CN114820951A - Wavelet and filter-based composite seabed geographic entity progressive decomposition method - Google Patents

Wavelet and filter-based composite seabed geographic entity progressive decomposition method Download PDF

Info

Publication number
CN114820951A
CN114820951A CN202210732260.2A CN202210732260A CN114820951A CN 114820951 A CN114820951 A CN 114820951A CN 202210732260 A CN202210732260 A CN 202210732260A CN 114820951 A CN114820951 A CN 114820951A
Authority
CN
China
Prior art keywords
seabed
water depth
geographic entity
geographic
entity
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210732260.2A
Other languages
Chinese (zh)
Inventor
汪九尧
吴自银
赵荻能
周洁琼
朱超
王明伟
李家彪
李春峰
任建业
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Second Institute of Oceanography MNR
Original Assignee
Second Institute of Oceanography MNR
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Second Institute of Oceanography MNR filed Critical Second Institute of Oceanography MNR
Priority to CN202210732260.2A priority Critical patent/CN114820951A/en
Publication of CN114820951A publication Critical patent/CN114820951A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
    • G01C13/008—Surveying specially adapted to open water, e.g. sea, lake, river or canal measuring depth of open water

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Hydrology & Water Resources (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Graphics (AREA)
  • Geometry (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Processing Or Creating Images (AREA)

Abstract

The invention discloses a wavelet and filter-based composite seabed geographic entity step-by-step decomposition method, which comprises three steps of data preprocessing, composite seabed geographic entity separation and feature extraction. Firstly, preprocessing original multi-beam water depth data to complete landform modeling and construct a water depth model; secondly, the water depth model obtained by processing is subjected to progressive decomposition of the composite terrain by adopting discrete wavelet transform, the number of decomposition stages is determined, and the separation result is reconstructed, so that seabed geographic entities of different levels are separated; and finally, establishing an element information table of the submarine geographic entity through submarine geographic entity boundary determination and morphological characteristic parameter extraction. The method applies wavelet transformation to the decomposition of the submarine geographic entity, and effectively solves the problems that the complex submarine geographic entity is difficult to define, quantitatively analyze and the like. The invention has important practical application value in the fields of seabed geographic entity planning, oceanographic mapping, oceanographic engineering construction and the like.

