CN101980295A - Method for detecting mainstream line of Yellow River based on skewness analysis - Google Patents
Method for detecting mainstream line of Yellow River based on skewness analysis Download PDFInfo
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Abstract
The invention discloses a method for detecting a mainstream line of the Yellow River based on skewness analysis, which is used for solving the technical problem of poor accuracy of the conventional method for detecting the mainstream line of the Yellow River. The technical scheme comprises the following steps of: roughly dividing the river according to spectral characteristics of a water area in a remote sensing image; extracting a coast line by edge detection and the like and determining a river region; segmenting the river according to the bending degree and direction of a river reach and river distribution between a south water edge and a north water edge; extracting mainstream points of the segmented river reaches by using spectral and physical characteristics of the mainstream line based on the skewness analysis in a transform domain; and connecting the extracted mainstream points by applying a multi-scale analysis method to form a final mainstream, thereby improving the accuracy of the method for detecting the mainstream line of the Yellow River.
Description
Technical field
The present invention relates to the main slide line detecting method in a kind of the Yellow River, particularly the main slide line detecting method of analyzing based on the degree of bias in the Yellow River.
Background technology
Being extracted in of the main slide line in the Yellow River all is by artificial drafting traditionally, and this method is not only time-consuming, effort, and is subjected to the influence of natural conditions such as weather easily, the more important thing is, is difficult in time grasp in flood season the main situation of change of slipping.In recent years, remote sensing image has been widely used in fields such as water body identification, river extraction, water quality detection, big flood detection, waters change-detection, and methods that these researchs are adopted are the characteristic that is shown on image according to water body basically and use some traditional sorting techniques to detect.The continuous development that remote sensing technology and remote sensing image are handled, making the utilization remote sensing image carry out the main slide line detection in the Yellow River becomes possibility.The problem that the main slide in river course shows in husky matter river course of the changeable alluvial of river gesture is outstanding, because less to the gesture research of river, heavily silt-carrying river river course in the world, so, also do not report at present about the applied research achievement of main slide in remote sensing image decipher river course and river gesture figure.Domestic, Preliminary Applications has only been carried out in river course main slide remote sensing image decipher at present in the Yellow River, yet, image data is handled and decipher relies on artificial visual, whole process is time-consuming, simultaneously, also has the difficulty that is difficult to accurately extract the main slide in river course according to the visual interpretation of remote sensing image.
Summary of the invention
In order to overcome the deficiency of existing the Yellow River main slide line detecting method poor accuracy, the invention provides a kind of main slide line detecting method of analyzing based on the degree of bias in the Yellow River, this method is carried out the river coarse segmentation according to the spectrum characteristic in waters in the remote sensing images; Utilize rim detection etc. to extract shoreline again, determine river region; Degree of crook and the distribution of the river between direction and the north and south flowage line according to the section are carried out segmentation to the river; To the river course after the segmentation, utilize spectrum, the physical characteristics of main slide line, extract the main point that slips based on the analysis of the transform domain degree of bias; Use multiscale analysis method, the main point that slips that extracts is connected, form the final main line that slips, can improve the Yellow River main slide line detecting method accuracy.
