CN112907615A - Submarine landform unit contour and detail identification method based on region growing - Google Patents
Submarine landform unit contour and detail identification method based on region growing Download PDFInfo
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Abstract
The invention discloses a submarine geomorphic unit outline and detail identification method based on region growth. The method comprises the following basic steps: 1) determining an initial seed point according to the water depth value, the curvature value and the gradient value of each observation point in the area to be identified; 2) performing seed region growth based on the water depth value; 3) performing seed region growth based on the gradient value; 4) carrying out fuzzy processing on the seed area; 5) extracting the outline of the landform unit; 6) supplementing details of the geomorphic unit based on the slope value; 7) details of the geomorphic units are supplemented based on the curvature values. The method has the advantages of simplicity, small calculated amount, good real-time performance, manpower saving, easiness in implementation and the like. The method is suitable for recognizing the contour and the details of the submarine geomorphic unit.
Description
Technical Field
The invention relates to the technical fields of ocean mapping, big data processing, edge identification, region growing and the like, in particular to a submarine geomorphologic unit contour and detail identification method based on region growing.
Background
Submarine geomorphologic unit edge identification is the basis for ocean development. By identifying the edge of the submarine geomorphic unit, data such as fluctuation change of a seabed, motion rules of submarine plates and the like can be acquired. The data can play an important role in marine science and engineering such as underwater pipeline arrangement, marine oil and gas exploration and environmental monitoring.
Through the measurement of the submarine landform, the information of the submarine measuring points can be obtained, including the position, the altitude, the direction and the like. At present, a multi-beam sounding system is commonly used for measurement, the system can emit hundreds of beams in a sector with orthogonal tracks, and the water depth is calculated by detecting the propagation time of sound waves from a transducer to the seabed and combining multiple parameters such as the current sound velocity, the sea level and the ship posture. Compared with the traditional single-beam echo detection technology, the multi-beam sounding system has the characteristics of high precision, wide coverage and the like.
When the submarine landform is observed, a two-dimensional topographic map is drawn by combining the position with water depth data, and the elevation change of a landform unit can be visually reflected by the color depth change of an observation point in the map. The types of the submarine geomorphic units are influenced by various factors such as earthquakes, ocean currents, biological effects and the like, have complexity and diversity and comprise coral reefs, submarine canyons, deep water channels, sea mountains, carbonate terraces, cliffs, landslides and the like, and bring certain difficulty to edge identification. Taking the submarine coral reef as an example, a great deal of details still exist in the edge contour, and all the details cannot be accurately identified only by the altitude characteristics.
Although some automatic extraction methods for the edge of the submarine geomorphic unit exist at present, such methods are mainly used for extracting specific types of edges such as ridge lines, valley lines, coastlines and the like, and the extraction of the whole edge or all details of the submarine geomorphic unit is difficult to realize.
Disclosure of Invention
The invention aims to extract the outline and the detail of a landform unit. The method has the advantages of simplicity, small calculated amount, good real-time performance, manpower saving, easiness in implementation and the like. The method is suitable for recognizing the contour and the details of the submarine geomorphic unit.