Description

Wavelet and filter-based composite seabed geographic entity progressive decomposition method
Technical Field
The invention relates to the technical fields of seabed geographic entity planning, marine surveying and mapping, seabed topography (irregular surface or outline measurement), marine geology, marine mapping and image data processing, deep sea mining, marine engineering construction and the like.
Background
Oceans account for about 71% of the earth's area, and contain abundant resources on which humans rely for survival. Detailed and accurate global submarine topography information will play an important role in our cognition, development and utilization of the ocean. However, according to the statistics of "sea bottom 2030", the largest global sea bottom topographic data collection project, only about 21% of the global sea bottom has completed high-precision mapping of topographic features, and the cognition degree of the sea bottom topography is not as good as that of mars and lunar surface topography.
In the global seabed part of mastered high-precision topographic and geomorphic information, humans have since the last half century identified, classified and named measurable, well-defined geographical entities, i.e. seabed geographical entities, according to respective standards and rules. According to the statistics of the international submarine place name database, 4700 the identification and naming work of a plurality of submarine geographic entities is completed globally at present.
The subsea geographic entities may be divided into multiple levels according to size and master-slave relationship. However, the sub-sea geographic entity with smaller dimension is often superimposed on the sub-sea geographic entity with larger dimension, resulting in a composite sub-sea geographic entity with abnormally complex morphology and containing multiple levels of entities. The problems that the definition is difficult, the morphological parameters of the submarine geographic entity are difficult to extract and the like are solved step by step before the entity definition is carried out, and further the quantitative research of the geographic entity is carried out.
For the problem of multi-scale terrain decomposition, scholars at home and abroad develop some related researches. For example, Gutierrez and the like use continuous wavelet transform and combine a robust spline filter to decompose and classify the seabed sand waves of various scales, but do not perform morphological calculation on the decomposed sand waves of various scales. Meanwhile, the decomposition method of the small-scale terrain is difficult to be directly applied to the step-by-step decomposition of the seabed geographic entity with larger scale.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a wavelet and filter-based composite seabed geographic entity progressive decomposition method.
The purpose of the invention is realized by the following technical scheme:
a composite seabed geographic entity progressive decomposition method based on wavelets and filters comprises three steps of data preprocessing, composite seabed geographic entity separation and feature extraction: firstly, data preprocessing comprises multi-beam water depth data preprocessing and landform modeling, and a water depth model is constructed; secondly, separating the composite seabed geographic entities, namely performing step-by-step decomposition of the composite terrain, determination of the decomposition grade number and reconstruction of separation results by adopting discrete wavelet transform, so as to separate the seabed geographic entities with different grades; and finally, the characteristic extraction comprises the steps of determining the range of the seabed geographic entity, extracting morphological characteristic parameters and constructing an element information table.
The multi-beam water depth data preprocessing comprises the following steps: CUBE filtering, sound velocity correction and abnormal value elimination processing are carried out on the original multi-beam water depth data to obtain a processed multi-beam water depth data set
Figure 837246DEST_PATH_IMAGE001
Whereinx m Andy m are the plane position coordinate values of all the multi-beam sounding points respectively,
Figure 551124DEST_PATH_IMAGE002
the depth value of the multi-beam sounding point,
Figure 642446DEST_PATH_IMAGE003
the number of the depth measurement points is,
Figure 568814DEST_PATH_IMAGE004
and
Figure 179923DEST_PATH_IMAGE003
are all natural numbers.
The landform modeling comprises the following steps: multi-beam water depth data set based on processing
Figure 284277DEST_PATH_IMAGE001
Constructing and obtaining an original water depth model by adopting a spline function interpolation method
Figure 348048DEST_PATH_IMAGE005
Wherein, in the step (A),X s,l andY s,l respectively the original water depth model
Figure 78106DEST_PATH_IMAGE006
To (1) a
Figure 792990DEST_PATH_IMAGE007
Line and first
Figure 317513DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 603000DEST_PATH_IMAGE009
as an original water depth model
Figure 887482DEST_PATH_IMAGE006
The depth value of the location in (a),
Figure 942026DEST_PATH_IMAGE010
and
Figure 168608DEST_PATH_IMAGE011
respectively the total row number and the total column number of the original water depth model,
Figure 456239DEST_PATH_IMAGE007
、
Figure 262521DEST_PATH_IMAGE008
、
Figure 437150DEST_PATH_IMAGE010
and
Figure 569054DEST_PATH_IMAGE011
are all natural numbers.
The discrete wavelet transform is adopted to carry out the stepwise decomposition of the composite terrain: based on original water depth model
Figure 579867DEST_PATH_IMAGE006
Performing discrete wavelet transform, combining a Mallat algorithm with a filter to realize wavelet multi-scale analysis, decomposing the composite terrain into low-frequency terrain approximate components and high-frequency terrain detail components, decomposing the approximate components into lower-level terrain approximate components and terrain detail components each time, and keeping the total data unchanged; the decomposition algorithm is expressed as the formula:
Figure 455419DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 750134DEST_PATH_IMAGE013
in order to disperse the number of the sample data,
Figure 325644DEST_PATH_IMAGE014
and
Figure 73021DEST_PATH_IMAGE015
low-pass and high-pass filter coefficients for the decomposition process,
Figure 486684DEST_PATH_IMAGE016
and
Figure 386638DEST_PATH_IMAGE017
are respectively the first
Figure 125924DEST_PATH_IMAGE018
And (3) terrain approximate components and terrain detail components obtained by level wavelet decomposition.
Determining the decomposition series and reconstructing the separation result: based on decomposed terrain approximation components
Figure 95017DEST_PATH_IMAGE016
And topographic detail component
Figure 561640DEST_PATH_IMAGE017
Determining the number of decomposition steps based on the frequency analysis information
Figure 565368DEST_PATH_IMAGE019
And reconstructing the separation result, wherein the reconstruction formula is as follows:
Figure 209976DEST_PATH_IMAGE020
wherein, in the step (A),
Figure 666365DEST_PATH_IMAGE021
and
Figure 438143DEST_PATH_IMAGE022
are respectively as
