CN105261024A - Remote sensing image complex water body boundary extraction method based on improved T-Snake model - Google Patents

Abstract

The invention relates to the field of water body remote sensing technology, and provides a remote sensing image complex water body boundary extraction method based on an improved T-Snake model. The method comprises the steps of setting an initial contour and an initial grid size; extracting a water body boundary by using an orthogonal T-Snake model and obtaining a contour curve; and determining whether the square grid width is 1 pixel, if so, ending the process and taking the contour curve obtained at the time as a final water body boundary, otherwise, taking the obtained contour curve after node interpolation operation as a new initial contour and halving the grid size, and then returning to the previous step. The technical scheme provided by the invention can not only reduce the operation time of the T-Snake model, but can also improve the accuracy of remote sensing image complex water body boundary extraction.

Description

Based on the remote sensing image Complex water body boundary extraction method improving T-Snake model

Technical field

The present invention relates to water body remote sensing technology field, particularly a kind of remote sensing image Complex water body boundary extraction method based on improving T-Snake model.

Background technology

The major technique extracting water boundary from remote sensing image is rim detection.Document 1 (Meng Lingkui, Lv Qifei, the orthogonal T-Snake model of improvement of Complex water body Boundary Extraction, mapping journal, 2015,44 (6)) provide one and utilize topology adaptation snake model (TopologyadaptiveSnake, T-Snake) method on remote sensing image Complex water body border is extracted, this model is the improvement to classical Snake model, its basic thought is in original image, build a series of square net, and restricted model evolution curve can only move along mesh lines direction and curve node can only be positioned at grid vertex place.The key step of T-Snake model extraction water boundary is taked to be: the inner initial point of first artificial given water body, and according to square net large little structure water body initial profile curve; Then an energy function is defined, to control distortion and the motion of initial given closed contour curve according to the image feature of water body and contour curve; Finally ask for energy function minimum value, obtain contour curve now, as water boundary.

When asking for energy function minimum value, what document 1 was taked is greedy algorithm, and first carry out node and split the energy value calculating and judgement of then carrying out node one by one, key step is:

(1) travel through the sequence node of current curves S, to each node, be split as four points of four direction up and down, these four point coordinate equidirectionals are different; Then remove the point that those point to curvilinear inner, remaining several points are inserted in sequence node in order;

(2) sequence node after traverse node fractionation, for every bit, calculates the target location that it will move to; Then calculate the local energy functional value of this point of mobile front and back respectively, and judge: if energy function value is less after mobile, then this point moves to reposition, otherwise remains unchanged;

(3) whether the total energy function value calculating whole contour curve is equal with before this curve deformation, if so, then illustrate that contour curve arrives coastal waters, so just exports the sequence node of contour curve, otherwise, then the iterative computation of carrying out a new round is needed.The contour curve finally obtained is exactly the closed contour in extracted waters.

In the T-Snake model of document 1, the size dividing the grid of image is large especially on the impact of Water recognition precision.Be in particular in: mesh width arranges excessive, the feature being less than a sizing grid can be made to be left in the basket, thus cause the decline of precision; And mesh width is when arranging too small, precision can increase, but can cause the increase of iterations, reduces the efficiency of Water recognition.

Summary of the invention

[technical matters that will solve]

The object of this invention is to provide a kind of improvement T-Snake model based on variable grid towards Complex water body Boundary Extraction, to reduce the T-Snake model calculation time, the precision of remote sensing image Complex water body Boundary Extraction can be improved again simultaneously.

[technical scheme]

The present invention is achieved by the following technical solutions.

The present invention relates to a kind of in the remote sensing image Complex water body boundary extraction method improving T-Snake model, comprise step:

The square net width r of A, the orthogonal T-Snake model of initialization, builds the initial profile curve of water body, and each node of record initial profile curve is V (x, y, O), wherein V (x, y) be node coordinate, O is node direction, calculates the energy of initial profile curve;

B, adopt orthogonal T-Snake model to extract border, waters in remote sensing image, obtain the contour curve after distortion;

C, judge whether square net width is 1, if so, then current contour curve is exited this method flow process as final water boundary, otherwise perform step D;

D, node interpolating operations is carried out to the contour curve that step B obtains, obtain a new contour curve;

E, current outline curve is updated to the contour curve that step D obtains, and k mesh width being set to current grid width is doubly, then returns and performs step B, wherein 0<k<1,

Described step B specifically comprises step:

B1, the width r obtaining current square net and contour curve, arrange gray average discrepancy threshold T, neighbor domain of node △ x, initialization iterations t=1;