The technical solution adopted for the present invention to solve the technical problems: a kind of main slide line detecting method of analyzing based on the degree of bias in the Yellow River is characterized in comprising the steps:
(a) adopt supervised classification and matching process to carry out the river and cut apart, and sorted image is carried out the morphology processing, merge the zonule, eliminate less beach and bridge according to the features of shape of section, the Yellow River;
(b) adopt the Canny operator that the edge is detected, connection is followed the tracks of at detected edge, connect preliminary north and south, the Yellow River bank flowage line, remove and disturb line segment, obtain one group of useful line segment according to the neighborhood method; Again according to statistical property, the number that the point of being judged in every line segment belongs to a certain bank is greater than a certain threshold value, north and south bank image; The line segment of two sides, north and south is stored in the matrix respectively in certain sequence in order, obtain the complete flowage line of north and south bank;
(c) the dam bank that calculates the space by the curvature of windowing transforms to the curvature territory, and the curved place, position of pushing up of bend is represented in the maximum point position of gained curvature sequence; The transition and linkage point position between two continuous river bends is represented in minimum point position between two continuous threshold points, and the Yellow River is divided into typical section and atypia section;
(d) background outside the shielding river course, projective transformation based on scatter matrix between class is carried out in certain section:
Step1 obtains p * n observing matrix data X=[X
1, X
2..., X
n], wherein each is listed as X
iRepresent an observation sample vector, each row is represented an observation attribute; Two class sample data S
1, S
2
Step2 asks for the mean deviation form B of X: order
Then
Be about to the center that coordinate axis moves to former data; Ask S
1, S
2Sample average M
1, M
2
Step3 asks scatter matrix G between class
b, it is the positive semidefinite matrix of p * p, is defined as
G
b＝(M
1-M
2)·(M
1-M
2)
T；
Step4 asks for scatter matrix G between class
bEach eigenwert eval
iWith proper vector eig
i, wherein, 1≤i≤n;
Step5 select from big to small by eigenwert and with its to deserved proper vector, constitute transformation matrix
T=(eig
1, eig
2, eig
3, eig
m), wherein, m≤n;
Step6 generates the data set Y:Y=T in the new coordinate system
TX;
First component after the conversion is carried out degree of bias analysis:
In the formula, SD is a standard deviation; Skewness=0 explanation distributional pattern is identical with the normal state degree of bias; Skewness＞0, positively biased, peak value is on a left side; Skewness＜0, negative bias, peak value is on the right side;
In the histogram set of a certain section, get the position of the position of coefficient of skewness maximum point as main slide point on the current section;
(e) 1. with the multiple dimensioned geometric areas that is divided into of original data space; Out to out
The definition geometric areas is that (j, k l) are the parallelogram zone to R, and its horizontal width is w=2
-j, vertical thickness is t=2
J-J+rJ=1 wherein ... J; Definition k is the position of region R level; Defining variable L represents the inclined degree in zone; H is the upright position of region R;
Two-dimensional space is carried out two along transverse axis to be advanced to divide;
Be the out to out of dividing; The width of each longitudinal region of dividing is w=2
-j, j=1 wherein ... J; Any 2 connections on each border, the longitudinal region left and right sides are being done two parallel lines up and down with this line respectively as center line, obtain a parallelogram zone, and establishing thickness is t=2
J-J+rThis parallelogram zone is a data structure; Slope with s definition center line; Upright position with h definition center line left end point; Define two scale factors, δ
1=t/ (Vw), δ
2=t/U, the resolution of corresponding slope and upright position respectively; Data space is divided the set in parallelogram zone under each yardstick; The absolute value of the slope that defined range tilts is no more than S; With the parallelogram region representation is that (l i), wherein is respectively yardstick, 0≤k＜1/w to R for j, k
-1,-S δ
1≤ l≤S δ
1, 0≤i≤δ
2 -1-1; For R (j, k, l, i), the horizontal ordinate on left vertical limit is x=kw, the intersection point on center line and left vertical limit is y=i δ
2, slope s=l δ
1
Two parallelogram region R
1(j
1, k
1, l
1, i
1) and region R
2(j
2, k
2, l
2, i
2) between the condition of continuity:
● two zones under same yardstick, i.e. j
1=j
2
● two zones are adjacent, promptly | and k
1-k
2|=1;
● the intersection point distance in public vertical sideline and two zones is very near, promptly | and l
1+ i
1-l
2|＜v;
● the middle line slope in two zones is more or less the same, promptly | and l
1-l
2|＜u;
With two zone definitions that satisfy above-mentioned four conditions is the good zone of continuity;
2. add up counting in the geometric areas under each yardstick, and select significant zone according to threshold value;
3. under each yardstick, set up a non-directed graph G
j=(V
j, E
j), the geometric areas when Count＞N is as the vertex v ∈ V of figure
jIn the formula, Count is counting in the region R, and N is the definition threshold value in the region R;
If two geometric areas satisfy successional condition, just between these two summits, connect a limit e ∈ E
jThe quantity of the point in the computational geometry zone is is one by one accepted or rejected and the continuity relation is set up the limit according to threshold value N, obtains non-directed graph;
4. use the depth-first search algorithm in each non-directed graph of setting up, search longest path, the longest path that searches out are the main line that slips.
