CN114723911B - Seabed geographic entity boundary automatic identification method based on D-P algorithm and optimal path - Google Patents
Seabed geographic entity boundary automatic identification method based on D-P algorithm and optimal path Download PDFInfo
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Abstract
The invention discloses a seabed geographic entity boundary automatic identification method based on a D-P algorithm and an optimal path. The method comprises the steps of sequentially extracting water depth profiles of a water depth grid model from the transverse direction and the longitudinal direction, respectively performing rarefaction on the water depth profiles by using a secondary leading extreme point method and a D-P algorithm, obtaining geographic entity characteristic points through gradient, water depth and distance screening, and traversing all rows and columns of the water depth profiles to obtain a single seabed geographic entity boundary. And then, constructing a cost value model, and realizing the segmentation of the composite geographic entity by adopting an optimal path method to finish the automatic identification of the composite seabed entity boundary. The method overcomes the defect that the prior art is difficult to quantitatively determine the submarine geographic entity boundary, so that the definition of the entity range lacks basis and has high randomness. The method has the advantages of improving the precision of the complicated seabed geographic entity planning, improving the working efficiency, being convenient to operate, and having important application value in the aspects of seabed geographic entity planning, seabed landform and geomorphology and the like.
Description
Technical Field
The invention relates to the technical fields of seabed geographic entity planning, seabed place names, oceanography, seabed landform, oceanography, oceanographic mapping and oceanographic information system, deep sea mining, oceanographic engineering construction and the like, in particular to a seabed geographic entity boundary automatic identification method based on a D-P algorithm and an optimal path.
Background
The seabed geographic entity refers to a behavior that the seabed under the seawater is divided into measurable and borderline landform units, and the standard names of the landform units are given to the landform units according to certain naming standards and specifications, and the behavior is called seabed geographic entity naming. The classification and naming of seabed geographic entities are research hotspots of subjects such as current marine surveying and mapping, marine geology, seabed topography, marine geology, landform and the like.
The accurate demarcation of the submarine geographic entity boundary is a precondition and a main technical difficulty for developing submarine geographic entity classification and naming. The sea mountain is a clear and distinguishable sea bottom high land which is approximately in equal dimension distribution, and the sea bottom geographical entity with the fluctuation height difference of the top and the surrounding terrain of more than 1000 meters is calculated from the deepest equal depth line around the sea bottom geographical entity. The seashore is the most common type of submarine geographic entity in the deep sea ocean, and the naming proposals related to it are also the most. According to the morphological characteristics of the seashore, the seashore is mainly divided into two types, one type is called as single type seashore and exists in the form of independent individuals; the other type is called a composite type sea mountain, and exists in a mode that a plurality of sea mountains are connected together. Bealock is the natural boundary of a composite sea mountain, also called the saddle. In most cases, the landform characteristics of the bealock are obvious and are in an obvious U shape, but some bealocks of the sea mountains exist, the landform forms of the bealocks are complex and changeable, and a proper boundary line is difficult to find to divide a plurality of sea mountains. Therefore, the method has important significance for carrying out the technical attack of the boundary defining method for the submarine geographic entities represented by the seas and mountains and researching the definition and naming of the submarine geographic entities.
At present, boundary delineation of a geographic entity is mostly based on a manual semi-automatic delineation method, the method obtains an isobath based on topographic model calculation, combines three-dimensional relief dizzy, and manually judges the position of a boundary point of the geographic entity and delineates the boundary of the entity by a closed polygon through a two-dimensional/three-dimensional visual analysis method. The method has the advantages of intuition and simplicity, but has the obvious defects of large subjective randomness, inaccurate geographic entity boundary, large workload, long time and difficulty in large-scale extraction of the geographic entity boundary. In response to this problem, researchers at home and abroad have conducted research on identification technologies for some typical geographic entity units. Such as: and S & nchez and the like subtract the sea bottom topographic data with flat areas from the real sea bottom topographic data, and further combine with water depth threshold value judgment to realize the identification of the sea bottom high land boundary. Although the existing literature introduces the extraction of the geographic entity characteristic line, no technical research has been carried out on the boundary delineation of important submarine geographic entity units such as seas and mountains.
Disclosure of Invention
The invention provides a seabed geographic entity boundary automatic identification method based on a D-P algorithm and an optimal path, aiming at the defects in the prior art, the invention sequentially extracts water depth profiles of a water depth grid model from the transverse direction and the longitudinal direction, respectively utilizes a secondary leading extreme point method and the D-P algorithm to carry out thinning on the water depth profiles, obtains geographic entity characteristic points through slope, water depth and distance screening, and traverses all rows and columns of water depth profiles to obtain a single seabed geographic entity boundary. And then, constructing a cost value model, and realizing the segmentation of the composite geographic entity by adopting an optimal path method to finish the automatic identification of the composite seabed entity boundary.
The invention is realized by the following technical scheme:
step (1) data processing and modeling: carrying out data filtering, sound velocity correction, tide level correction and sounding abnormal value elimination on the original multi-beam water depth data obtained by surveying, and constructing a water depth grid model
(ii) a Wherein,Modelanddepthrespectively representing the mesh model and the water depth,X i,j ,Y i,j ,Z i,j as a depth grid modeliGo, firstjThe coordinates of the plane position of the column and the depth value of the position,M、Nthe total number of the rows and the columns of the water depth grid model,i、j、MandNis a natural number; turning to the step (2);
intercepting the area in the step (2): determining geographic entity area rectangular range
WhereinAreaandfeaturerespectively representing a rectangular area and a geographical physical area,Area feature is a rectangular range of the geographic physical region,Xs、Ysplanar position coordinates for a rectangular range of geographic entity areas,sa natural number with a value of 1~4; rectangular range based on geographic entity areaArea feature To depth of water grid model
Intercepting and outputting according to the range to obtain the required geographic entity area grid model
WhereinModelandfeaturerespectively representing a mesh model and a geographical physical area,M s 、N s the total number of rows and columns of the grid model of the geographic entity region,i、j、M s andN s is a natural number.
