CN112907615B - 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 growing. 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 growing 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 geomorphic unit outline and detail identification method based on region growing.
Background
Submarine geomorphic 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 submarine landform measurement, submarine measuring point information including position, altitude, azimuth and the like can be acquired. 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 and to a transducer to the seabed and combining multiple parameters such as current sound velocity, sea level and 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 means of the altitude characteristics.
Although some automatic extraction methods for the edges of the submarine geomorphic unit are available at present, such methods are mainly used for extracting specific types of edges such as ridge lines, valley lines, coastlines and the like, and it is difficult to extract the whole edges or all details of the submarine geomorphic unit.
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 identified i Curvature value c i And a gradient value s i If satisfy s i ≥s k Or | c i |≥c k Then the i point is the initial seed point, where s k 、c k The gradient threshold and the curvature threshold which are used 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, and 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 h i ≥h g Or h i ≤h k Then the i point is marked as a seed point, where i ═ 1,2, …, n, h k ＜h g ，h k 、h g All the water depth thresholds are manually set according to the water depth range of the area to be identified;
(3) seed region growing based on the gradient values:
if the i point is a non-seed point adjacent to the seed point, constructing an m × m grid by using the i point as a center, and if s is i ≥s z Then the i point is marked as a seed point, where s z The 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 satisfied p If the value 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 satisfied q Q 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 manually p 、n q The 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 requirement
The i point is marked as a landform minutia; extracting all minutiae adjacent to non-minutiae for complementing the details of the geomorphic unit, wherein s t The 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 (5) aiming at 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 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 the result of seed region growing based on a gradient value;
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 the water depth value h of each observation point in the area to be identified i Curvature value c i And a gradient value s i If satisfy s i ≥s k Or | c i |≥c k Then the i point is the initial seed point, where s k 、c k And (3) respectively setting a gradient threshold and a curvature threshold for determining the initial seed point 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 geomorphic 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 s k ＝0.0444、c k ＝1.6602×10 -4 Then, the distribution of the obtained initial seed points is as shown in FIG. 2, and under the MATLAB environment, the initial seed points are obtainedAt time 1.7810 s.
(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 h i ≥h g Or h i ≤h k Then the i point is marked as a seed point, where i ═ 1,2, …, n, h k ＜h g ，h k 、h g And 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 taken k ＝-52.9140、h g The 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 is i ≥s z Then the i point is marked as a seed point, where s z Is the median of the slope values of the seed points adjacent to the non-seed points in the m x m grid, and m is 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 x l grid by taking the i point as the center, if the i point is the seed point and satisfies n p If 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 satisfied q Q 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 manually p 、n q The number of the seed points and the number of the non-seed points in the grid are respectively 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 this embodiment, the contour of the extracted landform 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 landform units based on the slope values:
performing median filtering on the gradient value of the seed point in the step (4), if the gradient value meets the requirement
The i point is marked as a landform minutia; extracting all minutiae adjacent to non-minutiae for complementing the details of the geomorphic unit, wherein s t For grade thresholds, for additional details, manually set,
the median filtered slope value is obtained;
in this embodiment, s t The 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 seconds.
(7) Complementing the details of the landform units based on the curvature values:
and (5) aiming at 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 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 the water depth value h of each observation point in the area to be identified i Curvature value c i And a gradient value s i If s is satisfied i ≥s k Or | c i |≥c k Then the i point is the initial seed point, where s k 、c k Respectively, a gradient threshold and a curvature threshold for determining an initial seed point, according to the point to be identifiedThe gradient range and the curvature range of each area 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 h i ≥h g Or h i ≤h k Then the i point is marked as a seed point, where i ═ 1,2, …, n, h k ＜h g ，h k 、h g All 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 is i ≥s z Then the i point is marked as a seed point, where s z The 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 x l grid by taking the i point as the center, if the i point is the seed point and satisfies n p If the value 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 satisfied q Q 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 manually p 、n q The 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 contour of the landform unit;
(6) complementing details of landform units based on the slope values: performing median filtering on the gradient value of the seed point in the step (4), if the gradient value meets the requirement
The i point is marked as a landform minutia; extracting all minutiae adjacent to non-minutiae for complementing the details of the geomorphic unit, wherein s t The 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 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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