 4.3 and 4.4 until the water depth point can not find the point meeting the maximum opening angle criterion.
The invention has the advantages and positive effects that:
the method aims at automatically clearing the multi-beam abnormal value, combines the topological relation constructed by the Delaunay triangulation network, fully considers the correlation of each water depth point, improves the region growing algorithm by editing the water depth value of the data gross error for the first time, and realizes the automatic clearing of the multi-beam sounding data gross error. The experimental result proves that the method can accurately identify and clear the rough error of the multi-beam sounding data, changes the current situation of clearing the abnormal multi-beam sounding value based on the grid and the air trace, can fully consider the correlation of each discrete point compared with a processing mode based on the grid, and is better suitable for the complexity of the submarine topography.
Drawings
FIG. 1 is a flow chart of the method of the present invention for automatic gross error removal;
FIG. 2 is a schematic view of improved region growth;
FIG. 3 is a partial enlarged view of a triangular mesh generated by experimental data construction;
FIG. 4 is a comparison graph of point clouds and terrain before and after gross error removal of experimental data in the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be illustrative, not limiting and are not intended to limit the scope of the invention.
A multi-beam sounding data gross error automatic clearing method for improving a region growing algorithm is characterized by comprising the following steps: the method comprises the following steps, and the flow chart is shown in figure 1:
step 1: analyzing data: analyzing the original multi-Beam data, outputting the standardized data of Ping number, Beam number, longitude, latitude and water depth, and setting a threshold value for data rejection according to 2.5% of the water depth value;
step 2: constructing a Delaunay triangulation network: constructing an irregular triangular network TIN by adopting a Delaunay growth method according to the longitude, the latitude and the water depth;
2.1 optional Point P of concentration at discrete Water depth points1；
2.2 selecting distance P in a set of discrete water depth points1Nearest point P2Forming a first reference edge;
2.3 on the right side of the first reference edge, finding a third point p according to a judgment rule that the maximum opening angle criterion of Delaunay triangulation is taken as the third point3I.e. piAs shown in formula (1), the first two points are connected to form a first Delaunay triangle;
and 2.4, searching other points capable of constructing the Delaunay triangle by taking three edges of the initial triangle as a reference until all discrete sampling points are traversed, and thus forming the irregular triangular network TIN.
And step 3: selecting a seed point: the first step of the regional growth is the selection of seed points, the method takes the submarine topography as a standard when selecting the seed points, and selects the most reliable water depth point as the seed point;
3.1 traversing all triangles of which each point participates in construction in the Delaunay triangle, and determining all vertexes of the triangle of which the point participates in construction;
3.2 calculating the standard deviation of the water depth value of the point and all the vertexes of the triangle participating in construction, as shown in formula (2), the smaller the standard deviation is, the more continuous the terrain change of the point is, the point is the real water depth value, and the point can be used as a seed point for regional growth
And 4, step 4: and (3) removing the depth sounding data gross error by using an improved region growing algorithm: starting to carry out regional growth from the seed points, and judging whether the water depth is an abnormal value or not according to a judging method;
in the step 4, the method for judging the abnormal water depth value includes:
4.1 in the discrete depth point set, finding the point P nearest to the seed point P1Forming a first reference edge;
4.2 on the right side of the reference edge, finding the third vertex P of the triangle according to the maximum opening angle criterion2；
4.3 calculating points P separately2Water depth value of (1) and P1Whether the difference value of the water depth values exceeds a threshold value or not, if both the water depth values exceed the threshold value, the point is considered to be an abnormal value and should be deleted, and the point is marked as a gross difference point; otherwise, the point is considered to be a normal water depth point and should be reserved;
4.4 at P P2Or P1P2Continuing the region growing for the reference, if P2If the water depth is a normal water depth point, judging according to the step 4.3; if P2The abnormal water depth point is obtained according to P and P1The depth value of the water is calculated by inverse distance weighted interpolation2The water depth value of (a) is shown in formula (3). When using PP2Finding the third point P of the triangle for reference to continue region growing3According to the modified P2And judging whether the water depth value is an abnormal water depth point or not according to the water depth value and the water depth value at the point P.
4.5 repeating the steps 4.3 and 4.4 until the water depth point can not find the point meeting the maximum opening angle criterion.
And 5: outputting a normal water depth point: and deleting the data marked as the abnormal water depth point, and only outputting the normal water depth point.
The working process of the invention is as follows:
the use of the present invention is described in connection with the following examples,
step 1: analyzing data: analyzing original multi-Beam sounding data, outputting Ping number, Beam number, longitude, latitude and water depth, obtaining 112912 water depth points in total, wherein the water depth range is [ -1169.006, -777.965], and the set threshold value is 30 m.