Figure 30798DEST_PATH_IMAGE023
And
Figure 111887DEST_PATH_IMAGE024
the conjugate of (2) is to repeat the coefficients of each filter bank, and finally the separated water depth model is obtained
Figure 570419DEST_PATH_IMAGE025
And
Figure 129576DEST_PATH_IMAGE026
thereby separating different levels of sub-sea geographic entities.
Determining the range of the seabed geographic entity:
(a) water depth model based on separation
Figure 107896DEST_PATH_IMAGE025
According to the depth of water of the seabed geographic entity
Figure 579460DEST_PATH_IMAGE027
Determining the range of the seabed geographic entity
Figure 276021DEST_PATH_IMAGE028
Wherein, in the step (A),
Figure 373290DEST_PATH_IMAGE029
and
Figure 252122DEST_PATH_IMAGE030
respectively are the plane position coordinate values of the water depth point on the seabed geographic entity boundary,
Figure 143854DEST_PATH_IMAGE031
the total number of water depth points on the seabed geographic entity boundary; seabed-based geographic entity range
Figure 875181DEST_PATH_IMAGE032
For the original water depth model
Figure 822146DEST_PATH_IMAGE006
Range interception and output are carried out to obtain an intercepted water depth model
Figure 243900DEST_PATH_IMAGE033
Wherein, in the step (A),X s,l andY s,l respectively a water depth model after cutting
Figure 837693DEST_PATH_IMAGE034
To (1) a
Figure 977687DEST_PATH_IMAGE007
Line and first
Figure 698649DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 506068DEST_PATH_IMAGE035
for the water depth model after cutting
Figure 520030DEST_PATH_IMAGE034
The depth value of the position of the plane in question,
Figure 412899DEST_PATH_IMAGE036
for the water depth model after cutting
Figure 921241DEST_PATH_IMAGE034
The minimum water depth of the water in the water tank,
Figure 68320DEST_PATH_IMAGE037
for the water depth model after cutting
Figure 269494DEST_PATH_IMAGE034
The maximum water depth of the water to be treated,
Figure 649660DEST_PATH_IMAGE010
and
Figure 210960DEST_PATH_IMAGE011
respectively a water depth model after cutting
Figure 727392DEST_PATH_IMAGE034
The total number of rows and the total number of columns,
Figure 568309DEST_PATH_IMAGE007
、
Figure 920924DEST_PATH_IMAGE008
、
Figure 771068DEST_PATH_IMAGE010
and
Figure 142007DEST_PATH_IMAGE011
are all natural numbers;
(b) water depth model based on separation
Figure 403093DEST_PATH_IMAGE026
To find a model of the gradient
Figure 492271DEST_PATH_IMAGE038
Wherein, in the step (A),X s,l andY s,l respectively being a model of gradient
Figure 146107DEST_PATH_IMAGE039
To (1) a
Figure 122284DEST_PATH_IMAGE007
Line and first
Figure 570583DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 412637DEST_PATH_IMAGE040
is a slope model
Figure 385010DEST_PATH_IMAGE039
A slope value of the plane position; based on gradient model
Figure 464961DEST_PATH_IMAGE039
According to the gradient range of the seabed geographic entity
Figure 100473DEST_PATH_IMAGE041
Determining the range of the seabed geographic entity
Figure 164244DEST_PATH_IMAGE042
Wherein, in the step (A),
Figure 159882DEST_PATH_IMAGE043
and
Figure 343608DEST_PATH_IMAGE044
respectively the plane position coordinate values of the gradient points on the boundary of the seabed geographic entity,
Figure 399288DEST_PATH_IMAGE045
the total number of slope points on the seabed geographic entity boundary; seabed-based geographic entity range
Figure 950355DEST_PATH_IMAGE046
For the original water depth model
Figure 234837DEST_PATH_IMAGE006
Range interception and output are carried out to obtain an intercepted water depth model
Figure 289381DEST_PATH_IMAGE047
Wherein, in the step (A),X s,l andY s,l respectively a water depth model after interception
Figure 781542DEST_PATH_IMAGE048
To (1) a
Figure 803594DEST_PATH_IMAGE007
Go, first
Figure 875455DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 50084DEST_PATH_IMAGE035
for the water depth model after cutting
Figure 667142DEST_PATH_IMAGE048
The depth value of the position of the plane in question,
Figure 927222DEST_PATH_IMAGE049
for the water depth model after cutting
Figure 802774DEST_PATH_IMAGE048
The minimum slope of the slope,
Figure 831910DEST_PATH_IMAGE050
for the water depth model after cutting
Figure 118403DEST_PATH_IMAGE048
The maximum slope of the slope,
Figure 865779DEST_PATH_IMAGE010
and
Figure 545022DEST_PATH_IMAGE011
respectively a water depth model after cutting
Figure 179397DEST_PATH_IMAGE048
The total number of rows and the total number of columns,
Figure 653104DEST_PATH_IMAGE007
、
Figure 622197DEST_PATH_IMAGE008
、
Figure 573972DEST_PATH_IMAGE010
and
Figure 826968DEST_PATH_IMAGE011
are all natural numbers.
The morphological characteristic parameter extraction: water depth model based on intercepted water depth
Figure 205997DEST_PATH_IMAGE051
Extracting morphological characteristic parameters of the seabed geographic entity by using GIS software
Figure 662386DEST_PATH_IMAGE052
Wherein
Figure 417852DEST_PATH_IMAGE011
And
Figure 26819DEST_PATH_IMAGE053
respectively representing the length and width of the sub-sea geographic entity,
Figure 107908DEST_PATH_IMAGE054
and
Figure 51593DEST_PATH_IMAGE036
respectively representing the maximum and minimum water depths of the sub-sea geographic entity,
Figure 345171DEST_PATH_IMAGE055
the average grade representing the seafloor geographical entity, expressed as the degree of slope of the local surface slope, may be expressed as a simplified difference formula when performing the grade calculation:
Figure 307180DEST_PATH_IMAGE056
wherein the content of the first and second substances,
Figure 293590DEST_PATH_IMAGE057
is prepared fromxElevation or depth of water rate of change;
Figure 990151DEST_PATH_IMAGE058
is prepared fromyElevation or depth of water rate of change; morphological characteristic parameter based on the seabed geographic entity
Figure 838152DEST_PATH_IMAGE059
Determining a rank of the subsea geographic entity
Figure 405400DEST_PATH_IMAGE060
And type
Figure 93870DEST_PATH_IMAGE061
。
The element information table is constructed as follows: based on seabed geographic entity scope
Figure 261415DEST_PATH_IMAGE062
Morphological characteristic parameter of seabed geographic entity
Figure 896796DEST_PATH_IMAGE059
Grade of seabed geographic entity
Figure 849708DEST_PATH_IMAGE060
And type
Figure 177922DEST_PATH_IMAGE061
And constructing an element information table of the seabed geographic entity
Figure 803069DEST_PATH_IMAGE063
。
The invention has the beneficial effects that:
the invention provides a decomposition method for a composite seabed geographic entity based on actual measurement multi-beam water depth data, which adopts discrete wavelet transform to carry out stepwise decomposition of composite terrain, decomposes the composite seabed geographic entity into landform units of different levels, and solves the difficult problems of difficult definition, quantitative analysis and the like of the complex seabed geographic entity.
The invention has important practical application value in the aspects of seabed geographic entity planning, oceanographic mapping, seabed engineering construction and the like.
Drawings
Fig. 1 is a flow chart of a wavelet and filter based composite seafloor geographical entity progressive decomposition method of the present invention.
Fig. 2 is an original water depth model.