The invention comprises the following steps:
(1) determining an initial seed point:
inputting water depth values h of observation points in the area to be identifiediCurvature value ciAnd a gradient value siIf s is satisfiedi≥s1Or | ci|≥c1Then the i point is the initial seed point, where s1、c1The gradient threshold and the curvature threshold are used for determining the initial seed point, the gradient range and the curvature range of the area to be identified are manually set, i is 1,2, …, n is the number of observation points of the area to be identified;
(2) and (3) performing seed region growth based on the water depth value:
if the i point is a non-seed point adjacent to the seed point and satisfies hi≥h2Or hi≤h1Then the i point is marked as a seed point, where i ═ 1,2, …, n, h1<h2,h1、h2All the water depth thresholds are manually set according to the water depth range of the area to be identified;
(3) growing the seed region based on the gradient value:
if the i point is a non-seed point adjacent to the seed point, constructing an m × m grid by taking the i point as the center, and if s isi≥szThen the i point is marked as a seed point, where szThe median of the gradient values of the seed points adjacent to the non-seed points in the m x m grid, wherein m is an odd number greater than 1;
(4) fuzzy processing of seed regions:
constructing l multiplied by l grid by taking the i point as a center, if the i point is a seed point and n is satisfiedpIf the number is less than or equal to p, marking the point i as a non-seed point; if the i point is a non-seed point and n is satisfiedqQ is less than or equal to the sum of the values of the i point, the i point is marked as a seed point, wherein p and q are fuzzy parameters and are set manually, i is 1,2, …, n and n are set manuallyp、nqThe number of the seed points and the number of the non-seed points in the grid are respectively l multiplied by l, wherein l is an odd number larger than 1;
(5) extracting the outline of the landform unit:
extracting all seed points adjacent to the non-seed points to form the outline of the landform unit;
(6) complementing details of the landform unit based on the slope value:
performing median filtering on the gradient value of the seed point in the step (4), if the gradient value meets the requirementThe i point is marked as a landform minutia; extracting all minutiae adjacent to non-minutiae for complementing the details of the geomorphic unit, wherein s2The grade threshold, for additional detail, is manually set,the median filtered slope value is obtained;
(7) complementing details of the geomorphic unit based on the curvature value:
and (4) aiming at the slope value and the curvature value of the seed point in the step (4), applying a non-maximum value inhibition method to obtain a local extreme value point in the contour of the landform unit, wherein the local extreme value point is used for supplementing the details of the landform unit.
Drawings
FIG. 1 is a water depth diagram of a seabed coral reef;
FIG. 2 is a graph of an initial seed point distribution;
FIG. 3 is a graph of the result of seeded region growth based on water depth values;
FIG. 4 is a graph of seed region growth results based on slope values;
FIG. 5 is a graph of seed region fuzzy results;
FIG. 6 is a profile view of a relief element;
FIG. 7 is a detail view of a relief unit supplemented based on slope values;
fig. 8 is a detailed diagram of a topographical unit supplemented based on non-maxima suppression.
Detailed Description
According to the method, the contour and detail extraction of the coral reef landform unit is realized by adopting a region growth and non-maximum suppression method according to the water depth data of the seabed coral reef region acquired by the multi-beam sounding system and the gradient and curvature data obtained by calculation.
The specific identification steps are as follows:
(1) determining an initial seed point:
inputting water depth values h of observation points in the area to be identifiediCurvature value ciAnd a gradient value siIf s is satisfiedi≥s1Or | ci|≥c1Then the i point is the initial seed point, where s1、c1And the gradient threshold and the curvature threshold for determining the initial seed point are respectively set manually according to the gradient range and the curvature range of the area to be identified, wherein i is 1,2, …, n, and n is the number of observation points of the area to be identified.
In this embodiment, the processor is configured as intel (r) pentium (r) CPU G4560@3.50GHz, 3500Mhz, and has 2 cores and 4 logic processors, the submarine coral reef area is selected as the object for extracting the contour and detail of the geomorphologic unit, the size of the data body is 3312KB, the number of observation points is 103350, and the water depth change of the submarine coral reef area is shown in fig. 1. When getting s1=0.0444、c1=1.6602×10-4Then, the distribution of the obtained initial seed points is as shown in fig. 2, and in the MATLAB environment, 1.7810s are used for obtaining the initial seed points.
(2) And (3) performing seed region growth based on the water depth value:
if the i point is a non-seed point adjacent to the seed point and satisfies hi≥h2Or hi≤h1Then the i point is marked as a seed point, where i ═ 1,2, …, n, h1<h2,h1、h2And all the water depth thresholds are manually set according to the water depth range of the area to be identified.
In this embodiment, when h is taken1=-52.9140、h2The seed region growth results are shown in figure 3 at-31.3040.
(3) Growing the seed region based on the gradient value:
if the i point is a non-seed point adjacent to the seed point, constructing an m × m grid by taking the i point as the center, and if s isi≥szThen the i point is marked as a seed point, where szIs the median of the slope values of the seed points adjacent to the non-seed points in the m x m grid, m being an odd number greater than 1.