Step 2, constructing a Delaunay triangulation network, selecting seed points, namely ① 112912 discrete water depth points to construct 225799 triangles as shown in figure 3, ② selecting the seed points as the seed points for regional growth, wherein the standard deviation of the water depth values of all the vertexes of all the triangles participating in the construction of the 74996 th water depth point is the minimum 0.117 according to the calculation result.
And step 3: and (3) removing the gross error of the sounding data by using a region growing algorithm: according to the gross error determination method, 138 water depth points are finally marked as gross error points.
And 4, step 4: outputting a normal water depth point: and outputting normal water depth points which are not marked as gross errors according to Ping numbers, Beam numbers, longitudes, latitudes and water depths.
The point cloud before and after gross error rejection and the topographic map are shown in fig. 4.
Claims (5)
1. A multi-beam sounding data gross error automatic clearing method for improving a region growing algorithm is characterized by comprising the following steps: the method comprises the following steps:
step 1: analyzing data: analyzing original multi-Beam data, outputting standardized data of Ping number, Beam number, longitude, latitude and water depth, and setting a threshold value for data rejection;
step 2: constructing a Delaunay triangulation network: constructing an irregular triangular network TIN by adopting a Delaunay growth method according to the longitude, the latitude and the water depth;
and step 3: selecting a seed point: the first step of the regional growth is the selection of seed points, the method takes the submarine topography as a standard when selecting the seed points, and selects the most reliable water depth point as the seed point;
and 4, step 4: and (3) removing the depth sounding data gross error by using an improved region growing algorithm: starting to carry out regional growth from the seed points, and judging whether the water depth is an abnormal value or not according to a judging method;
and 5: outputting a normal water depth point: and deleting the data marked as the abnormal water depth point, and only outputting the normal water depth point.
2. The method for automatically clearing the course of the multi-beam sounding data of the improved region growing algorithm according to claim 1, wherein the method comprises the following steps: in the step 1, the threshold value for data elimination is preferably set to be 2.5% of the water depth value.
3. The method for automatically clearing the course of the multi-beam sounding data of the improved region growing algorithm according to claim 1, wherein the method comprises the following steps: in the step 2, the construction of the irregular triangulation network TIN comprises the following steps:
2.1 optional Point P of concentration at discrete Water depth points1；
2.2 selecting distance P in a set of discrete water depth points1Nearest point P2Forming a first reference edge;
2.3 on the right side of the first reference edge, finding a third point P according to a judgment rule that the maximum opening angle criterion of Delaunay triangulation is taken as the third point3I.e. PiAs shown in formula (1), the first two points are connected to form a first Delaunay triangle;
and 2.4, searching other points capable of constructing the Delaunay triangle by taking three edges of the initial triangle as a reference until all discrete sampling points are traversed, and thus forming the irregular triangular network TIN.
4. The method for automatically clearing the course of multi-beam sounding data of the improved region growing algorithm according to claim 1,
the method is characterized in that: in the step 3, the seed points are selected by the following method comprising:
3.1 traversing all triangles of which each point participates in construction in the Delaunay triangle, and determining all vertexes of the triangle of which the point participates in construction;
3.2 calculating the standard deviation of the water depth value of the point and all the vertexes of the triangle participating in construction, as shown in formula (2), the smaller the standard deviation is, the more continuous the terrain change of the point is, the point is the real water depth value, and the point can be used as a seed point for regional growth
5. The method for automatically clearing the course of the multi-beam sounding data of the improved region growing algorithm according to claim 1, wherein the method comprises the following steps: in the step 4, the method for judging the abnormal water depth value includes:
4.1 in the discrete depth point set, finding the point P nearest to the seed point P1Forming a first reference edge;
4.2 on the right side of the reference edge, finding the third vertex P of the triangle according to the maximum opening angle criterion2；
4.3 calculating points P separately2Water depth value of (1) and P1Whether the difference value of the water depth values exceeds a threshold value or not, if both the water depth values exceed the threshold value, the point is considered to be an abnormal value and should be deleted, and the point is marked as a gross difference point; otherwise, the point is considered to be a normal water depth point and should be reserved;
4.4 at P P2Or P1P2Continuing the region growing for the reference, if P2If the water depth is a normal water depth point, judging according to the step 4.3; if P2The abnormal water depth point is obtained according to P and P1The depth value of the water is calculated by inverse distance weighted interpolation2The water depth value of (a) is shown in formula (3). When using PP2Finding the third point P of the triangle for reference to continue region growing3According to the modified P2And judging whether the water depth value is an abnormal water depth point or not according to the water depth value and the water depth value at the point P.