FIG. 3 is a diagram of the results of the separation of the first subsea geographic entity.
FIG. 4 is a diagram of secondary seafloor geographical entity separation results.
FIG. 5 is a water depth model of a seafloor geographical entity cut through the water depth range.
FIG. 6 is a water depth model of a seafloor geographical entity cut through a slope range.
Detailed Description
The invention is described in detail below with reference to examples and the accompanying drawings.
Example 1 concrete application of a typical land slope topography
As shown in fig. 1, the present example describes a wavelet and filter-based composite seabed geographic entity progressive decomposition method, which includes three steps of data preprocessing, composite seabed geographic entity separation and feature extraction.
Firstly, data preprocessing comprises multi-beam water depth data preprocessing and landform modeling, and a water depth model is constructed; secondly, separating the composite seabed geographic entities, namely performing step-by-step decomposition of the composite terrain, determination of the decomposition grade number and reconstruction of separation results by adopting discrete wavelet transform, so as to separate the seabed geographic entities with different grades; and finally, the characteristic extraction comprises the steps of determining the range of the seabed geographic entity, extracting morphological characteristic parameters and constructing an element information table.
The data preprocessing comprises the steps of multi-beam water depth data preprocessing and terrain and landform modeling, a water depth model is constructed, fig. 2 shows that an original water depth model containing 386 rows and 470 columns is obtained by utilizing a spline function interpolation method for modeling based on 258 ten thousand depth measuring point original multi-beam depth measuring data, and the specific steps comprise:
(i) preprocessing multi-beam water depth data:
CUBE filtering, sound velocity correction and abnormal value elimination processing are carried out on the original multi-beam water depth data to obtain a processed multi-beam water depth data set
Figure 773299DEST_PATH_IMAGE064
Wherein x is m And y m Respectively the plane position coordinate values of all the multi-beam sounding points,
Figure 315139DEST_PATH_IMAGE002
the depth value of the multi-beam sounding point.
(ii) Landform modeling:
multi-beam water depth data set based on processing
Figure 814253DEST_PATH_IMAGE064
Constructing and obtaining an original water depth model by adopting a spline function interpolation method
Figure 690811DEST_PATH_IMAGE065
WhereinX s,l AndY s,l respectively the original water depth model
Figure 667995DEST_PATH_IMAGE006
To (1) a
Figure 329920DEST_PATH_IMAGE007
Line and first
Figure 999936DEST_PATH_IMAGE008
The coordinate values of the plane positions of the rows,
Figure 865255DEST_PATH_IMAGE009
as an original water depth model
Figure 646129DEST_PATH_IMAGE006
The depth value of the position, the total row number of the original water depth model
Figure 428140DEST_PATH_IMAGE010
Is 386 rows and total column
Figure 541762DEST_PATH_IMAGE011
For column 470, the original water depth model is constructed as shown in fig. 2.
The separation of the composite seabed geographic entities comprises the steps of performing stepwise decomposition of the composite terrain, determining the decomposition grade number and reconstructing a separation result by adopting discrete wavelet transform, thereby separating the seabed geographic entities with different grades, and the separation results of the composite seabed geographic entities are shown in figures 3 and 4 and are respectively a first-grade seabed geographic entity: a land slope; second-level seabed geographic entity: the specific steps of the sea hills, the sea hills and the sea floor canyons include:
(i) and (3) performing stepwise decomposition of the composite terrain by adopting discrete wavelet transform:
based on original water depth model
Figure 409224DEST_PATH_IMAGE065
Discrete wavelet transform is carried out, and the discrete wavelet transform is combined with a filter through a Mallat algorithm to realize wavelet transformAnd (3) multi-scale analysis, namely decomposing the composite terrain into a low-frequency terrain approximate component and a high-frequency terrain detail component, wherein the approximate component is decomposed into a lower grade terrain approximate component and a lower grade terrain detail component every time the composite terrain is decomposed, and the total data quantity is kept unchanged. The decomposition algorithm is expressed as the formula:
Figure 993789DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 364728DEST_PATH_IMAGE013
in order to disperse the number of the sample data,
Figure 392858DEST_PATH_IMAGE014
and
Figure 216457DEST_PATH_IMAGE015
low-pass and high-pass filter coefficients are used for the decomposition process,
Figure 870293DEST_PATH_IMAGE016
and
Figure 876164DEST_PATH_IMAGE017
are respectively the first
Figure 324463DEST_PATH_IMAGE018
And (3) terrain approximate components and terrain detail components obtained by level wavelet decomposition.
(ii) Determining decomposition series and reconstructing separation results:
based on decomposed terrain approximation components
Figure 651670DEST_PATH_IMAGE016
And topographic detail component
Figure 843617DEST_PATH_IMAGE017
Determining the number of decomposition steps based on the frequency analysis information
Figure 923568DEST_PATH_IMAGE019
Is in 6 grades, and the separation result is reconstructed and divided intoThe reconstruction of the separation result is the inverse process of the decomposition algorithm, and the principle is to use the terrain approximate component
Figure 542768DEST_PATH_IMAGE016
And topographic detail component
Figure 855807DEST_PATH_IMAGE017
The separation results were recovered and the reconstruction formula was as follows:
Figure 585865DEST_PATH_IMAGE020
wherein, in the step (A),
Figure 785903DEST_PATH_IMAGE021
and
Figure 326737DEST_PATH_IMAGE022
are respectively as
Figure 612224DEST_PATH_IMAGE023
And
Figure 880395DEST_PATH_IMAGE024
the conjugate of (2) is to repeat the coefficients of each filter bank, and finally the separated water depth model is obtained
Figure 934938DEST_PATH_IMAGE025
And
Figure 879629DEST_PATH_IMAGE026
thereby separating different levels of sub-sea geographic entities.
The feature extraction comprises the steps of determining the range of the seabed geographic entity, extracting morphological feature parameters and constructing an element information table, a water depth model of the seabed geographic entity after the water depth range is intercepted is shown in figure 5, an attached table 1 shows the element information table of the seabed geographic entity, and the specific steps comprise:
(i) determining the range of the seabed geographic entity:
water depth model based on separation
Figure 652413DEST_PATH_IMAGE025
Determining the water depth range of the seabed geographic entity as 200m, 3600m]Determining the range of the seabed geographic entity
Figure 989854DEST_PATH_IMAGE066
Wherein, in the step (A),
Figure 649636DEST_PATH_IMAGE029
and
Figure 781541DEST_PATH_IMAGE030