In this example, m is 5, and the result of performing the seed region growing based on the gradient value is shown in fig. 4.
(4) Fuzzy processing of seed regions:
constructing l multiplied by l grid by taking the i point as a center, if the i point is a seed point and n is satisfiedpIf the number is less than or equal to p, marking the point i as a non-seed point; if the i point is a non-seed point and n is satisfiedqQ is less than or equal to the sum of the values of the i point, the i point is marked as a seed point, wherein p and q are fuzzy parameters and are set manually, i is 1,2, …, n and n are set manuallyp、nqThe number of the seed points and the number of the non-seed points in the grid are respectively l multiplied by l, and l is an odd number larger than 1.
In this embodiment, l is 3, p is 3, and q is 3, and the result of the blurring process for the seed region is shown in fig. 5.
(5) Extracting the outline of the landform unit:
and extracting all seed points adjacent to the non-seed points to form the contour of the landform unit.
In the present embodiment, the outline of the extracted geomorphic unit is as shown in fig. 6. The processing was performed using a MATLAB environment, and it took 2.436 seconds to acquire the contour.
(6) Complementing details of the landform unit based on the slope value:
performing median filtering on the gradient value of the seed point in the step (4), if the gradient value meets the requirementThe i point is marked as a landform minutia; extracting all minutiae adjacent to non-minutiae for complementing the details of the geomorphic unit, wherein s2The grade threshold, for additional detail, is manually set,the median filtered slope value is obtained;
in the present embodiment, s2The details of the resulting relief units supplemented based on the slope values are shown in fig. 7, 0.0134. And MATLAB is applied for processing, and the time for acquiring the details of the landform units is 0.322 second.
(7) Complementing details of the geomorphic unit based on the curvature value:
and (4) aiming at the slope value and the curvature value of the seed point in the step (4), applying a non-maximum value inhibition method to obtain a local extreme value point in the contour of the landform unit, wherein the local extreme value point is used for supplementing the details of the landform unit.
In the present embodiment, details of the obtained relief cells are supplemented based on the non-maximum suppression method, and the result is shown in fig. 8. And (3) calculating by using MATLAB, wherein the time for obtaining the complementary details of the landform units is 0.338 seconds.
Claims (1)
1. A submarine geomorphologic unit outline and detail identification method based on region growing is characterized by comprising the following steps:
(1) determining an initial seed point: inputting water depth values h of observation points in the area to be identifiediCurvature value ciAnd a gradient value siIf s is satisfiedi≥s1Or | ci|≥c1Then the i point is the initial seed point, where s1、c1The gradient threshold and the curvature threshold are used for determining the initial seed point, the gradient range and the curvature range of the area to be identified are manually set, i is 1,2, …, n is the number of observation points of the area to be identified;
(2) and (3) performing seed region growth based on the water depth value: if the i point is a non-seed point adjacent to the seed point and satisfies hi≥h2Or hi≤h1Then the i point is marked as a seed point, where i ═ 1,2, …, n, h1<h2,h1、h2All the water depth thresholds are manually set according to the water depth range of the area to be identified;
(3) growing the seed region based on the gradient value: if the i point is a non-seed point adjacent to the seed point, constructing an m × m grid by taking the i point as the center, and if s isi≥szThen the i point is marked as a seed point, where szThe median of the gradient values of the seed points adjacent to the non-seed points in the m x m grid, wherein m is an odd number greater than 1;
(4) fuzzy processing of seed regions: constructing l × l grid with i point as center, if i point is seed point, andsatisfies npIf the number is less than or equal to p, marking the point i as a non-seed point; if the i point is a non-seed point and n is satisfiedqQ is less than or equal to the sum of the values of the i point, the i point is marked as a seed point, wherein p and q are fuzzy parameters and are set manually, i is 1,2, …, n and n are set manuallyp、nqThe number of the seed points and the number of the non-seed points in the grid are respectively l multiplied by l, wherein l is an odd number larger than 1;
(5) extracting the outline of the landform unit: extracting all seed points adjacent to the non-seed points to form the outline of the landform unit;
(6) complementing details of the landform unit based on the slope value: performing median filtering on the gradient value of the seed point in the step (4), if the gradient value meets the requirementThe i point is marked as a landform minutia; extracting all minutiae adjacent to non-minutiae for complementing the details of the geomorphic unit, wherein s2The grade threshold, for additional detail, is manually set,the median filtered slope value is obtained;
(7) complementing details of the geomorphic unit based on the curvature value: and (4) aiming at the slope value and the curvature value of the seed point in the step (4), applying a non-maximum value inhibition method to obtain a local extreme value point in the contour of the landform unit, wherein the local extreme value point is used for supplementing the details of the landform unit.
