CN111047704B - Multi-beam sounding data rough error automatic clearing method for improving region growing algorithm - Google Patents
Multi-beam sounding data rough error automatic clearing method for improving region growing algorithm Download PDFInfo
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Abstract
The invention belongs to the field of post-processing of marine mapping data, in particular to an automatic multi-beam sounding data rough difference removing method for improving a region growing algorithm, which comprises the following steps of: analyzing data: analyzing the 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: according to longitude, latitude and water depth, adopting a Delaunay growth method to construct an irregular triangular network TIN; step 3: selecting a seed point: the first step of region growth is the selection of seed points, and the invention selects the most reliable water depth point as the seed point by taking the submarine topography as a standard when selecting the seed point; step 4: removing depth measurement data gross errors by using an improved region growing algorithm: starting to perform regional growth from the seed points, and judging whether the water depth is an abnormal value according to a judging method; step 5: outputting normal water depth points: 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 mapping data post-processing, and particularly relates to a multi-beam sounding data rough 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, fledermaus mostly adopts a manual interaction method to remove abnormal values, and the process usually needs to consume a great deal 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, 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 filter along or perpendicular to the direction of a navigation path, correlation of each water depth point is not fully considered, and complexity of submarine topography cannot be well adapted.
Therefore, a method for automatically removing the data rough difference should be designed to address the above-mentioned shortcomings.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art and provides a multi-beam sounding data rough difference automatic clearing method of an improved region growing algorithm applying a region growing algorithm and a sounding data point topological relation established by Delaunay.
The technical scheme adopted by the invention is as follows:
the multi-beam sounding data coarse error automatic clearing method for improving the region growing algorithm is characterized by comprising the following steps of:
step 1: analyzing data: analyzing the 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: according to longitude, latitude and water depth, adopting a Delaunay growth method to construct an irregular triangular network TIN;
step 3: selecting a seed point: the first step of region growth is the selection of seed points, and the invention selects the most reliable water depth point as the seed point by taking the submarine topography as a standard when selecting the seed point;
step 4: removing depth measurement data gross errors by using an improved region growing algorithm: starting to perform regional growth from the seed points, and judging whether the water depth is an abnormal value according to a judging method;
step 5: outputting normal water depth points: deleting the data marked as the abnormal water depth point, and only outputting the normal water depth point.
Further, in the step 1, the threshold value for setting the data rejection is preferably 2.5% of the water depth value.
Further, in the step 2, the construction of the irregular triangular net TIN includes the following steps:
2.1 optional Point P in the discrete Water depth Point set 1 ；
2.2 selecting distance P in a discrete Water depth Point set 1 Nearest point P 2 Forming a first datum edge;
2.3 on the right side of the first reference edge, finding a third point P according to the maximum opening angle criterion of Delaunay triangulation as the third point discriminant 3 Namely P i As shown in formula (1), the first two points are respectively connected to form a first Delaunay triangle;
2.4 searching other points capable of constructing Delaunay triangle by taking three sides of the initial triangle as reference until all discrete sampling points are traversed, namely forming the irregular triangle network TIN.
Further, in the step 3, the selecting of the seed points adopts 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 triangles of which the point participates in construction;
3.2 calculating the standard deviation of the water depth values of the point and all vertexes of the triangle participating in construction, wherein n is the number of vertexes and x is shown in the formula (2) i Is the value of the water depth of the top point,is the average value of the water depth of the vertex. The smaller standard deviation shows that the more continuous the topography change of the point is, the point is the true water depth value and can be used as the seed point for the regional growth
Further, in the step 4, the method for judging the abnormal value of the water depth includes:
4.1 concentrating at discrete Water depth points, finding the point P closest to the seed point P 1 Forming a first reference edge;
4.2 finding the third vertex P of the triangle to be constructed on the right side of the reference edge according to the maximum opening angle criterion 2 ；
4.3 calculating the Point P respectively 2 Water depth value of (2) and P 1 If the difference value of the water depth values exceeds the threshold value, the point is considered to be an abnormal value to be deleted when the difference value of the water depth values exceeds the threshold value, and the abnormal value is marked as a rough difference point; otherwise, the point is considered to be a normal water depth point to be reserved;
4.4 at P P 2 Or P 1 P 2 To continue the region growth for the reference, if P 2 Judging the water depth point to be a normal water depth point according to the step 4.3; if P 2 Is an abnormal water depth point, then according to P and P 1 Calculating P using inverse distance weighted interpolation 2 As shown in formula (3), wherein Z i D is the water depth value of the abnormal water depth point i Is the distance between the abnormal point and the other two vertexes of the triangle. When P P 2 Finding the third point P of the triangle for reference continued region growth 3 When according to the modified P 2 And judging whether the water depth value and the P point water depth value are abnormal water depth points or not.