And (3) model conversion: will be provided with
Converting into set of water depth matrix points of geographic entity region
Wherein, in the process,Z i,j is the first of the water depth matrix of the geographic entity regioniLine and firstjThe value of the column water depth is,i、j、M s andN s is a natural number; turning to the step (4);
step (4), extracting water deep section points: from the set of water depth matrix points of the geographic entity region
In turn select the firstmLine data thereinmIs 1 toM s Is formed based onmGeographic entity water depth profile data set of rows
WhereinX m,n ,Y m,n ,Z m,n respectively, the first in the data setnThe plane position coordinates and the water depth value of the points,mandnfor the number of rows of the data set and the number of points in the row,N s is the total number of the data concentration points,n、N s is a natural number; then from the set of water depth matrix points of the geographic entity region
In turn select the firstnLine data whereinnIs 1 toN s Is formed based onnGeographic entity water depth profile data set of columns
WhereinX n,m ,Y n,m ,Z n,m respectively, the first in the data setmThe plane position coordinates and the water depth values of the points;nandmfor the number of columns of the data set and the number of points in the column,M s is the total number of the data concentration points,m、M s is a natural number.
And (5) differential simplification: based onmGeographic entity water depth profile data set of rows
Calculating it by using difference algorithm to obtain the basismGeographic entity slope profile dataset of rows
Wherein, in the process,S m,n is based onmGeographic entity gradient profile data set of rowsnThe value of the slope of the point is,m、nandN s is a natural number; continue to adopt differential algorithm pairmGeographic entity slope profile dataset of rows
Performing calculation to obtain the basemGeographic entity secondary navigation profile dataset of rows
WhereinSS m,n is based onmGeographic entity secondary navigation profile data set of rowsnA second derivative value of the point; search location is based onmGeographic entity secondary navigation profile dataset of rows
The plane position coordinates and the water depth value of each extreme point are obtained based onmGeographic entity extreme point water depth profile data set of rows
Wherein, in the process,extreme_pointthe value of the extreme point is represented,N ep to search for the number of located extreme points,m、nandN ep is a natural number; turning to the step (6);
step (6), thinning the section: based on D-P algorithmmGeographic entity extreme point water depth profile data set of rows
Performing thinning, and setting the threshold of the D-P algorithm to beTIs obtained based onmGeographic entity water depth profile data set after row D-P thinning
Wherein, in the process,DPrepresenting the thinning out of the D-P algorithm,N dp after thinning for D-P algorithmThe number of the water depth points in the geographic entity water depth profile data set,m、nandN dp is a natural number.
And (7) gradient screening: based on differential algorithmmGeographic entity water depth profile data set after row D-P thinning
Performing calculation to obtain the basemGeographic entity slope profile dataset after D-P thinning of rows
WhereinDP_sloperepresenting the geographical entity grade profile after D-P thinning,S m,n is based onmGeographic entity grade profile data set after D-P thinning of rowsnThe value of the slope of the point is,m、nandN dp is a natural number; adjacent grade difference algorithm is adopted to solveS m,n The absolute value of the difference is sorted from large to small and extracted beforekThe plane position coordinate and the water depth value corresponding to the value are obtained based onmData set of points having large absolute difference values of rows
Whereinkis the total number of the data concentration points,MVDrepresents the point where the absolute value of the difference is large,m、nandkturning to the step (8) if the number is a natural number;
step (8) water depth screening: search is based onmGeographic entity water depth profile data set of rows
Medium minimum water depth valuez min Setting the water depth threshold value asGWherein G =z min +200, screening based onmData set of points having large absolute difference values of rows
Depth of medium water is less thanGThe point (c) of (a) is,is obtained based onmRunning water depth threshold screening dataset
WhereinGrepresenting a threshold value for the depth of water,k_gis the total number of the data concentration points,m、nandk_gis a natural number; turning to the step (9);
step (9) distance screening: search is based onmGeographic entity water depth profile data set of rows
Coordinates corresponding to the medium maximum water depth value (x max ,y max ) Is obtained based onmRunning water depth threshold screening dataset
Distance between points (A), (B), (C)x max ,y max ) To obtain a distance value of
WhereinL m,n as a data set
To middlenDistance of points (x max ,y max ) A distance value of (d); searching
InL m,n The plane position coordinates corresponding to the minimum two points are obtained based onmGeographic entity feature point data set of rows
WhereinFPthe characteristic points of the representative geographic entity,m、nis a natural number.
Step (10), extracting geographic entity feature points: sequentially traversing water depth matrix point set of geographic entity region
To other (1) ofmLine data and the secondnLine data whereinmIs 1 toM s The number of the first and second images,nis 1 toN s Repeating the steps (4) - (10) to obtain geographic entity feature point data sets of all rows
And geographic entity feature point data set for all columns
Wherein
and
respectively representing the geographic entity feature points of all the rows and the geographic entity feature points of all the columns; turning to step (11);
step (11) judging the type of the geographic entity: judging geographical entity area grid model
Determining feature points on two sides of a bealock position of the geographic entity as a starting point and an end point if the type of the geographic entity in the system is a composite type; turning to step (12);
and (12) outputting the single type geographic entity boundary: feature point data set of geographic entities of all rows
And geographic entity feature point data set for all columns
Summing to obtain a single type geographic entity boundary
WhereinBDandsingle_featurerespectively representing a border and a single type of geographic entity,x k 、y k planar position coordinates of points formed by the boundaries of the unitary geographic entity,k、M s andN s is a natural number.