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
CN114782211A (en) * | 2022-05-16 | 2022-07-22 | 广州海洋地质调查局 | Method and system for acquiring information of sea and mountain distribution range |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107067012A (en) * | 2017-04-25 | 2017-08-18 | 中国科学院深海科学与工程研究所 | Submarine geomorphy cell edges intelligent identification Method based on image procossing |
CN108320291A (en) * | 2018-01-25 | 2018-07-24 | 中国科学院深海科学与工程研究所 | Submarine geomorphy boundary extraction method based on Otsu algorithm |
CN108460422A (en) * | 2018-03-15 | 2018-08-28 | 中国石油大学(华东) | Submarine geomorphy kind identification method based on depth distribution feature |
CN108520271A (en) * | 2018-03-15 | 2018-09-11 | 中国石油大学(华东) | Submarine geomorphy type sorter design method based on factorial analysis |
CN108629364A (en) * | 2018-03-15 | 2018-10-09 | 中国石油大学(华东) | Non-gaussian type submarine geomorphy kind identification method based on multi-fractal spectrum signature |
CN110674823A (en) * | 2019-09-26 | 2020-01-10 | 中国科学院声学研究所 | Sample library construction method based on automatic identification of deep sea large benthonic animals |
CN111047704A (en) * | 2019-10-29 | 2020-04-21 | 国家海洋信息中心 | Multi-beam sounding data gross error automatic clearing method for improving region growing algorithm |
-
2021
- 2021-01-08 CN CN202110022615.4A patent/CN112907615B/en active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107067012A (en) * | 2017-04-25 | 2017-08-18 | 中国科学院深海科学与工程研究所 | Submarine geomorphy cell edges intelligent identification Method based on image procossing |
CN108320291A (en) * | 2018-01-25 | 2018-07-24 | 中国科学院深海科学与工程研究所 | Submarine geomorphy boundary extraction method based on Otsu algorithm |
CN108460422A (en) * | 2018-03-15 | 2018-08-28 | 中国石油大学(华东) | Submarine geomorphy kind identification method based on depth distribution feature |
CN108520271A (en) * | 2018-03-15 | 2018-09-11 | 中国石油大学(华东) | Submarine geomorphy type sorter design method based on factorial analysis |
CN108629364A (en) * | 2018-03-15 | 2018-10-09 | 中国石油大学(华东) | Non-gaussian type submarine geomorphy kind identification method based on multi-fractal spectrum signature |
CN110674823A (en) * | 2019-09-26 | 2020-01-10 | 中国科学院声学研究所 | Sample library construction method based on automatic identification of deep sea large benthonic animals |
CN111047704A (en) * | 2019-10-29 | 2020-04-21 | 国家海洋信息中心 | Multi-beam sounding data gross error automatic clearing method for improving region growing algorithm |
Non-Patent Citations (2)
Title |
---|
HONG‑XIA MA 等: "Deep‑water depositional architecture and sedimentary evolution in the Rakhine Basin, northeast Bay of Bengal", 《PETROLEUM SCIENCE》 * |
郭婧: "基于图像分析与处理的海底地貌单元边界提取方法研究", 《中国优秀博硕士学位论文全文数据库(硕士) 基础科学辑》 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114782211A (en) * | 2022-05-16 | 2022-07-22 | 广州海洋地质调查局 | Method and system for acquiring information of sea and mountain distribution range |
CN114782211B (en) * | 2022-05-16 | 2023-04-28 | 广州海洋地质调查局 | Sea mountain distribution range information acquisition method and system |
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 |
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