B2, initialization global energy E _snake ^(t)=0;

First node P of B3, acquisition contour curve;

The local energy E (P) of B4, computing node P, obtain node P=(x, y) target gridding summit P'(x', y') coordinate;

B5, calculate gray average μ (P), μ (P') in P, P' neighborhood △ x in respective original image respectively;

B6, judge whether to meet | μ (P)-μ (P') | <T, if met, performs step B7, otherwise then performs step B8;

The local energy functional value E (P') of B7, calculating P', if E (P) >E (P'), then makes P=P' and performs step B8, otherwise then directly performing step B8;

The global energy functional value E of B8, renewal contour curve _snake ^(t)=E _snake ^(t)+ E (P);

B9, judge that whether P is last node of contour curve, if it is export E _snake ^(t)and perform step B10, otherwise the next node pointed by P is set to P and returns step B4;

B10: judge whether to meet E _snake ^(t)>=E _snake ^(t-1)if meet, using the contour curve under current square net as water boundary, otherwise then iterations t added 1 and return step B2.

As one preferred embodiment, the method building the initial profile curve of water body in described steps A is: the inner any point (x in selected waters ₀, y ₀), build the initial profile in this waters so that the four direction up and down of this point to be connected successively at a distance of four pixel clock-wise order for r.

As another preferred embodiment, the value of described k is 1/2.

As another preferred embodiment, described energy function is defined as: $E\left(i\right)=\mathrm{\&eta;}\left|V_{i-1}-2V_i+V_{i+1}\right|+\mathrm{\&gamma;}\left|V_i-C\right|e^{-\mathrm{\&epsiv;}(\mathrm{\&sigma;}+\mathrm{\&delta;})}+\mathrm{\&lambda;}|\&dtri;I\left(V_i\right)|,$ Wherein, η, γ, λ are coefficient, and C is the geometric center of current Snake curve, σ and δ is gray standard deviation and the extreme difference of neighbor domain of node respectively, node V _ithe image gradient at place.

[beneficial effect]

The technical scheme that the present invention proposes has following beneficial effect:

The present invention is based on T-Snake model and it is improved, particularly, first the present invention adopts macrolattice to realize the preliminary extraction of water boundary, then contour curve constantly approaching water boundary is realized by progressively reducing size of mesh opening, on the one hand, because early stage size of mesh opening larger thus minimizing the model calculation time, on the other hand, size of mesh opening due to the later stage is less thus can capture minimum boundary characteristic (the recessed region that such as throat width is less), and therefore the present invention can improve again the precision of Boundary Extraction.In a word, relative to document 1, the improvement T-Snake model based on variable grid towards Complex water body Boundary Extraction provided by the invention, not only can reduce the T-Snake model calculation time, can improve again the precision of remote sensing image Complex water body Boundary Extraction simultaneously.

Accompanying drawing explanation

The process flow diagram of the remote sensing image Complex water body boundary extraction method in improvement T-Snake model that Fig. 1 provides for embodiments of the invention one;

The current grid bottom profiled curve update method process flow diagram that Fig. 2 provides for embodiments of the invention one.

Embodiment

For making the object, technical solutions and advantages of the present invention clearly, carry out clear, complete description by the specific embodiment of the present invention below.

Embodiment one

Fig. 1 for embodiments of the invention one provide in the process flow diagram of remote sensing image Complex water body boundary extraction method improving T-Snake model, as shown in Figure 1, the method comprising the steps of S1, to step S5, is described in detail above-mentioned steps below respectively.

Step S1: initial profile and initial mesh size are set.

In step S1, the square net width r (r=2 of the orthogonal T-Snake model of initialization ⁿn>=0), build the initial profile curve of remote sensing image water body to be extracted, each node of record initial profile curve is V (x, y, O), wherein V (x, y) is node coordinate, O is node direction, calculate the energy of initial profile curve, need to illustrate, the value of r can be arranged according to actual needs.

Particularly, the method for the initial profile curve of step S1 structure water body is: the inner any point (x in selected waters ₀, y ₀), build the initial profile in this waters so that the four direction up and down of this point to be connected successively at a distance of four pixel clock-wise order for r, namely initial profile is of a size of 2 ⁿ× 2 ⁿindividual pixel.In addition, the present embodiment definition energy function is:

$E\left(i\right)=\mathrm{\&eta;}\left|V_{i-1}-2V_i+V_{i+1}\right|+\mathrm{\&gamma;}\left|V_i-C\right|e^{-\mathrm{\&epsiv;}(\mathrm{\&sigma;}+\mathrm{\&delta;})}+\mathrm{\&lambda;}|\&dtri;I\left(V_i\right)|,$

Wherein, η, γ, λ are coefficient, and C is the geometric center of current Snake curve, σ and δ is gray standard deviation and the extreme difference of neighbor domain of node respectively, node V _ithe image gradient at place.The present embodiment calculates the energy of contour curve by this energy function.