The invention has the beneficial effects as follows: the spectrum characteristic owing to according to waters in the remote sensing images, carry out the river coarse segmentation; Utilize rim detection etc. to extract shoreline again, determine river region; Degree of crook and the distribution of the river between direction and the north and south flowage line according to the section are carried out segmentation to the river; To the river course after the segmentation, utilize spectrum, the physical characteristics of main slide line, extract the main point that slips based on the analysis of the transform domain degree of bias; Use multiscale analysis method, the main point that slips that extracts is connected, form the final main line that slips, improved the Yellow River main slide line detecting method accuracy.
Below in conjunction with the drawings and specific embodiments the present invention is elaborated.
Description of drawings
Accompanying drawing is to the present invention is based on the Yellow River main slide line detecting method process flow diagram that the degree of bias is analyzed.
Embodiment
With reference to accompanying drawing.1, the river is cut apart.
Importing a secondary TM multispectral image, at first is the river coarse segmentation.Adopt spectral classification and matching technique, as: spectrum to flux matched, mahalanobis distance is cut apart and method such as Gauss Markov is carried out the spectrum picture classification, and according to the aftertreatment of classifying of the features of shape of section, the Yellow River, merge as: zone etc.Divide time-like to adopt supervised classification method, because image is subjected to the influence of factors such as weather, in the image there be than big-difference the spectrum of the upper reaches of the Yellow River and downstream water body, have very mistake if adopt single sample to carry out the classification of spectrum angle, therefore adopt two kinds of samples respectively image to be classified, then composograph.Because the influence of bridge and beach is arranged in the image, be not easy to the extraction of two sides, north and south flowage line, under the situation that requires to be fit to further study in edge definition image has been carried out the morphology processing, promptly image expands and corrodes operation, has eliminated less beach and bridge.
2, bank line extracts.Adopt the Canny operator that the edge is detected, and connection is followed the tracks of at detected edge, sort out the flowage line of north and south, river bank, for next step river segmentation is got ready.For the ease of extracting the two sides, north and south, need resulting edge line is carried out connection tracking; According to the neighborhood method, utilize a border following algorithm to arrive and obtain one group of continuous segments, remove some short interference line segments according to length, can remove beach, thereby obtain one group of useful line segment according to the style characteristic of beach simultaneously.According to the distribution characteristics of the Yellow River water body, observe it and have certain horizontal and vertical features, but algorithm for design carries out the judgement of north and south bank to every section line segment in view of the above.Only to need to consider to obtain the information of which bank under this whole section flowage line to the judgement that some representational point in every section carries out the north and south bank based on efficiency of algorithm.According to statistical property, the point of being judged in every section belongs to the number of a certain bank can judge that greater than a certain threshold value this section is to belong to southern bank or northern bank, otherwise, then belong to an other bank, thereby obtain north and south bank image.As required the line segment of two sides, north and south is stored in the matrix respectively in certain sequence in order, thereby obtain the complete flowage line in two sides, be beneficial to next step the main line drawing that slips.
3, river segmentation.With regard to the Yellow River main slide line problem the Yellow River is divided into typical section and atypia section.The typical case section is divided into straight little curved, the crooked and branch branch of a river three classes again.
The shape of each class section all has very big difference, and the main mode one of describing difference is to utilize tortuosity factor and curvature to describe the degree of crook and the direction of section, and the 2nd, distribute to determine whether to exist by the river between the flowage line of north and south and divide a branch of a river.Space relationship between section and the section also is one of main slide line foundation in the Yellow River of judging, therefore, it also is very important calculating for the relation of the space distribution between the section, the present invention is sections such as Curved Continuous is joined, bend is come over and pledged allegiance to the space distribution contextual definition, utilizes the continuous variation arrangement between the section to be distinguished from the space.Concrete segmentation method is as follows:
A) the dam bank that calculates the space by the curvature of windowing transforms to the curvature territory, and the curved place, position of pushing up of bend has been represented in the maximum point position of gained curvature sequence.
B) the transition and linkage point position between two continuous river bends is represented in the minimum point position between two continuous threshold points.
4, the main point that slips extracts.The main extracting method that slips based on the analysis of the transform domain degree of bias.