respectively are the plane position coordinate values of the water depth point on the seabed geographic entity boundary,
Figure 776041DEST_PATH_IMAGE031
the total number of water depth points on the seabed geographic entity boundary;
based on seabed geographic entity scope
Figure 651593DEST_PATH_IMAGE067
For the original water depth model
Figure 195576DEST_PATH_IMAGE006
Range interception and output are carried out to obtain an intercepted water depth model
Figure 232802DEST_PATH_IMAGE068
Wherein, in the step (A),X s,l andY s,l respectively a water depth model after cutting
Figure 980178DEST_PATH_IMAGE069
To (1)
Figure 393842DEST_PATH_IMAGE007
Line and first
Figure 28217DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 236344DEST_PATH_IMAGE035
to interceptModel of water depth
Figure 205437DEST_PATH_IMAGE069
The depth value of the plane position is shown in figure 5, and the intercepted water depth model is shown in figure 5.
(ii) Morphological characteristic parameter extraction:
water depth model based on intercepted water depth
Figure 422792DEST_PATH_IMAGE069
Extracting morphological characteristic parameters of the seabed geographic entity by applying GIS software
Figure 410209DEST_PATH_IMAGE052
Wherein
Figure 54817DEST_PATH_IMAGE011
And
Figure 776785DEST_PATH_IMAGE053
respectively representing the length and width of the subsea geographic entity,
Figure 282984DEST_PATH_IMAGE054
and
Figure 141218DEST_PATH_IMAGE036
respectively representing the maximum and minimum water depths of the sub-sea geographic entity,
Figure 956728DEST_PATH_IMAGE055
the average grade representing the seafloor geographical entity, expressed as the degree of slope of the local surface slope, may be expressed as a simplified difference formula when performing the grade calculation:
Figure 634834DEST_PATH_IMAGE056
wherein the content of the first and second substances,
Figure 443259DEST_PATH_IMAGE057
is prepared fromxElevation or depth of water rate of change;
Figure 156000DEST_PATH_IMAGE058
is prepared fromyElevation or depth of water rate of change; obtaining the length of the seabed geographic entity
Figure 142410DEST_PATH_IMAGE070
=527 km, width
Figure 573391DEST_PATH_IMAGE071
=271 km, maximum water depth
Figure 686972DEST_PATH_IMAGE072
= 3600m, minimum water depth
Figure 254220DEST_PATH_IMAGE036
= 200m, average slope
Figure 411532DEST_PATH_IMAGE055
= 0.65°。
Morphological characteristic parameter based on the seabed geographic entity
Figure 64230DEST_PATH_IMAGE059
Determining a rank of the subsea geographic entity
Figure 214457DEST_PATH_IMAGE060
Is a first order sum type
Figure 901791DEST_PATH_IMAGE061
Is a big land slope.
(iii) And (3) constructing an element information table:
based on seabed geographic entity scope
Figure 230004DEST_PATH_IMAGE062
Morphological characteristic parameter of seabed geographic entity
Figure 369998DEST_PATH_IMAGE059
Grade of seabed geographic entity
Figure 559802DEST_PATH_IMAGE060
And type
Figure 101642DEST_PATH_IMAGE061
And constructing an element information table of the seabed geographic entityManifest= {Grade，Type，Area，Parameters}= first order, continental slope, [200, 3600 =]Km, km, 3600m, 200m, 0.65 degrees, the element information table of the seabed geographic entity obtained by construction is shown in the following table 1:
Figure DEST_PATH_IMAGE073
example 2 specific application of the example of typical sea and mountain terrain
As shown in fig. 1, the present example describes a wavelet and filter-based composite seabed geographic entity progressive decomposition method, which includes three steps of data preprocessing, composite seabed geographic entity separation and feature extraction.
Firstly, data preprocessing comprises multi-beam water depth data preprocessing and landform modeling, and a water depth model is constructed; secondly, separating the composite seabed geographic entities, namely performing step-by-step decomposition of the composite terrain, determination of the decomposition grade number and reconstruction of separation results by adopting discrete wavelet transform, so as to separate the seabed geographic entities with different grades; and finally, the characteristic extraction comprises the steps of determining the range of the seabed geographic entity, extracting morphological characteristic parameters and constructing an element information table.
The data preprocessing comprises the steps of multi-beam water depth data preprocessing and terrain and landform modeling, a water depth model is constructed, fig. 2 shows that an original water depth model containing 386 rows and 470 columns is obtained by utilizing a spline function interpolation method for modeling based on 258 ten thousand depth measuring point original multi-beam depth measuring data, and the specific steps comprise:
(i) preprocessing multi-beam water depth data:
CUBE filtering, sound velocity correction and abnormal value elimination processing are carried out on the original multi-beam water depth data to obtain a processed multi-beam water depth data set
Figure 912341DEST_PATH_IMAGE064
Wherein x is m And y m Respectively all multiple wavesThe plane position coordinate value of the beam depth measurement point,
Figure 539631DEST_PATH_IMAGE002
the depth value of the multi-beam sounding point.
(ii) Landform modeling:
multi-beam water depth data set based on processing
Figure 782394DEST_PATH_IMAGE064
Constructing and obtaining an original water depth model by adopting a spline function interpolation method
Figure 444319DEST_PATH_IMAGE065
WhereinX s,l AndY s,l respectively the original water depth model
Figure 865067DEST_PATH_IMAGE006
To (1) a
Figure 714075DEST_PATH_IMAGE007
Line and first
Figure 760528DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 276960DEST_PATH_IMAGE009
as an original water depth model
Figure 367145DEST_PATH_IMAGE006
The depth value of the position, the total row number of the original water depth model
Figure 703448DEST_PATH_IMAGE010
Is 386 rows and total column
Figure 288013DEST_PATH_IMAGE011
For column 470, the original water depth model is constructed as shown in fig. 2.
The separation of the composite seabed geographic entities comprises the steps of performing stepwise decomposition of the composite terrain, determining the decomposition grade number and reconstructing a separation result by adopting discrete wavelet transform, thereby separating the seabed geographic entities with different grades, and the separation results of the composite seabed geographic entities are shown in figures 3 and 4 and are respectively a first-grade seabed geographic entity: a big land slope; second-level seabed geographic entity: the specific steps of the sea hills, the sea hills and the sea floor canyons include:
(i) and (3) performing stepwise decomposition of the composite terrain by adopting discrete wavelet transform:
based on original water depth model
Figure 658952DEST_PATH_IMAGE065
Discrete wavelet transformation is carried out, the Mallat algorithm is combined with a filter, wavelet multi-scale analysis is realized, the composite terrain is decomposed into low-frequency terrain approximate components and high-frequency terrain detail components, the approximate components are decomposed into lower-level terrain approximate components and lower-level terrain detail components every time decomposition is carried out, and the total data amount is kept unchanged. The decomposition algorithm is expressed as the formula:
Figure 421502DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 510681DEST_PATH_IMAGE013
in order to disperse the number of the sample data,
Figure 633358DEST_PATH_IMAGE014
and
Figure 124382DEST_PATH_IMAGE015
low-pass and high-pass filter coefficients are used for the decomposition process,
Figure 556369DEST_PATH_IMAGE016
and
Figure 867265DEST_PATH_IMAGE017
are respectively the first
Figure 793633DEST_PATH_IMAGE018
And (3) terrain approximate components and terrain detail components obtained by level wavelet decomposition.
ii) decomposition progression determination and separation result reconstruction:
based on decomposed terrain approximation components
Figure 139163DEST_PATH_IMAGE016
And topographic detail component
Figure 243517DEST_PATH_IMAGE017
Determining the number of decomposition steps based on the frequency analysis information
Figure 41709DEST_PATH_IMAGE019
6 grades, and reconstructing a separation result, wherein the reconstruction of the separation result is the inverse process of a decomposition algorithm, and the principle is to utilize a terrain approximate component
Figure 37346DEST_PATH_IMAGE016
And topographic detail component
Figure 971804DEST_PATH_IMAGE017
The separation results were recovered and the reconstruction formula was as follows:
Figure 745594DEST_PATH_IMAGE020
wherein, in the process,
Figure 296661DEST_PATH_IMAGE021
and
Figure 830411DEST_PATH_IMAGE022
are respectively as
Figure 635687DEST_PATH_IMAGE023
And
Figure 331110DEST_PATH_IMAGE024
the conjugate of (2) is to repeat the coefficients of each filter bank, and finally the separated water depth model is obtained
Figure 103894DEST_PATH_IMAGE025
And
Figure 175756DEST_PATH_IMAGE026
thereby separating different levels of sub-sea geographic entities.
The reconstruction of the separation result is the inverse process of the decomposition algorithm, and the principle is to use the terrain approximate component
Figure 334073DEST_PATH_IMAGE016
And topographic detail component
Figure 465977DEST_PATH_IMAGE017
The separation results were recovered and the reconstruction formula was as follows:
Figure 460478DEST_PATH_IMAGE020
wherein, in the step (A),
Figure 336030DEST_PATH_IMAGE021
and
Figure 850319DEST_PATH_IMAGE022
are respectively as
Figure 153125DEST_PATH_IMAGE023
And
Figure 634922DEST_PATH_IMAGE024
i.e. re-sequence the coefficients of the filter banks.
The characteristic extraction comprises the steps of determining the range of the seabed geographic entity, extracting morphological characteristic parameters and constructing an element information table, and a water depth model of the seabed geographic entity subjected to slope range interception is shown in figure 6, and the specific steps comprise:
(i) determining the range of the seabed geographic entity:
water depth model based on separation
Figure 48585DEST_PATH_IMAGE026
To find a model of the gradient
Figure 181495DEST_PATH_IMAGE074
Wherein, in the step (A),X s,l andY s,l are respectively asSlope model
Figure 655202DEST_PATH_IMAGE039
To (1) a
Figure 624295DEST_PATH_IMAGE007
Line and first
Figure 841650DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 330531DEST_PATH_IMAGE040
is a slope model
Figure 975139DEST_PATH_IMAGE039
The slope value of the plane position.
Based on gradient model
Figure 431528DEST_PATH_IMAGE039
According to the gradient range of the seabed geographic entity
Figure 186995DEST_PATH_IMAGE075
Determining the range of the seabed geographic entity
Figure 91234DEST_PATH_IMAGE076
Wherein, in the step (A),
Figure 641164DEST_PATH_IMAGE043
and
Figure 584850DEST_PATH_IMAGE044
respectively the plane position coordinate values of the gradient points on the boundary of the seabed geographic entity,
Figure 894739DEST_PATH_IMAGE045
the total number of slope points lying on the seafloor geographical physical boundary.
Based on seabed geographic entity scope
Figure 607480DEST_PATH_IMAGE077
For the original water depth model
Figure 593891DEST_PATH_IMAGE006
Range interception and output are carried out to obtain an intercepted water depth model
Figure 759293DEST_PATH_IMAGE078
Wherein, in the step (A),
Figure 105830DEST_PATH_IMAGE079
respectively a water depth model after cutting
Figure 204236DEST_PATH_IMAGE080
To (1) a
Figure 361548DEST_PATH_IMAGE007
Line and first
Figure 764978DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 665938DEST_PATH_IMAGE035
for the water depth model after cutting
Figure 353271DEST_PATH_IMAGE080
The depth value of the plane position is shown in the attached figure 6, and the intercepted water depth model is shown in the attached figure.
(ii) Morphological characteristic parameter extraction:
water depth model based on intercepted water depth
Figure 681485DEST_PATH_IMAGE080
Extracting morphological characteristic parameters of the seabed geographic entity by applying GIS software
Figure 94184DEST_PATH_IMAGE052
In which
Figure 533256DEST_PATH_IMAGE011
And
Figure 75095DEST_PATH_IMAGE053
respectively representing the length and width of the sub-sea geographic entity,
Figure 574210DEST_PATH_IMAGE054
and
Figure 952233DEST_PATH_IMAGE036
respectively representing the maximum and minimum water depths of the sub-sea geographic entity,
Figure 194995DEST_PATH_IMAGE055
the average grade representing the seafloor geographical entity, expressed as the degree of slope of the local surface slope, may be expressed as a simplified difference formula when performing the grade calculation:
Figure 591341DEST_PATH_IMAGE056
wherein the content of the first and second substances,
Figure 261357DEST_PATH_IMAGE057
is prepared fromxElevation or depth of water rate of change;
Figure 625211DEST_PATH_IMAGE058
is prepared fromyElevation or depth of water rate of change; obtaining the length of the seabed geographic entity
Figure 671665DEST_PATH_IMAGE011
= 2150m, width
Figure 188097DEST_PATH_IMAGE053
= 2060m, maximum water depth
Figure 29014DEST_PATH_IMAGE054
= 2350m, minimum water depth
Figure 381629DEST_PATH_IMAGE036
= 370m, average slope
Figure 966194DEST_PATH_IMAGE055
= 12.35°。
Based onMorphological characteristic parameter of the seabed geographic entity
Figure 337132DEST_PATH_IMAGE059
Determining a rank of the subsea geographic entity
Figure 348951DEST_PATH_IMAGE060
Is of two-level sum type
Figure 687397DEST_PATH_IMAGE061
Is Haishan.
(iii) And (3) constructing an element information table:
based on seabed geographic entity scope
Figure 810074DEST_PATH_IMAGE062
Morphological characteristic parameter of seabed geographic entity
Figure 35519DEST_PATH_IMAGE059
Grade of seabed geographic entity
Figure 483818DEST_PATH_IMAGE060
And type
Figure 545446DEST_PATH_IMAGE061
Constructing an element information table of the seabed geographic entity,Manifest= {Grade，Type，Area，Parameters}= second order, sea mountain, [5 °, 15 °]2150m, 2060m, 2350m, 370m, 12.35 ° }, the element information table of the seabed geographic entity obtained by construction is shown in the following table 2:
Figure 471813DEST_PATH_IMAGE081
the above examples are merely illustrative of several embodiments of the present invention, and the description thereof is more specific and detailed, but not to be construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims (8)