Step S2: adopt orthogonal T-Snake model extraction water boundary.

In step S2, adopt orthogonal T-Snake model to extract border, waters in remote sensing image, obtain the contour curve after distortion.Compared with document 1, the embodiment of the present invention still adopts greedy algorithm to carry out Water recognition, namely in each iterative process, first carry out node fractionation and carry out node motion again, but the embodiment of the present invention is improved node moving process during the t time iteration, to ensure that, when size of mesh opening is larger, contour curve can not cross over water boundary.The settling mode taked is: in iterative process each time, for any one node P on contour curve, decision its whether before this next grid vertex P' moved to, carry out a judgement: if the neighborhood gray average of P' and P is more or less the same on the original image, then illustrate that P' is still water body internal point, otherwise then think that P' spans water boundary.Only have when P' is still water body internal point, just can calculate the local energy function of P', and make comparisons with the local energy function of P, to determine whether P point should move.Particularly, step S2 comprises step S201 to step S210:

Step S201: the width r and the contour curve that obtain current square net, initialization iteration control parameter.

In step S201, obtain width r and the contour curve of current square net, gray average discrepancy threshold T, neighbor domain of node △ x are set, initialization iterations t=1, need to illustrate, the value of gray average discrepancy threshold T, neighbor domain of node △ x can be arranged according to actual needs.

Step S202: initialization global energy E _snake ^(t)=0.

Step S203: first the node P obtaining contour curve.

Step S204: the local energy of computing node P, obtains the target gridding apex coordinate P' of node P.

In step S204, the local energy E (P) of computing node P, obtain node P=(x, y) target gridding summit P'(x', y') coordinate.

Step S205: calculate the gray average in P, P' neighborhood △ x in respective original image respectively.

In step S205, calculate the gray average μ (P) in the neighborhood △ x of P in its original image, calculate the gray average μ (P') in the neighborhood △ x of P' in its original image.

Step S206: judge whether to meet | μ (P)-μ (P') | <T, if met, performs step S207, otherwise then performs step S208.

Step S207: the local energy functional value E (P') calculating P', if E (P) >E (P'), then make P=P', perform step S208 by P' assignment to P, if E (P)≤E (P'), directly perform step S208.

Step S208: the global energy functional value upgrading contour curve.

In step S208, upgrade the global energy functional value E of contour curve by following formula _snake ^(t)=E _snake ^(t)+ E (P).

Step S209: judge that whether P is last node of contour curve, if it is export E _snake ^(t)and perform step B210, otherwise the next node pointed by P is set to P and returns step S204.

In step S209, first need to judge that whether P is last node of current outline curve, if P is last node of current outline curve, then export the global energy functional value E of current outline curve _snake ^(t)and perform step S210

Step S210: judge whether to meet E _snake ^(t)>=E _snake ^(t-1)if met, the contour curve under current square net is performed step S3 as water boundary, otherwise then iterations t is added 1 and return step S202.

Step S3: judge whether square net width is 1, if so, then exits current outline curve from this method flow process as final water boundary, otherwise performs step S4.

In step S3, first judge whether square net width is 1 pixel, is if it is out of shape end, current outline curve is exited this method flow process as final water boundary, otherwise perform step S4.

Step S4: node interpolating operations is carried out to the contour curve got.

In step S4, node interpolating operations is carried out to the contour curve that step S2 obtains, obtain a new contour curve.

Step S5: upgrade contour curve and sizing grid, return step S2.

In step S5, mesh width as current outline curve, and is set to 1/2 times of current grid width by the contour curve obtained using step S4, then returns and performs step S2.

L-G simulation test

Be 1407 × 1835 pixels for a width image size, spatial resolution is the same water body target in the GF1 near infrared image of 16 meters, test is at Intel Duo i5, CPU frequency 2.27GHz, on the computing machine of internal memory 4GB, Python programming realization is used under eclipse+pydev translation and compiling environment, found that: embodiment one extract the correctness of water boundary and integrity degree is respectively 99.8% and 99.6%, the correctness of water boundary is extracted and integrity degree is respectively 98.5% and 98.3% without the T-Snake model improved, and the former is consuming time is only 21.4 seconds, the latter is consuming time is 116.7 seconds.From the result of test, embodiments of the invention one can ensure that the precision of Complex water body Boundary Extraction can improve model calculation efficiency again.