Step (13) topographic factor calculation: formula (1):
(ii) a Wherein,Lthe distance value of two water depth points is obtained; computing a geo-physical area mesh model using equation (1)
Reclassifying the gradient values to obtain a reclassified grid model of the gradient values of the geographic entity region
WhereinModel、 featureandre_sloperespectively represent a grid model, a geographical entity area and a slope value reclassification,rS i,j for the slope value grid model of the geographic entity regioniGo, firstjReclassifying the gradient values of the columns; extracting geographic entity region grid model
Lowest point water depth value ofZ max (ii) a Formula (2):
whereinZ max the water depth value of the shallowest point is taken as the water depth value of the shallowest point; calculation using equation (2)
Reclassifying the fluctuation values to obtain a reclassified grid model of the fluctuation values of the geographic entity region
WhereinModel、featureandre_fluctuaterespectively representing a grid model, a geographical entity area and a heaviness value reclassification,rF i,j for the relief value grid model of the geographical entity regioniLine and firstjRe-categorizing the columns by a relief value; turning to step (14);
step (14), cost value model construction: reclassifying grid model for gradient value of geographic entity region by using formula (3)
Reclassifying grid model with geographic entity region fluctuation values
Carrying out superposition calculation to obtain a grid model of the cost value of the geographic entity region
WhereinModel、featureandsumrespectively representing a mesh model, a geographical physical area and a cost value,
cost value mesh model for geographic entity regioniLine and firstjA cost value for the column; formula (3):
。
step (15) calculating a cost distance weight function: respectively extracting characteristic points on two sides of the composite geographic entity bealock position as a starting point and an end point, and based on a geographic entity area cost value grid model
Calculating a path distance mesh model between two points
And orientation mesh model
Wherein, in the process,Model、featurerespectively representing a mesh model and a geographical physical area,path、directionrespectively representing the path distance and direction,L i,j mesh model for path distance between two pointsiLine and firstjThe path distance value of the column is determined,D i,j as a directional grid model between two pointsiLine and firstjA column direction index value; turning to step (16);
step (16) shortest path calculation: path distance mesh model based on determined start and end positions
And orientation mesh model
Generating shortest path point set by utilizing Dijkstra algorithm
Whereinpathrepresenting the shortest path,kandN p are all natural numbers, and are all natural numbers,x k 、y k is the plane position coordinate of the point formed by the composite geographic entity dividing line,N p the sum of the shortest path point sets; completing the composite geographic entity segmentation; turning to step (17);
and (17) outputting a composite geographic entity boundary: boundary of unitary geographic entity
And shortest path point set
Summing to obtain composite geographic entity boundary
WhereinBDandmixed_featurerespectively representing a border and a composite geographic entity,x k 、y k is the plane position coordinate of the point formed by the composite geographic entity boundary,k、N p 、M s andN s is a natural number.
The invention has the beneficial effects that:
the invention provides and realizes a seabed geographic entity boundary automatic identification method based on a D-P algorithm and an optimal path. The method overcomes the defect that the prior art is difficult to quantitatively determine the submarine geographic entity boundary, so that the definition of the entity range lacks basis and has high randomness. The method can be applied to the technical links of automatically generating any two-dimensional water depth matrix by a grid model, then performing section analysis, automatically identifying topographic section characteristics, drawing a comprehensive section, generating key boundary points in ocean demarcation, automatically identifying the type of the submarine landform and the like. The invention can play an important role in the fields of seabed geographic entity demarcation, ocean mapping, deep sea mining, ocean engineering construction and the like.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic diagram of a geographic entity region grid model in embodiment 1 of the present invention.
Fig. 3 is a schematic water depth profile of the geographic entity in embodiment 1 of the present invention.
Fig. 4 is a schematic cross-sectional view of a geographical entity gradient in embodiment 1 of the present invention.
Fig. 5 is a schematic cross-sectional view of a geographic entity secondary navigation in embodiment 1 of the present invention.
Fig. 6 is a schematic water depth profile of the geographic entity extreme point in embodiment 1 of the present invention.
FIG. 7 is a water depth profile of the geographical entity after D-P thinning in example 1 of the present invention.
Fig. 8 is a schematic diagram of geographic entity feature points in embodiment 1 of the present invention.
Fig. 9 shows the result of single-type automatic recognition of the boundary of a seashore mountain in embodiment 1 of the present invention.
Fig. 10 is a schematic diagram of a geo-entity area mesh model in embodiment 2 of the present invention.
Fig. 11 is a result of automatic recognition of a composite type seashore boundary in embodiment 2 of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples.