1. A composite seabed geographic entity progressive decomposition method based on wavelets and filters is characterized by comprising three steps of data preprocessing, composite seabed geographic entity separation and feature extraction: firstly, data preprocessing comprises multi-beam water depth data preprocessing and landform modeling, and a water depth model is constructed; secondly, separating the composite seabed geographic entities, namely performing step-by-step decomposition of the composite terrain, determination of the decomposition grade number and reconstruction of separation results by adopting discrete wavelet transform, so as to separate the seabed geographic entities with different grades; and finally, the characteristic extraction comprises the steps of determining the range of the seabed geographic entity, extracting morphological characteristic parameters and constructing an element information table.
2. The wavelet and filter based progressive decomposition method for composite seafloor geographical entities as claimed in claim 1, wherein the multi-beam depth data preprocessing comprises: CUBE filtering, sound velocity correction and abnormal value elimination processing are carried out on the original multi-beam water depth data to obtain a processed multi-beam water depth data set
Figure 574355DEST_PATH_IMAGE001
Whereinx m Andy m respectively the plane position coordinate values of all the multi-beam sounding points,
Figure 509950DEST_PATH_IMAGE002
the depth value of the multi-beam sounding point,
Figure 139383DEST_PATH_IMAGE003
the number of the depth measurement points is,
Figure 185836DEST_PATH_IMAGE004
and
Figure 702268DEST_PATH_IMAGE003
are all natural numbers.
3. The wavelet and filter based composite seafloor geographical entity progressive decomposition method of claim 2, wherein the topographic modeling: multi-beam water depth data set based on processing
Figure 808765DEST_PATH_IMAGE001
Constructing and obtaining an original water depth model by adopting a spline function interpolation method
Figure 895800DEST_PATH_IMAGE005
Wherein, in the step (A),X s,l andY s,l respectively the original water depth model
Figure 480366DEST_PATH_IMAGE006
To (1) a
Figure 116883DEST_PATH_IMAGE007
Line and first
Figure 380899DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 470078DEST_PATH_IMAGE009
as an original water depth model
Figure 858334DEST_PATH_IMAGE006
The depth value of the location in (a),
Figure 349358DEST_PATH_IMAGE010
and
Figure 282810DEST_PATH_IMAGE011
respectively the total row number and the total column number of the original water depth model,
Figure 859285DEST_PATH_IMAGE007
、
Figure 785652DEST_PATH_IMAGE008
、
Figure 380451DEST_PATH_IMAGE010
and
Figure 999651DEST_PATH_IMAGE011
are all natural numbers.
4. A wavelet and filter based composite seafloor geographical entity progressive decomposition method as claimed in claim 1 or 3, wherein the discrete wavelet transform is used for progressive decomposition of composite terrain: based on original water depth model
Figure 797843DEST_PATH_IMAGE006
Performing discrete wavelet transform, combining a Mallat algorithm with a filter to realize wavelet multi-scale analysis, decomposing the composite terrain into low-frequency terrain approximate components and high-frequency terrain detail components, decomposing the approximate components into lower-level terrain approximate components and terrain detail components each time, and keeping the total data unchanged; the decomposition algorithm is expressed as the formula:
Figure 544213DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 744250DEST_PATH_IMAGE013
in order to disperse the number of the sample data,
Figure 534351DEST_PATH_IMAGE014
and
Figure 819839DEST_PATH_IMAGE015
low-pass and high-pass filter coefficients are used for the decomposition process,
Figure 390805DEST_PATH_IMAGE016
and
Figure 710928DEST_PATH_IMAGE017
are respectively the first
Figure 157084DEST_PATH_IMAGE018
And (3) terrain approximate components and terrain detail components obtained by level wavelet decomposition.
5. The wavelet and filter based composite seafloor geographical entity progressive decomposition method of claim 4, wherein the decomposition level determination and separation result reconstruction: based on decomposed terrain approximation components
Figure 195447DEST_PATH_IMAGE016
And topographic detail component
Figure 532887DEST_PATH_IMAGE017
Determining the number of decomposition steps based on the frequency analysis information
Figure 691205DEST_PATH_IMAGE019
And reconstructing the separation result, wherein the reconstruction formula is as follows:
Figure 823109DEST_PATH_IMAGE020
wherein, in the step (A),
Figure 817610DEST_PATH_IMAGE021
and
Figure 709474DEST_PATH_IMAGE022
are respectively as
Figure 473031DEST_PATH_IMAGE023
And
Figure 41415DEST_PATH_IMAGE024
the conjugate of (2) is to repeat the coefficients of each filter bank, and finally the separated water depth model is obtained
Figure 523212DEST_PATH_IMAGE025
And
Figure 454652DEST_PATH_IMAGE026
thereby separating different levels of sub-sea geographic entities.
6. The wavelet and filter based progressive decomposition method of composite seabed geographic entity as claimed in claim 5, wherein said seabed geographic entity range is determined by:
(a) water depth model based on separation
Figure 338295DEST_PATH_IMAGE025
According to the depth of water of the seabed geographic entity
Figure 812001DEST_PATH_IMAGE027
Determining the range of the seabed geographic entity
Figure 62985DEST_PATH_IMAGE028
Wherein, in the step (A),
Figure 545919DEST_PATH_IMAGE029
and
Figure 284068DEST_PATH_IMAGE030
respectively are the plane position coordinate values of the water depth point on the seabed geographic entity boundary,
Figure 709102DEST_PATH_IMAGE031
the total number of water depth points on the seabed geographic entity boundary; based on seabed geographic entity scope
Figure 431070DEST_PATH_IMAGE032
For the original water depth model
Figure 202848DEST_PATH_IMAGE006
Range interception and output are carried out to obtain an intercepted water depth model
Figure 326662DEST_PATH_IMAGE033
Wherein, in the step (A),X s,l andY s,l respectively a water depth model after cutting
Figure 142172DEST_PATH_IMAGE034
To (1) a
Figure 603633DEST_PATH_IMAGE007
Go, first
Figure 428370DEST_PATH_IMAGE008
The coordinate values of the plane positions of the rows,
Figure 141111DEST_PATH_IMAGE035
for the water depth model after cutting
Figure 143833DEST_PATH_IMAGE034
The depth value of the position of the plane in question,
Figure 309235DEST_PATH_IMAGE036
for the water depth model after cutting
Figure 186930DEST_PATH_IMAGE034
The minimum water depth of the water in the water tank,
Figure 285336DEST_PATH_IMAGE037
for the water depth model after cutting
Figure 990118DEST_PATH_IMAGE034
The maximum water depth of the water to be treated,
Figure 642816DEST_PATH_IMAGE010
and
Figure 543776DEST_PATH_IMAGE011
respectively a water depth model after cutting
Figure 231110DEST_PATH_IMAGE034
The total number of rows and the total number of columns,
Figure 77099DEST_PATH_IMAGE007
、
Figure 217094DEST_PATH_IMAGE008
、
Figure 656165DEST_PATH_IMAGE010
and
Figure 198005DEST_PATH_IMAGE011
are all natural numbers;
(b) water depth model based on separation
Figure 447852DEST_PATH_IMAGE026
To find a model of the gradient
Figure 340721DEST_PATH_IMAGE038
Wherein, in the step (A),
Figure 583484DEST_PATH_IMAGE039
respectively being a model of gradient
Figure 494677DEST_PATH_IMAGE040
To (1) a
Figure 430272DEST_PATH_IMAGE007
Line and first
Figure 810438DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 607624DEST_PATH_IMAGE041
is a slope model
Figure 858476DEST_PATH_IMAGE040
A slope value of the plane position; based on gradient model
Figure 964973DEST_PATH_IMAGE040
According to the gradient range of the seabed geographic entity
Figure 830771DEST_PATH_IMAGE042
Determining the range of the seabed geographic entity
Figure 415336DEST_PATH_IMAGE043
Wherein, in the step (A),
Figure 51854DEST_PATH_IMAGE044
and
Figure 63672DEST_PATH_IMAGE045
respectively the plane position coordinate values of the gradient points on the boundary of the seabed geographic entity,
Figure 903584DEST_PATH_IMAGE046
the total number of slope points on the seabed geographic entity boundary; based on seabed geographic entity scope
Figure 291840DEST_PATH_IMAGE047
For the original water depth model
Figure 517285DEST_PATH_IMAGE006
Range interception is carried out and output is carried out to obtain an intercepted water depth model
Figure 480430DEST_PATH_IMAGE048
Wherein, in the step (A),
Figure 791326DEST_PATH_IMAGE049
respectively a water depth model after cutting
Figure 983273DEST_PATH_IMAGE050
To (1) a
Figure 79536DEST_PATH_IMAGE007
Line and first
Figure 433157DEST_PATH_IMAGE008
The plane position coordinate values of the columns,
Figure 231349DEST_PATH_IMAGE035
for the water depth model after cutting
Figure 226986DEST_PATH_IMAGE050
The depth value of the position of the plane in question,
Figure 679221DEST_PATH_IMAGE051
for the water depth model after cutting
Figure 469322DEST_PATH_IMAGE050
The minimum slope of the slope,
Figure 754810DEST_PATH_IMAGE052
for the water depth model after cutting
Figure 304871DEST_PATH_IMAGE050
The maximum slope of the slope,
Figure 359415DEST_PATH_IMAGE010
and
Figure 320418DEST_PATH_IMAGE011
respectively a water depth model after cutting
Figure 342469DEST_PATH_IMAGE050
The total number of rows and the total number of columns,
Figure 148751DEST_PATH_IMAGE007
、
Figure 323381DEST_PATH_IMAGE008
、
Figure 455285DEST_PATH_IMAGE010
and
Figure 466097DEST_PATH_IMAGE011
are all natural numbers.
7. The wavelet and filter based composite seafloor geographical entity progressive decomposition method as claimed in claim 6, wherein the morphological feature parameter extraction: water depth model based on intercepted water depth
Figure 76070DEST_PATH_IMAGE053
Extracting morphological characteristic parameters of the seabed geographic entity by using GIS software
Figure 370785DEST_PATH_IMAGE054
Wherein
Figure 925788DEST_PATH_IMAGE011
And
Figure 673164DEST_PATH_IMAGE055
respectively representing the length and width of the sub-sea geographic entity,
Figure 86828DEST_PATH_IMAGE056
and
Figure 986782DEST_PATH_IMAGE036
respectively representing the maximum and minimum water depths of the sub-sea geographic entity,
Figure 460488DEST_PATH_IMAGE057
the average grade representing the seafloor geographical entity, expressed as the degree of slope of the local surface slope, may be expressed as a simplified difference formula when performing the grade calculation:
Figure 429581DEST_PATH_IMAGE058
wherein the content of the first and second substances,
Figure 646936DEST_PATH_IMAGE059
is prepared fromxElevation or depth of water rate of change;
Figure 899932DEST_PATH_IMAGE060
is prepared fromyElevation or depth of water rate of change; morphological characteristic parameter based on the seabed geographic entity
Figure 544540DEST_PATH_IMAGE061
Determining a rank of the subsea geographic entity
Figure 266508DEST_PATH_IMAGE062
And type
Figure 38286DEST_PATH_IMAGE063
。
8. The wavelet and filter based composite seafloor geographical entity progressive decomposition method as claimed in claim 7, wherein the element information table constructs: based on seabed geographic entity scope
Figure 630942DEST_PATH_IMAGE064
Morphological characteristic parameter of seabed geographic entity
Figure 446451DEST_PATH_IMAGE061
Grade of seabed geographic entity
Figure 907913DEST_PATH_IMAGE062
And type
Figure 467070DEST_PATH_IMAGE063
And constructing an element information table of the seabed geographic entity
Figure 179811DEST_PATH_IMAGE065
。
CN202210732260.2A 2022-06-27 2022-06-27 Wavelet and filter-based composite seabed geographic entity progressive decomposition method Pending CN114820951A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210732260.2A CN114820951A (en) 2022-06-27 2022-06-27 Wavelet and filter-based composite seabed geographic entity progressive decomposition method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210732260.2A CN114820951A (en) 2022-06-27 2022-06-27 Wavelet and filter-based composite seabed geographic entity progressive decomposition method