Background outside the shielding river course, projective transformation based on scatter matrix between class is carried out in certain section:
Step1 obtains p * n observing matrix data X=[X
1, X
2..., X
n], wherein each is listed as X
iRepresent an observation sample vector, each row is represented an observation attribute; Two class sample data S
1, S
2
Step2 asks for the mean deviation form B of X: order
Then
Be about to the center that coordinate axis moves to former data; Ask S
1, S
2Sample average M
1, M
2
Step3 asks scatter matrix G between class
b, it is the positive semidefinite matrix of p * p, is defined as G
b=(M
1-M
2) (M
1-M
2)
T
Step4 asks for scatter matrix G between class
bEach eigenwert eval
iWith proper vector eig
i, wherein, 1≤i≤n;
Step5 select from big to small by eigenwert and with its to deserved proper vector, constitute transformation matrix T=(eig
1, eig
2, eig
3, eig
m), wherein, m≤n;
Step6 generates the data set Y:Y=T in the new coordinate system
TX.First component after the conversion is carried out degree of bias analysis:
Wherein, SD is a standard deviation.Skewness=0 explanation distributional pattern is identical with the normal state degree of bias; Skewness＞0, positively biased, peak value is on a left side; Skewness＜0 is a negative bias, and peak value is on the right side.| Skewness| is big more, and the distributional pattern degrees of offset is big more.By discovering that the regional coefficient of skewness that contains main slide line is greater than the regional coefficient of skewness that does not contain main slide line.Therefore, in the histogram set of a certain section, get the position of the position of coefficient of skewness maximum point as main slide line on the current section.Promptly use 3 * 3 window calculation coefficients of skewness, and the ordinate of the point of the every row coefficient of skewness of recording image maximum, the ordinate of adjacent 3 row is average in will writing down then, as the main some position that slips.
5, the main point that slips connects.Above main the slide in the method that point extracts, the main point that slips that obtains need be connected and could obtain line segment.The present invention adopts the curve method of attachment of multiscale analysis:
At first will lead slide point data space and under each yardstick, divide, to each zone that marks off, counting in the statistical regions.Make up non-directed graph then.The longest path of last searching in the drawings.Concrete step is:
A) multiple dimensioned division: original data space is carried out multiple dimensioned division, be divided into geometric areas.At first definition has the multiple dimensioned geometric areas of different directions, yardstick, length.Out to out
The definition geometric areas is that (j, k l) are the parallelogram zone to R, and its horizontal width is w=2
-j, vertical thickness is t=2
J-J+rJ=1 wherein ... J.Width and thickness depend on the number of the size and the data point of yardstick.Definition k is the position of region R level.Defining variable L represents the inclined degree in zone in addition.H is the upright position of region R.
Then two-dimensional space being carried out two along transverse axis advances to divide.
Be the out to out of dividing.The width of each longitudinal region of dividing is w=2
-j, j=1 wherein ... J.Any 2 connections on each border, the longitudinal region left and right sides are being done two parallel lines up and down with this line respectively as center line, obtain a parallelogram zone, and establishing thickness is t=2
J-J+rThis parallelogram zone is our needed data structure, obtains more parallelogram zone by translation and rotation center line.Use the slope of s definition center line.And the upright position of h definition center line left end point.Define two scale factors in addition, δ
1=t/ (Vw), δ
2=t/U, the resolution of corresponding slope and upright position respectively.Data space is divided the set in parallelogram zone under each yardstick.The absolute value of the slope that defined range tilts is no more than S.With the parallelogram region representation is that (l i), wherein is respectively yardstick, 0≤k＜1/w to R for j, k
-1,-S δ
1≤ l≤S δ
1, 0≤i≤δ
2 -1-1.Therefore for R (j, k, l, i), the horizontal ordinate on left vertical limit is x=kw, the intersection point on center line and left vertical limit is y=i δ
2, slope s=l δ
1
Define two parallelogram region R
1(j
1, k
1, l
1, i
1) and region R
2(j
2, k
2, l
2, i
2) between the condition of continuity.
● two zones under same yardstick, i.e. j
1=j
2
● two zones are adjacent, promptly | and k
1-k
2|=1.
● the intersection point distance in public vertical sideline and two zones is very near, promptly | and l
1+ i
1-l
2|＜v.