Example 1 single type seashore boundary automatic identification;
referring to the scheme shown in figure 1:
step (1), data processing and modeling: carrying out data filtering, sound velocity correction, tide level correction and elimination processing on the original multi-beam water depth data obtained by surveying to construct a water depth grid model
(ii) a Wherein,Modelanddepthrespectively representing the mesh model and the water depth,
as a depth grid modeliLine and firstjThe plane position coordinates of the columns and the water depth value of the position, 960 and 1050 are the total number of rows and columns of the water depth grid model,i、jis a natural number; turning to the step (2);
intercepting an area: determining a rectangular range of a geographic entity region
WhereinAreaandFeaturerespectively representing a rectangular area and a geographical physical area,Area Feature is a rectangular range of the geographic physical region,
is the plane position coordinate of the geographic entity region rectangular range,sa natural number with a value of 1~4; rectangular range based on geographic entity areaArea Feature To depth of water grid model
Intercepting and outputting according to the range to obtain the required geographic entity region grid model as shown in figure 2
WhereinModelandFeaturerepresenting the mesh model and the geo-physical area, respectively, 244,330 is the total number of rows and columns of the geo-physical area mesh model,i、jis a natural number; turning to the step (3);
and (3) model conversion: will be provided with
Converting into set of water depth matrix points of geographic entity region
，
Is the first of the water depth matrix of the geographic entity regioniLine and firstjThe value of the column water depth is,i、jis a natural number; turning to the step (4);
step (4), extracting water deep section points: from the set of water depth matrix points of the geographic entity region
In turn select the firstm（mNatural number of 1 to 244) of data, the construction is based onmGeographic entity water depth profile data set of rows
Whereinx m,n 、y m,n 、z m,n are respectively based onmGeographic entity depth profile data set of rowsnThe plane position coordinates and the water depth value of the points,mandnis based onmThe number of rows and the number of midpoints of the rows of the geographic entity water depth profile data set of the rows is the total number of concentration points of the water depth profile data,nis a natural number; then from the water depth matrix point of the geographical entity areaCollection
In turn select the firstnColumn (A)nNatural numbers from 1 to 330) based onnGeographic entity water depth profile data set of columns
Whereinx n,m 、y n,m 、z n,m are respectively based onnGeographic entity depth profile data set of columnsmThe plane position coordinates and the water depth values of the points;nandmis based onnThe number of columns of the column's geodetic water depth profile data set and the number of points in the column, 244 is the total number of points in the water depth profile data set,mis a natural number; turning to the step (5);
and (5) differential simplification: as shown in figure 3 with a 150 row-based geoentity water depth profile data set
For example, the difference algorithm is used to calculate the gradient profile data, and a 150-line geographic entity gradient profile data set is obtained as shown in FIG. 4
Wherein
is a 150-line based geographic entity grade Profile data setnThe value of the slope of the point is,m、nandNsis a natural number; continuously adopting differential algorithm to carry out 150-row-based geographic entity gradient profile data set
Calculating to obtain a geographic entity secondary navigation profile data set based on 150 rows as shown in figure 5
Wherein
is the second derivative profile data set of the geographic entity based on 150 rowsnA second derivative value of the point; search positioning 150-row-based geographic entity secondary navigation profile dataset
The position coordinates and the water depth values of the plane of each extreme point are obtained as shown in FIG. 6, and a 150-row-based geographic entity extreme point water depth profile data set is obtained
Wherein
representing extreme points, 78 the number of search located extreme points, nis a natural number; turning to the step (6);
step (6), thinning the section: D-P algorithm is adopted to carry out 150-row-based geographic entity extreme point water depth profile data set
Performing thinning, setting the threshold of the D-P algorithm to be 18, and obtaining a geographic entity water depth profile data set based on the D-P thinned 150 rows as shown in figure 7
WhereinDPrepresenting the D-P algorithm rarefaction, 14 is the number of water depth points in the geographic entity water depth profile data set after the D-P algorithm rarefaction,nis a natural number; turning to the step (7);
and (7) gradient screening: adopting differential algorithm to carry out D-P (dimension-to-dimension) rarefied geographic entity water depth profile data set based on 150 rows
Calculating to obtain a geographical entity gradient profile data set after D-P thinning based on 150 rows
Wherein
representing the geographical entity grade profile after D-P thinning,
set forth for 150 rows based geographic entity grade Profile data after D-P thinningnThe value of the slope of the point is,nis a natural number; adjacent grade difference algorithm is adopted to solve
Sorting the absolute values of the differences from large to small, extracting plane position coordinates and water depth values corresponding to the first 7 values, and obtaining a data set based on 150 rows of points with large absolute values of the differences
Where 7 is the total number of data concentration points,MVDrepresents a point where the absolute value of the difference is large,m、nandkturning to the step (8) if the number is a natural number;
step (8) water depth screening: search of 150-row-based geographic entity water depth profile dataset
The minimum water depth value is-3822.83 m, the water depth threshold value is-3622.83 m, and a data set based on 150 rows of points with large absolute difference values is screened
The point that the value of the medium water depth is less than-3622.83 m obtains a water depth threshold value screening data set based on 150 rows
Where 4 is the total number of data concentration points,nis a natural number; turning to the step (9);
step (9)) Distance screening: searching a 150-row-based geographic entity water depth profile dataset
The coordinate (116.42,16.3735) corresponding to the medium maximum water depth value is used for obtaining a water depth threshold value screening data set based on 150 rows
The distance value of the distance (116.42,16.3735) of each point in the image is obtained
Wherein
as a data set
To middlenA distance value of the point distance (116.42,16.3735); searching
In
The coordinates of the plane positions corresponding to the minimum two points are obtained as shown in FIG. 8, and a 150-row-based geographic entity feature point data set is obtained
WhereinFPthe characteristic points of the representative geographic entity, nis a natural number; turning to the step (10);
step (10), extracting geographic entity feature points: sequentially traversing water depth matrix point set of geographic entity region
To other (1) ofm（mNatural number of 1 to 244) and the fourth line datanColumn (A)nNatural number of 1 to 330), and repeating the steps (4) to (9) to obtain geographic realities of all linesVolume feature point data set
And geographic entity feature point data set for all columns
Wherein
and
respectively representing the geographic entity feature points of all rows and the geographic entity feature points of all columns,m、nis a natural number; turning to step (11);
step (11) judging the type of the geographic entity: judging geographical entity area grid model
The type of the geographic entity in (1) is a single type of seashore; turning to step (12);
and (12) outputting the single type geographic entity boundary: set geographic entity feature point data of all rows
And geographic entity feature point data set for all columns
Summing to obtain a single type geographic entity boundary as shown in FIG. 9
WhereinBDandsingle_featurerespectively representing a border and a single type of geographic entity,x k 、y k planar position coordinates of points formed by the boundaries of the unitary geographic entity,k、M s andN s is a natural number; and completing the delineation of the single type of seashore boundary.