Publications (1)

Publication Number Publication Date
CN114820951A true CN114820951A (en) 2022-07-29

Family

ID=82520786

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210732260.2A Pending CN114820951A (en) 2022-06-27 2022-06-27 Wavelet and filter-based composite seabed geographic entity progressive decomposition method

Country Status (1)

Country Link
CN (1) CN114820951A (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013055236A1 (en) * 2011-10-13 2013-04-18 Institute Of Geological & Nuclear Sciences Limited System and method for modelling multibeam backscatter from a seafloor
CN103400405A (en) * 2013-08-01 2013-11-20 国家海洋局第二海洋研究所 Multi-beam bathymetric chart construction method based on seabed digital depth model feature extraction
CN104613945A (en) * 2015-02-11 2015-05-13 国家海洋局第二海洋研究所 Reconstruction method for terrain of shallow-sea large-sized complicated sand wave area
US20150243074A1 (en) * 2013-07-08 2015-08-27 The Second Institute Of Oceanography, Soa Submarine topography construction method based on multi-source water depth data integration
US20190339414A1 (en) * 2017-02-15 2019-11-07 Halliburton Energy Services, Inc. Evaluating subsea geodetic data
CN110532615A (en) * 2019-07-26 2019-12-03 自然资源部第二海洋研究所 A kind of decomposition method step by step of shallow sea complicated landform
CN114663640A (en) * 2022-05-20 2022-06-24 自然资源部第二海洋研究所 Submarine geographic entity demarcation and classification method based on landform and structural characteristics

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013055236A1 (en) * 2011-10-13 2013-04-18 Institute Of Geological & Nuclear Sciences Limited System and method for modelling multibeam backscatter from a seafloor
US20150243074A1 (en) * 2013-07-08 2015-08-27 The Second Institute Of Oceanography, Soa Submarine topography construction method based on multi-source water depth data integration
CN103400405A (en) * 2013-08-01 2013-11-20 国家海洋局第二海洋研究所 Multi-beam bathymetric chart construction method based on seabed digital depth model feature extraction
CN104613945A (en) * 2015-02-11 2015-05-13 国家海洋局第二海洋研究所 Reconstruction method for terrain of shallow-sea large-sized complicated sand wave area
US20190339414A1 (en) * 2017-02-15 2019-11-07 Halliburton Energy Services, Inc. Evaluating subsea geodetic data
CN110532615A (en) * 2019-07-26 2019-12-03 自然资源部第二海洋研究所 A kind of decomposition method step by step of shallow sea complicated landform
CN114663640A (en) * 2022-05-20 2022-06-24 自然资源部第二海洋研究所 Submarine geographic entity demarcation and classification method based on landform and structural characteristics

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
夏伟等: "基于正交小波变换的海底地形复杂程度分类方法研究", 《武汉大学学报(信息科学版)》 *
张伙带等: "基于多波束数据的南海海盆洋壳区海山地形特征", 《海洋地质与第四纪地质》 *
汪九尧 等: "基于近底原位观测的小尺度海底沙波地形小波分解", 《海洋学研究》 *
贾帅东等: "基于CUBE算法的多波束水深异常值剔除", 《测绘科学》 *

Similar Documents

Publication Publication Date Title
CN109190538B (en) Sediment-laden river delta coastal zone evolution analysis method based on remote sensing technology
Jenson Applications of hydrologic information automatically extracted from digital elevation models
Hudson et al. MODIS observed increase in duration and spatial extent of sediment plumes in Greenland fjords
Tugume et al. Precambrian crustal structure in Africa and Arabia: evidence lacking for secular variation
Robinson The accuracy of digital elevation models derived from digitised contour data
Andrews et al. Automated feature extraction and spatial organization of seafloor pockmarks, Belfast Bay, Maine, USA
Trevisani et al. Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin
Bilaşco et al. Implementation of the USLE model using GIS techniques. Case study the Someşean Plateau
CN108985304A (en) It is a kind of based on the Structure of the deposits extraction method for shallowly cuing open data
Gunnink et al. The three-dimensional groundwater salinity distribution and fresh groundwater volumes in the Mekong Delta, Vietnam, inferred from geostatistical analyses
CN110532615A (en) A kind of decomposition method step by step of shallow sea complicated landform
Wang et al. An automated procedure to calculate the morphological parameters of superimposed rhythmic bedforms
CN116187168A (en) Method for improving water depth inversion accuracy based on neural network-gravity information wavelet decomposition
CN105718725A (en) Quantification method of contaminated bottom sediment and contaminants thereof
CN108320291A (en) Submarine geomorphy boundary extraction method based on Otsu algorithm
Zieger et al. Mapping reef features from multibeam sonar data using multiscale morphometric analysis
Cao et al. Terrain relief periods of loess landforms based on terrain profiles of the Loess Plateau in northern Shaanxi Province, China
CN113834547A (en) River virtual station water level time sequence reconstruction method and system
CN114820951A (en) Wavelet and filter-based composite seabed geographic entity progressive decomposition method
Őrsi Quantifying the geodiversity of a study area in the Great Hungarian Plain
CN116861955A (en) Method for inverting submarine topography by machine learning based on topography unit partition
Chakraborty et al. Multifractal detrended fluctuation analysis to compare coral bank and seafloor seepage area-related characterization along the central western continental margin of India
CN115905877A (en) Depth sounding model inversion method and system based on VGGNet
Outcalt et al. Fractal physiography?
Brook et al. Double Fourier series analysis of cockpit and doline karst near Browns Town, Jamaica

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20220729