● the middle line slope in two zones is more or less the same, promptly | and l
1-l
2|＜u.
With two zone definitions that satisfy above-mentioned four conditions is the good zone of continuity.
B) data statistics: add up counting in the geometric areas under each yardstick, and select significant zone according to threshold value.
C) structure non-directed graph: the continuity of utilizing marking area and zone to see makes up non-directed graph.
Under each yardstick, set up a non-directed graph G
j=(V
j, E
j), use the vertex v ∈ V of each above-mentioned geometric areas as figure
j, but be not all to set up a summit for each geometric areas.But to choose wherein satisfactoryly, be called the candidate region.Choosing of candidate region is quantity decision by data point in the zone.If counting in the region R is Count, definition threshold value N, when Count＞N, this zone is the candidate region.
If two geometric areas satisfy successional condition, just between these two summits, connect a limit e ∈ E
jThe quantity of the point in the computational geometry zone one by one, N accepts or rejects according to threshold value, sets up the limit according to the continuity relation then, thereby sets up a non-directed graph.
The search longest path: use the depth-first search algorithm in each non-directed graph of setting up, search longest path, the longest path that searches out are the main line that slips.
Claims (1)
1. the main slide line detecting method of analyzing based on the degree of bias in the Yellow River is characterized in that comprising the steps:
(a) adopt supervised classification and matching process to carry out the river and cut apart, and sorted image is carried out the morphology processing, merge the zonule, eliminate less beach and bridge according to the features of shape of section, the Yellow River;
(b) adopt the Canny operator that the edge is detected, connection is followed the tracks of at detected edge, connect preliminary north and south, the Yellow River bank flowage line, remove and disturb line segment, obtain one group of useful line segment according to the neighborhood method; Again according to statistical property, the number that the point of being judged in every line segment belongs to a certain bank is greater than a certain threshold value, north and south bank image; The line segment of two sides, north and south is stored in the matrix respectively in certain sequence in order, obtain the complete flowage line of north and south bank;
(c) the dam bank that calculates the space by the curvature of windowing transforms to the curvature territory, and the curved place, position of pushing up of bend is represented in the maximum point position of gained curvature sequence; The transition and linkage point position between two continuous river bends is represented in minimum point position between two continuous threshold points, and the Yellow River is divided into typical section and atypia section;
(d) background outside the shielding river course, projective transformation based on scatter matrix between class is carried out in certain section:
Step1 obtains p * n observing matrix data X=[X
1, X
2..., X
n], wherein each is listed as X
iRepresent an observation sample vector, each row is represented an observation attribute; Two class sample data S
1, S
2
Step2 asks for the mean deviation form B of X: order
Then
Be about to the center that coordinate axis moves to former data; Ask S
1, S
2Sample average M
1, M
2
Step3 asks scatter matrix G between class
b, it is the positive semidefinite matrix of p * p, is defined as
G
b＝(M
1-M
2)·(M
1-M
2)
T；
Step4 asks for scatter matrix G between class
bEach eigenwert eval
iWith proper vector eig
i, wherein, 1≤i≤n;
Step5 select from big to small by eigenwert and with its to deserved proper vector, constitute transformation matrix
T=(eig
1, eig
2, eig
3, eig
m), wherein, m≤n;
Step6 generates the data set Y:Y=T in the new coordinate system
TX;
First component after the conversion is carried out degree of bias analysis:
In the formula, SD is a standard deviation; Skewness=0 explanation distributional pattern is identical with the normal state degree of bias; Skewness＞0, positively biased, peak value is on a left side; Skewness＜0, negative bias, peak value is on the right side;
In the histogram set of a certain section, get the position of the position of coefficient of skewness maximum point as main slide point on the current section;
(e) 1. with the multiple dimensioned geometric areas that is divided into of original data space; Out to out
The definition geometric areas is that (j, k l) are the parallelogram zone to R, and its horizontal width is w=2
-j, vertical thickness is t=2