4.5 repeating the steps 4.3 and 4.4 until the water depth point cannot find the point meeting the maximum opening angle criterion.
The invention has the advantages and positive effects that:
the method aims at automatically removing the abnormal value of the multi-beam, combines the topological relation constructed by the Delaunay triangulation network, fully considers the mutual relevance of all the water depth points, improves the region growing algorithm by editing the water depth value of the data gross error for the first time, and realizes the automatic removal of the multi-beam sounding data gross error. Experimental results prove that the multi-beam sounding data rough difference can be accurately identified and removed, the current situation of removing the multi-beam sounding abnormal value based on the grid and the path line is changed, and compared with the processing mode based on the grid, the correlation of each discrete point can be fully considered, and the multi-beam sounding abnormal value removing method is better suitable for the complexity of submarine topography.
Drawings
FIG. 1 is a flow chart of an automatic rough error removal method according to the present invention;
FIG. 2 is a schematic diagram of improved area growth;
FIG. 3 is a partial enlarged view of a triangular network generated by experimental data construction;
fig. 4 is a graph showing the comparison of point clouds and topography before and after the rough cleaning of experimental data in the present invention.
Detailed Description
The invention will now be further illustrated by reference to 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 rough error automatic clearing method for improving a region growing algorithm is characterized in that: the method comprises the following steps that the flow chart is shown in fig. 1:
step 1: analyzing data: analyzing the 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 according to 2.5% of the water depth value;
step 2: constructing a Delaunay triangulation network: according to longitude, latitude and water depth, adopting a Delaunay growth method to construct an irregular triangular network TIN;
2.1 at discrete Water depth Point setsOptionally at a point P 1 ；
2.2 selecting distance P in a discrete Water depth Point set 1 Nearest point P 2 Forming a first datum edge;
2.3 on the right side of the first reference edge, finding a third point p according to the maximum opening angle criterion of Delaunay triangulation as the third point discriminant 3 I.e. p i As shown in formula (1), the first two points are respectively connected to form a first Delaunay triangle;
2.4 searching other points capable of constructing Delaunay triangle by taking three sides of the initial triangle as reference until all discrete sampling points are traversed, namely forming the irregular triangle network TIN.
Step 3: selecting a seed point: the first step of region growth is the selection of seed points, and the invention selects the most reliable water depth point as the seed point by taking the submarine topography as a standard when selecting 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 triangles of which the point participates in construction;
3.2 calculating the standard deviation of the water depth values of the point and all vertexes of the triangle participating in construction, wherein as shown in the formula (2), the smaller the standard deviation is, the more continuous the topography change of the point is, the point is the real water depth value and can be used as the seed point for region growth
Step 4: removing depth measurement data gross errors by using an improved region growing algorithm: starting to perform regional growth from the seed points, and judging whether the water depth is an abnormal value according to a judging method;
in the step 4, the judgment of the abnormal value of the water depth adopts the following method that:
4.1 in the separationConcentrated water scattering deep points, finding the point P nearest to the seed point P 1 Forming a first reference edge;
4.2 finding the third vertex P of the triangle to be constructed on the right side of the reference edge according to the maximum opening angle criterion 2 ；
4.3 calculating the Point P respectively 2 Water depth value of (2) and P 1 If the difference value of the water depth values exceeds the threshold value, the point is considered to be an abnormal value to be deleted when the difference value of the water depth values exceeds the threshold value, and the abnormal value is marked as a rough difference point; otherwise, the point is considered to be a normal water depth point to be reserved;
4.4 at P P 2 Or P 1 P 2 To continue the region growth for the reference, if P 2 Judging the water depth point to be a normal water depth point according to the step 4.3; if P 2 Is an abnormal water depth point, then according to P and P 1 Calculating P using inverse distance weighted interpolation 2 As shown in formula (3). When in PP 2 Finding the third point P of the triangle for reference continued region growth 3 When according to the modified P 2 And judging whether the water depth value and the P point water depth value are abnormal water depth points or not.
4.5 repeating the steps 4.3 and 4.4 until the water depth point cannot find the point meeting the maximum opening angle criterion.
Step 5: outputting normal water depth points: 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 the original multi-Beam sounding data, outputting Ping number, beam number, longitude, latitude and water depth, obtaining 112912 water depth points in total, setting the water depth range as [ -1169.006, -777.965], and setting the threshold as 30m.