Embodiment 2 automatic recognition of composite sea and mountain boundaries;
referring to the scheme shown in figure 1:
step (1), data processing and modeling: carrying out data filtering, sound velocity correction, tide level correction and sounding abnormal value elimination on the original multi-beam water depth data obtained by surveying, and constructing a water depth grid model
(ii) a Wherein,Modelanddepthrespectively representing the mesh model and the water depth,
as a depth grid modeliGo, firstjThe plane position coordinates of the column and the water depth value of the position, 960 and 1050 are the total number of the rows and the columns of the water depth grid model,i、jis a natural number; turning to the step (2);
intercepting the area in the step (2): determining geographic entity area rectangular range
WhereinAreaandFeaturerespectively representing a rectangular area and a geographical physical area,Area Feature is a rectangular range of the geographic entity area,X s 、Y s planar position coordinates for a rectangular range of geographic entity areas,sa natural number with a value of 1~4; rectangular range based on geographic entity areaArea Feature To depth of water grid model
Intercepting and outputting according to range to obtain the required geographic entity region grid model as shown in figure 10
WhereinModelandFeaturerespectively representing a grid model and a geographic entity region, 598, 382 are the total number of rows and columns of the grid model of the geographic entity region,i、jis a natural number; go to the step（3）；
And (3) model conversion: will be provided with
Set of water depth matrix points converted into geographic entity area
，
Is the first of the water depth matrix of the geographic entity regioniLine and firstjThe value of the column water depth is,i、jis a natural number; turning to the step (4);
step (4), extracting water deep section points: from the set of water depth matrix points of the geographic entity region
In turn select the firstm（mNatural number of 1 to 598) of data, the construction is based onmGeographic entity water depth profile data set of rows
WhereinX m,n ,Y m,n ,Z m,n are respectively based onmGeographic entity depth profile data set of rowsnThe plane position coordinates and the water depth value of the points,mandnis based onmThe number of rows of the row's geo-physical water depth profile data set and the number of points in the row, 382 is the total number of points in the water depth profile data set,nis a natural number; then from the set of water depth matrix points of the geographic entity region
In turn select the firstnColumn (A)nNatural number of 1 to 382) data based onnGeographic entity water depth profile data set of columns
Whereinx n,m 、y n,m 、z n,m are respectively based onnGeographic entity depth profile data set of columnsmThe plane position coordinates and the water depth values of the points;nandmis based onnThe number of columns of the column's geo-physical water depth profile data set and the number of midpoints in the column, 598 is the total number of concentration points for the water depth profile data,mis a natural number; turning to the step (5);
and (5) differential simplification: with a 150-row-based geographic entity water depth profile data set
For example, a differential algorithm is used to calculate the gradient profile data set of the geographic entity based on 150 rows
Wherein
is a 150-line based geographic entity grade Profile data setnThe value of the slope of the point is,m、nis a natural number; continuing to employ the differential algorithm to the 150-line-based geographic entity slope profile dataset
Calculating to obtain a geographic entity secondary navigation profile data set based on 150 rows
Wherein
is the second derivative profile data set of the geographic entity based on 150 rowsnA second derivative value of the point; search positioning 150-row-based geographic entity secondary navigation profile dataset
Obtaining a 150-row-based geographic entity extreme point water depth profile data set based on the plane position coordinates and the water depth values of each extreme point
Wherein
representing extreme points, 94 is the number of search located extreme points, nis a natural number; turning to the step (6);
step (6), thinning the section: D-P algorithm is adopted to carry out water depth profile data set on extreme point of geographic entity based on 150 rows
Performing thinning, setting the threshold of the D-P algorithm to be 18, and obtaining a geographic entity water depth profile data set after D-P thinning based on 150 rows
WhereinDPrepresenting the D-P algorithm rarefaction, 17 is the number of water depth points in the geographic entity water depth profile data set after the D-P algorithm rarefaction,nis a natural number;
and (7) gradient screening: adopting differential algorithm to carry out D-P (dimension-to-dimension) rarefied geographic entity water depth profile data set based on 150 rows
Calculating to obtain a geographical entity gradient profile data set after D-P thinning based on 150 rows
Wherein
representing the geographical entity grade profile after D-P thinning,
set forth first for 150 rows based D-P diluted geographic entity slope profile datanThe value of the slope of the point is,nis a natural number; adjacent grade difference algorithm is adopted to solve
Sorting the absolute values of the differences from large to small, extracting plane position coordinates and water depth values corresponding to the first 7 values, and obtaining a data set based on 150 rows of points with large absolute values of the differences
Where 7 is the total number of data concentration points,MVDrepresents a point where the absolute value of the difference is large,m、nandkturning to the step (8) if the number is a natural number;
step (8) water depth screening: search of 150-row-based geographic entity water depth profile dataset
The minimum water depth value is-4332 m, the water depth threshold value is set to be-4132 m, and a data set based on 150 rows of points with large absolute difference values is screened
Obtaining a water depth threshold value screening data set based on 150 rows at the point that the middle water depth value is less than-4132 m