4.5 repeating the steps 4.3 and 4.4 until the water depth point can not find the point meeting the maximum opening angle criterion.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112035430A (en) * | 2020-07-13 | 2020-12-04 | 国家海洋信息中心 | Boundary-constrained self-adaptive submarine topography water depth point detection and screening method |
| CN112907615A (en) * | 2021-01-08 | 2021-06-04 | 中国石油大学(华东) | Submarine landform unit contour and detail identification method based on region growing |
| CN113129328A (en) * | 2021-04-22 | 2021-07-16 | 中国电子科技集团公司第二十九研究所 | Target hotspot area fine analysis method |
| CN114509050A (en) * | 2021-12-31 | 2022-05-17 | 广州南方卫星导航仪器有限公司 | Water depth data processing method and system |
| CN115291182A (en) * | 2022-07-29 | 2022-11-04 | 连云港港口工程设计研究院有限公司 | Filtering method for channel single-beam dense water extraction depth |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102446367A (en) * | 2011-09-19 | 2012-05-09 | 哈尔滨工程大学 | Method for constructing three-dimensional terrain vector model based on multi-beam sonar submarine measurement data |
| CN104180873A (en) * | 2014-09-02 | 2014-12-03 | 长江航道测量中心 | Single-wave-beam depth finder water depth gross error detection and correction method and system |
| CN107229594A (en) * | 2017-05-05 | 2017-10-03 | 深圳市建设综合勘察设计院有限公司 | A kind of multibeam bathymetric data trend surface filtering method and system |
| CN108447125A (en) * | 2018-02-28 | 2018-08-24 | 哈尔滨工程大学 | A kind of Delaunay triangulation network rapid generation based on multibeam bathymetric data |
| CN110111375A (en) * | 2019-03-29 | 2019-08-09 | 中国地质大学（武汉） | A kind of Image Matching elimination of rough difference method and device under Delaunay triangulation network constraint |
-
2019
- 2019-10-29 CN CN201911036196.9A patent/CN111047704B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102446367A (en) * | 2011-09-19 | 2012-05-09 | 哈尔滨工程大学 | Method for constructing three-dimensional terrain vector model based on multi-beam sonar submarine measurement data |
| CN104180873A (en) * | 2014-09-02 | 2014-12-03 | 长江航道测量中心 | Single-wave-beam depth finder water depth gross error detection and correction method and system |
| CN107229594A (en) * | 2017-05-05 | 2017-10-03 | 深圳市建设综合勘察设计院有限公司 | A kind of multibeam bathymetric data trend surface filtering method and system |
| CN108447125A (en) * | 2018-02-28 | 2018-08-24 | 哈尔滨工程大学 | A kind of Delaunay triangulation network rapid generation based on multibeam bathymetric data |
| CN110111375A (en) * | 2019-03-29 | 2019-08-09 | 中国地质大学（武汉） | A kind of Image Matching elimination of rough difference method and device under Delaunay triangulation network constraint |
Non-Patent Citations (3)
| Title |
|---|
| XIAODONG CUI,ETC: "A method for multibeam seafloor terrain classification based on self-adaptive geographic classification unit" * |
| 张雪洋等: "基于三角网滤波检验的KD 树粗差剔除法" * |
| 彭彤;邹亚未;周平;卢凯乐;: "基于趋势面的多波束测深粗差剔除研究" * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112035430A (en) * | 2020-07-13 | 2020-12-04 | 国家海洋信息中心 | Boundary-constrained self-adaptive submarine topography water depth point detection and screening method |
| CN112035430B (en) * | 2020-07-13 | 2023-12-19 | 国家海洋信息中心 | Boundary constraint self-adaptive submarine topography water depth point detection and screening method |
| CN112907615A (en) * | 2021-01-08 | 2021-06-04 | 中国石油大学(华东) | Submarine landform unit contour and detail identification method based on region growing |
| CN113129328A (en) * | 2021-04-22 | 2021-07-16 | 中国电子科技集团公司第二十九研究所 | Target hotspot area fine analysis method |
| CN113129328B (en) * | 2021-04-22 | 2022-05-17 | 中国电子科技集团公司第二十九研究所 | Target hotspot area fine analysis method |
| CN114509050A (en) * | 2021-12-31 | 2022-05-17 | 广州南方卫星导航仪器有限公司 | Water depth data processing method and system |
| CN114509050B (en) * | 2021-12-31 | 2023-07-21 | 广州南方卫星导航仪器有限公司 | Water depth data processing method and system |
| CN115291182A (en) * | 2022-07-29 | 2022-11-04 | 连云港港口工程设计研究院有限公司 | Filtering method for channel single-beam dense water extraction depth |
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