Step 2: constructing a Delaunay triangle network, and selecting seed points: (1) 112912 discrete water depth points construct 225799 triangles, as shown in fig. 3; (2) according to the calculation result, the standard deviation of the water depth values of all the vertices of the triangle which participate in construction of the 74996 th water depth point is 0.117, and the triangle is selected as a seed point for regional growth.
Step 3: removing rough difference of sounding data by using a region growing algorithm: according to the rough difference judging method, 138 water depth points are finally marked as rough difference points.
Step 4: outputting normal water depth points: the normal water depth point which is not marked as the rough difference is output according to Ping number, beam number, longitude, latitude and water depth.
The point clouds before and after the rough difference elimination and the topography comparison chart are shown in fig. 4.
Claims (3)
1. A multi-beam sounding data rough error automatic clearing method for improving a region growing algorithm is characterized in that: the method comprises the following steps:
step 1: analyzing data: analyzing the 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: according to longitude, latitude and water depth, adopting a Delaunay growth method to construct an irregular triangular network TIN;
step 3: selecting a seed point: the first step of the region growth is the selection of seed points, the most reliable water depth point is selected as the seed point by taking the seabed topography as a standard when the seed point is selected, and the seed point is selected by adopting the following method which comprises the following steps:
3.1, traversing all triangles of which each point participates in construction in the Delaunay triangle, and determining all vertexes of the triangles of which the point participates in construction;
3.2, calculating the standard deviation of the water depth values of the point and all vertexes of the triangle participating in construction, wherein as shown in the following formula, the smaller the standard deviation is, the more continuous the topography change of the point is, and the point is a real water depth value and can be used as a seed point for regional growth;
wherein n is the number of vertexes, x i Is the value of the water depth of the top point,is the average value of the water depth of the vertex;
step 4: removing depth measurement data gross errors by using an improved region growing algorithm: starting to perform regional growth from the seed points, and judging whether the water depth is an abnormal value according to a judging method;
the judgment of the abnormal value of the water depth adopts the following method that:
4.1 concentrating at discrete Water depth points, finding the point P closest to the seed point P 1 Forming a first reference edge;
4.2 finding the third vertex P of the triangle to be constructed on the right side of the reference edge according to the maximum opening angle criterion 2 ；
4.3 calculating the Point P respectively 2 Water depth value of (2) and P 1 If the difference value of the water depth values exceeds the threshold value, the point is considered to be an abnormal value to be deleted when the difference value of the water depth values exceeds the threshold value, and the abnormal water depth point is marked; otherwise, the point is considered to be a normal water depth point to be reserved;
4.4 use of PP 2 Or P 1 P 2 To continue the region growth for the reference, if P 2 Judging the water depth point to be a normal water depth point according to the step 4.3; if P 2 Is an abnormal water depth point, then according to P and P 1 Calculating P using inverse distance weighted interpolation 2 As shown in formula (3), when the water depth is represented by PP 2 Finding the third point P3 of the triangle for the reference continued region growth according to the modified P 2 Judging whether the water depth value and the P point water depth value are abnormal water depth points or not;
4.5 repeating the steps 4.3 and 4.4 until the water depth point cannot find the point meeting the maximum opening angle criterion;
step 5: outputting normal water depth points: deleting the data marked as the abnormal water depth point, and only outputting the normal water depth point.
2. The method for automatically removing coarse and fine data of multi-beam sounding with improved area growing algorithm according to claim 1, wherein the method comprises the steps of: in the step 1, the threshold value for data rejection is set to be 2.5% of the water depth value.
3. The method for automatically removing coarse and fine data of multi-beam sounding with improved area growing algorithm according to claim 1, wherein the method comprises the steps of: in the step 2, the construction of the irregular triangular net TIN includes the following steps:
2.1 optional Point P in the discrete Water depth Point set 1 ；
2.2 selecting distance P in a discrete Water depth Point set 1 Nearest point P 2 Forming a first datum edge;
2.3 on the right side of the first reference edge, finding a third point P according to the maximum opening angle criterion of Delaunay triangulation as the third point discriminant 3 Namely P i As shown in formula (1), the two points are respectively connected with each other to form an initial triangle;
2.4 searching other points capable of constructing Delaunay triangle by taking three sides of the initial triangle as reference until all discrete sampling points are traversed, namely forming the irregular triangle network TIN.
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| 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 |
| CN112907615B (en) * | 2021-01-08 | 2022-07-26 | 中国石油大学(华东) | Submarine landform unit contour and detail identification method based on region growing |
| 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 |
| 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 |
| CN115291182B (en) * | 2022-07-29 | 2023-06-06 | 连云港港口工程设计研究院有限公司 | Filtering method for channel single-beam dense mining water depth |
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