Where 4 is the total number of data concentration points,nis a natural number; turning to the step (9);
step (9) distance screening: distance screening: searching a 150-row-based geographic entity water depth profile dataset
Obtaining a water depth threshold value screening data set based on 150 rows by using a coordinate (115.2511,13.5817) corresponding to the medium-maximum water depth value
The distance value of the distance (115.2511,13.5817) of each point in the image is obtained
Wherein
as a data set
The distance value of the nth point (115.2511,13.5817); searching
In
Obtaining a geographic entity feature point data set based on 150 rows by using the plane position coordinates corresponding to the minimum two points
WhereinFPrepresenting the characteristic points of the geographic entity, nis a natural number; turning to the step (10);
step (10) of extracting geographic entity feature points: sequentially traversing water depth matrix point sets of geographic entity regions
To other (1) ofmLine (A)mNatural number of 1 to 598) andncolumn (A)nNatural number of 1 to 382), and repeating the steps (4) to (9) to obtain geographic entity feature point data sets of all rows
And geographic entity feature point data set for all columns
Wherein
and
respectively representing the geographic entity feature points of all rows and the geographic entity feature points of all columns,m、nis a natural number; turning to step (11);
step (11) judging the type of the geographic entity: judging geographical entity area grid model
The type of the geographic entity in the system is a composite type, and characteristic points on two sides of a bealock position of the geographic entity are determined as a starting point and an end point; turning to step (12);
and (12) outputting the single type geographic entity boundary: feature point data set of geographic entities of all rows
And geographic entity feature point data set for all columns
Summing to obtain a single type geographic entity boundary
Wherein, in the process,BDandsingle_featurerespectively representing a border and a single type of geographic entity,
planar position coordinates of points formed by the boundaries of the unitary geographic entity,kis a natural number; turning to step (13);
step (13) topographic factor calculation: computing a geo-physical area mesh model using equation (1)
Reclassifying the gradient values to obtain a reclassified grid model of the gradient values of the geographic entity region
Wherein,Model、 Featureand
respectively representing a grid model and a geographical entity regionThe domain and the gradient value are heavily classified,
for the geography entity area gradient grid modeliLine and firstjReclassifying the gradient values of the columns; extracting geographic entity region grid model
The water depth value of the shallowest point in (1) is-304 m, and is calculated by using the formula (2)
Reclassifying the fluctuation values to obtain a reclassified grid model of the fluctuation values of the geographic entity region
Wherein, in the process,Model、featureandre_fluctuaterespectively representing a grid model, a geographical entity area and a heaviness value reclassification,
for the relief grid model of the geographic entity regioniLine and firstjClassifying the fluctuation values by column weight; formula (1):
(ii) a Wherein,Lthe distance value of two water depth points is obtained; formula (2):
(ii) a Turning to step (14);
step (14), cost value model construction: reclassifying grid model for gradient value of geographic entity region by using formula (3)
Reclassifying grid model with geographic entity region fluctuation values
Performing superposition calculation to obtain a geographic entity areaCost value mesh model
WhereinModel、featureandsumrespectively representing a mesh model, a geo-physical region and a cost value,
cost value mesh model for geographic entity regioniLine and firstjA cost value for the column; formula (3):
(ii) a Turning to step (15);
and (15) calculating a cost distance weight function: respectively extracting characteristic points on two sides of the composite geographic entity bealock position as a starting point and an end point, and based on a geographic entity area cost value grid model
Calculating a path distance mesh model between two points
And orientation mesh model
WhereinModel、featurerespectively representing a mesh model and a geographical physical area,path、directionrespectively representing the path distance and direction,L i,j mesh model number for path distance between two pointsiLine and firstjThe path distance value of the column is,D i,j as a directional grid model between two pointsiLine and firstjA column direction index value; turning to step (16);
step (16) shortest path calculation: distance mesh model based on determined start and end positions, path
And orientation mesh model
Generating shortest path point set by utilizing Dijkstra algorithm
Whereinpathrepresenting the shortest path to the mobile station,kandN p are all natural numbers, and are all natural numbers,x k 、y k the plane position coordinates of points formed by composite geographic entity partition lines, and 33 is the total number of the shortest path point sets; completing the composite geographic entity segmentation; turning to step (17);
and (17) outputting a composite geographic entity boundary: bounding a single type of geographic entity
And shortest path point set
Summing to obtain composite geographic entity boundary as shown in FIG. 11
WhereinBDandmixed_featurerespectively representing a border and a composite geographic entity,x k 、y k is the plane position coordinate of the point formed by the composite geographic entity boundary,kis a natural number.
The embodiments in the above description can be further combined or replaced, and the embodiments are only described as preferred examples of the present invention, and do not limit the concept and scope of the present invention, and various changes and modifications made to the technical solution of the present invention by those skilled in the art without departing from the design concept of the present invention belong to the protection scope of the present invention. The scope of the invention is given by the appended claims and any equivalents thereof.