J-J+rJ=1 wherein ... J; Definition k is the position of region R level; Defining variable L represents the inclined degree in zone; H is the upright position of region R;
Two-dimensional space is carried out two along transverse axis to be advanced to divide;
Be the out to out of dividing; The width of each longitudinal region of dividing is w=2
-j, j=1 wherein ... J; Any 2 connections on each border, the longitudinal region left and right sides are being done two parallel lines up and down with this line respectively as center line, obtain a parallelogram zone, and establishing thickness is t=2
J-J+rThis parallelogram zone is a data structure; Slope with s definition center line; Upright position with h definition center line left end point; Define two scale factors, δ
1=t/ (Vw), δ
2=t/U, the resolution of corresponding slope and upright position respectively; Data space is divided the set in parallelogram zone under each yardstick; The absolute value of the slope that defined range tilts is no more than S; With the parallelogram region representation is that (l i), wherein is respectively yardstick, 0≤k＜1/w to R for j, k
-1,-S δ
1≤ l≤S δ
1, 0≤i≤δ
2 -1-1; For R (j, k, l, i), the horizontal ordinate on left vertical limit is x=kw, the intersection point on center line and left vertical limit is y=i δ
2, slope s=l δ
1
Two parallelogram region R
1(j
1, k
1, l
1, i
1) and region R
2(j
2, k
2, l
2, i
2) between the condition of continuity:
● two zones under same yardstick, i.e. j
1=j
2
● two zones are adjacent, promptly | and k
1-k
2|=1;
● the intersection point distance in public vertical sideline and two zones is very near, promptly | and l
1+ i
1-l
2|＜v;
● the middle line slope in two zones is more or less the same, promptly | and l
1-l
2|＜u;
With two zone definitions that satisfy above-mentioned four conditions is the good zone of continuity;
2. add up counting in the geometric areas under each yardstick, and select significant zone according to threshold value;
3. under each yardstick, set up a non-directed graph G
j=(V
j, E
j), the geometric areas when Count＞N is as the vertex v ∈ V of figure
jIn the formula, Count is counting in the region R, and N is the definition threshold value in the region R;
If two geometric areas satisfy successional condition, just between these two summits, connect a limit e ∈ E
jThe quantity of the point in the computational geometry zone is is one by one accepted or rejected and the continuity relation is set up the limit according to threshold value N, obtains non-directed graph;
4. use the depth-first search algorithm in each non-directed graph of setting up, search longest path, the longest path that searches out are the main line that slips.
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| CN103123723A (en) * | 2013-01-23 | 2013-05-29 | 中国人民解放军信息工程大学 | Flowage line extracting method based on Canny edge detection and active contour model |
| CN105719300A (en) * | 2016-01-22 | 2016-06-29 | 黄河水利委员会信息中心 | Riverway main stream line detection method based on SNE manifold learning |
| CN105913430A (en) * | 2016-04-12 | 2016-08-31 | 西北工业大学 | Cooperated extracting method for main ice information of The Yellow River based on multispectral remote sensing image |
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| CN103123723A (en) * | 2013-01-23 | 2013-05-29 | 中国人民解放军信息工程大学 | Flowage line extracting method based on Canny edge detection and active contour model |
| CN105719300A (en) * | 2016-01-22 | 2016-06-29 | 黄河水利委员会信息中心 | Riverway main stream line detection method based on SNE manifold learning |
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| CN105913430A (en) * | 2016-04-12 | 2016-08-31 | 西北工业大学 | Cooperated extracting method for main ice information of The Yellow River based on multispectral remote sensing image |
| CN105913430B (en) * | 2016-04-12 | 2019-01-15 | 西北工业大学 | Yellow River Main based on multi-spectral remote sensing image slips information synergism extracting method |
| CN108319902A (en) * | 2018-01-19 | 2018-07-24 | 中国科学院南京土壤研究所 | A kind of meander extraction method |
| CN108319902B (en) * | 2018-01-19 | 2019-02-22 | 中国科学院南京土壤研究所 | A kind of meander extraction method |
| CN109902682A (en) * | 2019-03-06 | 2019-06-18 | 太原理工大学 | A kind of mammary gland x line image detection method based on residual error convolutional neural networks |
| CN113177183A (en) * | 2021-06-29 | 2021-07-27 | 广东海洋大学 | Seawater pollution monitoring and early warning method and system based on ocean remote sensing image |
| CN113177183B (en) * | 2021-06-29 | 2021-09-14 | 广东海洋大学 | Seawater pollution monitoring and early warning method and system based on ocean remote sensing image |
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