As can be seen from above embodiment and l-G simulation test thereof, the embodiment of the present invention is based on T-Snake model and improve it, particularly, first the embodiment of the present invention adopts macrolattice to realize the preliminary extraction of water boundary, then contour curve constantly approaching water boundary is realized by progressively reducing size of mesh opening, on the one hand, because early stage size of mesh opening larger thus minimizing the model calculation time, on the other hand, size of mesh opening due to the later stage is less thus can capture minimum boundary characteristic (the recessed region that such as throat width is less), therefore the present invention can improve again the precision of Boundary Extraction.In a word, relative to document 1, the improvement T-Snake model based on variable grid towards Complex water body Boundary Extraction that the embodiment of the present invention provides, not only can reduce the T-Snake model calculation time, can improve again the precision of remote sensing image Complex water body Boundary Extraction simultaneously.

Need to illustrate, the embodiment of foregoing description is a part of embodiment of the present invention, instead of whole embodiment, neither limitation of the present invention.Based on embodiments of the invention, those of ordinary skill in the art, not paying the every other embodiment obtained under creative work prerequisite, belong to protection scope of the present invention.

Claims (4)

1., based on the remote sensing image Complex water body boundary extraction method improving T-Snake model, it is characterized in that comprising step:

The square net width r of A, the orthogonal T-Snake model of initialization, build the initial profile curve of remote sensing image water body, the each node of record initial profile curve is V (x, y, O), wherein V (x, y) is node coordinate, O is node direction, calculates the energy of initial profile curve;

B, adopt orthogonal T-Snake model to extract border, waters in remote sensing image, obtain the contour curve after distortion;

C, judge whether square net width r is 1, if so, then current contour curve is exited this method flow process as final water boundary, otherwise perform step D;

D, node interpolating operations is carried out to the contour curve that step B obtains, obtain a new contour curve;

E, current outline curve is updated to the contour curve that step D obtains, and k mesh width being set to current grid width is doubly, then returns and performs step B, wherein 0<k<1,

Described step B specifically comprises step:

B1, the width r obtaining current square net and contour curve, arrange gray average discrepancy threshold T, neighbor domain of node △ x, initialization iterations t=1;

B2, initialization global energy E _snake ^(t)=0;

First node P of B3, acquisition contour curve;

The local energy E (P) of B4, computing node P, obtain node P=(x, y) target gridding summit P'(x', y') coordinate;

B5, calculate gray average μ (P), μ (P') in P, P' neighborhood △ x in respective original image respectively;

B6, judge whether to meet | μ (P)-μ (P') | <T, if met, performs step B7, otherwise then performs step B8;

The local energy functional value E (P') of B7, calculating P', if E (P) >E (P'), then makes P=P' and performs step B8, otherwise then directly performing step B8;

The global energy functional value E of B8, renewal contour curve _snake ^(t)=E _snake ^(t)+ E (P);

B9, judge that whether P is last node of contour curve, if it is export E _snake ^(t)and perform step B10, otherwise the next node pointed by P is set to P and returns step B4;

B10: judge whether to meet E _snake ^(t)>=E _snake ^(t-1)if meet, using the contour curve under current square net as water boundary, otherwise then iterations t added 1 and return step B2.

2. the remote sensing image Complex water body boundary extraction method based on improving T-Snake model according to claim 1, is characterized in that the method for the initial profile curve building water body in described steps A is: the inner any point (x in selected waters ₀, y ₀), build the initial profile in this waters so that the four direction up and down of this point to be connected successively at a distance of four pixel clock-wise order for r.

3. the remote sensing image Complex water body boundary extraction method based on improving T-Snake model according to claim 1, is characterized in that the value of described k is 1/2.

4. the remote sensing image Complex water body boundary extraction method based on improving T-Snake model according to claim 1, is characterized in that described energy function is defined as: $E\left(i\right)=\mathrm{\&eta;}\left|V_{i-1}-2V_i+V_{i+1}\right|+\mathrm{\&gamma;}\left|V_i-C\right|e^{-\mathrm{\&epsiv;}(\mathrm{\&sigma;}+\mathrm{\&delta;})}+\mathrm{\&lambda;}|\&dtri;I\left(V_i\right)|,$ Wherein, η, γ, λ are coefficient, and C is the geometric center of current outline curve, σ and δ is gray standard deviation and the extreme difference of neighbor domain of node respectively, node V _ithe image gradient at place.