Claims (8)
1. The seabed geographic entity boundary automatic identification method based on the D-P algorithm and the optimal path is characterized by comprising the following steps: obtaining a regional grid, obtaining a water depth profile, thinning the water depth profile, screening entity characteristic points, obtaining a single entity boundary, preparing cost data and obtaining a composite entity boundary; firstly, obtaining regional grids, wherein the steps comprise: data processing and modeling, and region interception; secondly, acquiring a water depth profile, wherein the steps comprise: model conversion, water depth section point extraction; then water depth profile thinning is carried out, and the steps comprise: the difference is simplified, and the section is thinned; subsequently, entity feature point screening is carried out, and the method comprises the following steps: gradient screening, water depth screening and distance screening; then, acquiring a monomorphic entity boundary, comprising the following steps of: extracting geographic entity feature points, judging the type of a geographic entity, and outputting a single type geographic entity boundary; cost data preparation is then performed, the steps comprising: calculating terrain factors and constructing a cost value model; and finally, acquiring a composite entity boundary, wherein the steps comprise: calculating a cost distance weight function, solving a shortest path, and outputting a composite geographic entity boundary.
2. The method of claim 1, wherein said regional grid acquisition comprises the steps of:
step (1), data processing and modeling: carrying out data filtering, sound velocity correction, tide level correction and sounding abnormal value elimination on the original multi-beam water depth data obtained by surveying, and constructing a water depth grid model
(ii) a Wherein,Modelanddepthrespectively representing the mesh model and the water depth,X i,j ,Y i,j ,Z i,j as a depth grid modeliGo, firstjThe coordinates of the plane position of the column and the depth value of the position,M、Nthe total number of rows and columns of the water depth grid model,i、j、MandNis a natural number; turning to the step (2);
intercepting the area in the step (2): determining geographic entity area rectangular range
WhereinAreaandfeaturerespectively representing a rectangular area and a geographical physical area,Area feature is a rectangular range of the geographic physical region,X s 、Y s planar position coordinates for a rectangular range of geographic entity areas,sa natural number with a value of 1~4; rectangular range based on geographic entity areaArea feature To depth of water grid model
Intercepting and outputting according to the range to obtain the required geographic entity area grid model
WhereinModelandfeaturerespectively representing a mesh model and a geographical physical area,M s 、N s the total number of rows and columns of the grid model of the geographic entity region,i、j、M s andN s is a natural number.
3. The method of claim 2, wherein the water depth profile acquisition comprises the steps of:
and (3) model conversion: will be provided with
Converting into set of water depth matrix points of geographic entity region
WhereinZ i,j is the first of the water depth matrix of the geographic entity regioniGo, firstjThe value of the column water depth is,i、j、M s andN s is a natural number; turning to the step (4);
step (4), extracting water deep section points: from a geographical entityRegional water depth matrix point set
In turn select the firstmLine data thereinmIs 1 toM s Is formed based onmGeographic entity water depth profile data set of rows
WhereinX m,n ,Y m,n ,Z m,n respectively, the first in the data setnThe plane position coordinates and the water depth value of the points,mandnfor the number of rows in the data set and the number of points in the rows,N s is the total number of the data concentration points,n、N s is a natural number; then from the set of water depth matrix points of the geographic entity region
In turn select the firstnColumn data, whereinnIs 1 toN s Is formed based onnGeographic entity water depth profile data set of columns
WhereinX n,m ,Y n,m ,Z n,m respectively, the first in the data setmThe plane position coordinates and the water depth values of the points;nandmfor the number of columns of the data set and the number of points in the column,M s is the total number of the data concentration points,m、M s is a natural number.
4. The method of claim 3, wherein the water depth profile thinning comprises the steps of:
and (5) differential simplification: based onmGeographic entity water depth profile data set of rows
Calculating the difference by adopting a difference algorithm to obtain the basismGeographic entity slope profile dataset of rows
WhereinS m,n is based onmGeographic entity gradient profile data set of rowsnThe value of the slope of the point is,m、nandN s is a natural number; continue to adopt differential algorithm pairmGeographic entity slope profile dataset of rows
Performing calculation to obtain the basemGeographic entity secondary navigation profile dataset of rows
WhereinSS m,n is based onmGeographic entity secondary navigation profile data set of rowsnA second derivative value of the point; search positioning is based onmGeographic entity secondary navigation profile dataset of rows
The plane position coordinates and the water depth value of each extreme point are obtained based onmGeographic entity extreme point water depth profile data set of rows
Whereinextreme_pointthe value of the extreme point is represented by,N ep to search for the number of located extreme points,m、nandN ep is a natural number; turning to the step (6);
step (6), thinning the section: based on D-P algorithmmGeographic entity extreme point water depth profile data set of rows
Performing thinning, and setting the threshold of the D-P algorithm to beTIs obtained based onmGeographic entity water depth profile data set after row D-P thinning
WhereinDPrepresenting the thinning out of the D-P algorithm,N dp the number of the water depth points in the geographic entity water depth profile data set after the D-P algorithm is diluted,m、nandN dp is a natural number.
5. The method of claim 4, wherein said entity feature point screening comprises the steps of:
and (7) gradient screening: based on the use of differential algorithmmGeographic entity water depth profile data set after row D-P thinning
Performing calculation to obtain the basemGeographic entity slope profile dataset after D-P thinning of rows
WhereinDP_sloperepresenting the geographical entity grade profile after D-P thinning,S m,n is based onmGeographic entity grade profile data set after D-P thinning of rowsnThe value of the slope of the point is,m、nandN dp is a natural number; adjacent grade difference algorithm is adopted to solveS m,n Sorting the absolute values of the differences from large to small, and extracting the absolute values beforekThe plane position coordinate and the water depth value corresponding to the value are obtained based onmData set of points having large absolute difference values of rows
Whereinkis the total number of the data concentration points,MVDrepresents a point where the absolute value of the difference is large,m、nandkturning to the step (8) if the number is a natural number;
step (8) water depth screening: search is based onmGeographic entity water depth profile data set of rows
Medium minimum water depth valuez min Setting the water depth threshold value asGWherein G =z min +200, screening based onmData set of points having large absolute difference values of rows
Depth of medium water is less thanGIs obtained based onmRunning water depth threshold screening dataset
WhereinGrepresenting a threshold value for the water depth,k_gis the total number of the data concentration points,m、nandk_gis a natural number; turning to the step (9);
step (9) distance screening: search is based onmGeographic entity water depth profile data set of rows
Coordinates corresponding to the medium maximum water depth value (x max ,y max ) Is obtained based onmRunning water depth threshold screening dataset
Distance between points (A), (B), (C)x max ,y max ) Is obtained by obtaining
WhereinL m,n as a data set
To middlenDistance of points (x max ,y max ) A distance value of (d); searching
InL m,n The plane position coordinates corresponding to the minimum two points are obtained based onmGeographic entity feature point data set of rows
Wherein, in the process,FPthe characteristic points of the representative geographic entity,m、nis a natural number.
6. The method of claim 5, wherein said single type entity boundary acquisition comprises the steps of:
step (10), extracting geographic entity feature points: sequentially traversing water depth matrix point sets of geographic entity regions
To other (1) ofmLine data and the secondnLine data whereinmIs 1 toM s The number of the first and second images,nis 1 toN s Repeating the steps (4) - (10) to obtain geographic entity feature point data sets of all rows
And geographic entity feature point data set for all columns
Wherein
and
respectively representing geographic entity feature points of all rows and geographic entity features of all columnsPoint; turning to step (11);
step (11) judging the type of the geographic entity: judging geographic entity region grid model
Determining feature points on two sides of a bealock position of the geographic entity as a starting point and an end point if the type of the geographic entity in the system is a composite type; turning to step (12);
and (12) outputting the single type geographic entity boundary: feature point data set of geographic entities of all rows
And geographic entity feature point data set for all columns
Summing to obtain a single type geographic entity boundary
WhereinBDandsingle_featurerespectively representing a border and a single type of geographic entity,x k 、y k planar position coordinates of points formed by the boundaries of the unitary geographic entity,k、M s andN s is a natural number.
7. The method of claim 6, wherein said cost data preparation comprises the steps of:
step (13) topographic factor calculation: formula (1):
(ii) a Wherein,Lthe distance value of two water depth points is obtained; computing a geo-physical area mesh model using equation (1)
Reclassifying the gradient values to obtain a reclassified grid model of the gradient values of the geographic entity region
WhereinModel、 featureandre_sloperespectively represent a grid model, a geographical entity area and a slope value reclassification,rS i,j for the geographic entity region gradient value grid modeliGo, firstjReclassifying the gradient values of the columns; extracting geographic entity region grid model
Lowest point water depth value ofZ max (ii) a Formula (2):
wherein, in the process,Z max the water depth value of the shallowest point is taken as the water depth value of the shallowest point; calculation using equation (2)
Reclassifying the fluctuation values to obtain a reclassified grid model of the fluctuation values of the geographic entity region
WhereinModel、featureandre_fluctuaterespectively representing a grid model, a geographical entity area and a heaviness value reclassification,rF i,j for the relief value grid model of the geographical entity regioniLine and firstjRe-categorizing the columns by a relief value; turning to step (14);
step (14), cost value model construction: reclassifying grid model for gradient value of geographic entity region by using formula (3)
Reclassifying grid model with geographic entity region fluctuation values
Carrying out superposition calculation to obtain a grid model of the cost value of the geographic entity area
WhereinModel、featureandsumrespectively representing a mesh model, a geo-physical region and a cost value,
cost value for geo-entity area mesh model number oneiLine and firstjA cost value for the column; formula (3):
。
8. the method of claim 7, wherein the complex entity boundary acquisition comprises the steps of:
step (15) of calculating a cost distance weight function: respectively extracting characteristic points on two sides of the composite geographic entity bealock position as a starting point and an end point, and based on a geographic entity area cost value grid model
Calculating a path distance mesh model between two points
And orientation mesh model
WhereinModel、featurerespectively representing a mesh model and a geographical physical area,path、directionrespectively representing the path distance and direction,L i,j mesh model for path distance between two pointsiGo, firstjThe path distance value of the column is,D i,j is two pointsThe direction mesh model ofiLine and firstjA column direction index value; turning to step (16);
step (16) shortest path calculation: path distance mesh model based on determined start and end positions
And orientation mesh model
Generating shortest path point set by utilizing Dijkstra algorithm
Whereinpathrepresenting the shortest path to the mobile station,kandN p are all natural numbers, and are all natural numbers,x k 、y k is the plane position coordinate of the point formed by the composite geographic entity parting line,N p the sum of the shortest path point sets; completing the composite geographic entity segmentation; turning to step (17);
and (17) outputting a composite geographic entity boundary: bounding a single type of geographic entity
And shortest path point set
Summing to obtain composite geographic entity boundary
WhereinBDandmixed_featurerespectively representing a border and a composite geographic entity,x k 、y k is the plane position coordinate of the point formed by the composite geographic entity boundary,k、N p 、M s andN s is a natural number.
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