CN102426700A - Level set SAR image segmentation method based on local and global area information - Google Patents

Abstract

The invention discloses a level set SAR (Synthetic Aperture Radar) image segmentation method based on local and global area information, which mainly solves the problem that the existing level set method is influenced by speckle and cannot segment the SAR images with uneven gray. The method comprises the following implementation steps of: firstly, initializing a level set function phi, and segmenting the SAR image into an internal area omega 1 and an external area omega 2; secondly, convolving intensity information of the internal area and the external area of the image through a Gaussian Kernel Function, taking the convolved information as local area information, and forming an energy term based on the local area; then, solving intensity mean values c1 and c2 and probability densities p1 and p2 of the internal area and the external area, and forming the energy term of the global area; and finally, adding a bound term L (phi) of level set length and a penalty P (phi) which avoids renewed initialization, forming a total energy function ESAR, solving a gradient sinking equation through a variation method, and updating the level set phi. The obtained new segmented area and the experimental result show that the segmentation method provided by the invention can get more ideal segmentation effects, and the method can be used for SAR image segmentation and target identification.

Description

Level set SAR image partition method based on local and global area information

Technical field

The invention belongs to technical field of image processing; Relate to the application in SAR image segmentation field; Specifically a kind of level set SAR image partition method that combines based on local and global area information can be used for the cutting apart of SAR image, rim detection and Target Recognition.

Background technology

Synthetic-aperture radar SAR is the active radar of a kind of high resolving power, but has advantages such as round-the-clock, round-the-clock, the high side-looking imaging of resolution, can be applicable to numerous areas such as military affairs, agricultural, navigation, geographical supervision.The SAR image is applied in object detection field widely, and the SAR image segmentation then is the important step from the Flame Image Process to the graphical analysis, is target classification and base of recognition.Because SAR is a kind of coherence imaging system, the SAR image is to the electromagnetic scattering characteristic of target and the reaction of architectural characteristic in itself, and its imaging effect depends on radar parameter and region electromagnetic parameter to a great extent.Because the singularity of SAR imaging system; Make the information representation mode and the optical imagery of SAR image that very big-difference arranged; And can receive the influence of many geometric properties such as coherent speckle noise and shade, just because of these factors make the dividing method that is applicable to optical imagery no longer suitable to the SAR image.

Conventional on the whole SAR image partition method is almost still cut apart the SAR image with traditional parted pattern; As some based on the cutting apart of threshold value, based on the edge cut apart and based on dividing method of region growing etc.; But a lot of dividing methods wherein receive the influence of coherent speckle noise in the SAR image easily, so these class methods all need to suppress coherent speckle noise through pre-service earlier.These preprocess methods have lost a lot of edges and target information inevitably when suppressing coherent speckle noise, influenced segmentation effect.

Level set image segmentation method is one of important images disposal route.The advantage of Level Set Method is to adapt to changes of topology structure, and algorithm stability is higher.Utilize Level Set Method research SAR image segmentation problem; Through setting up suitable energy functional; Can in the definition of energy functional, introduce image area information; Do not needing to obtain segmentation result more accurately for the SAR image that influenced by coherent speckle noise under the pretreated situation of coherent spot.

Can be divided into based on regional area with based on the method for global area based on the Level Set Method in zone.Utilize the half-tone information tectonic energy flow function of the regional area of image based on the method for regional area; On medical image and natural image, obtained better segmentation effect; The CV model that Chan and Vese propose is a kind of level set dividing method based on the zone; As a kind of zone level collection parted pattern that can effectively improve curve evolvement topology adaptive ability, it has utilized the global area information of image.But equally also there is weak point in it; The internal energy term of this model just guarantees zero level collection line smoothing; And do not consider level set function itself intrinsic inwardness; Also need reinitialize in some applications,, guarantee the stability of numerical solution so that it is near the symbolic distance function to level set function.

Though the level set dividing method has obtained great success in optics and medical image segmentation; But in SAR image segmentation field; Research also fewer, the scholar who studies in the world at present based on the Level Set Method of area information has people such as Chunming Li and Ayed, and Chunming Li lays particular stress on and studies the application of local two-value match LBF method in medical image segmentation; Because the SAR picture noise is a multiplicative noise; Be different from the additive noise in medical image and the natural image, the regional area method is vulnerable to the influence of the property taken advantage of coherent speckle noise, so the LBF method is not suitable for the SAR image.The Level Set Method of people such as Ayed research is easy to generate more complicated SAR image cuts apart phenomenon with Louing, is not ideal very to the segmentation result of complex region SAR image.

Summary of the invention

The objective of the invention is to deficiency to above-mentioned prior art; A kind of level set SAR image partition method that combines based on local and global area information is provided; To realize accurately cutting apart to ill-defined SAR image-region; Improve the positioning performance at edge, thereby improve the quality of SAR image segmentation.

Realize that the object of the invention technical thought is: according to the half-tone information of SAR image; Use a local window function gaussian kernel function strength information of exterior domain in the image is carried out convolution; As local region information, according to the statistical property of SAR image, calculate the estimated value of dividing the probability density in two zones that obtain by level set again; With the logarithm of each estimated value as global area information; Bound term that combines level set length at last and the penalty term of avoiding reinitializing combine this four partial information and carry out the modeling of energy function, thereby reaches desirable segmentation result.Its concrete implementation procedure comprises as follows:

(1) level set function φ is initialized to the symbolic distance functional form, positive and negative according to the level set function value is divided into interior zone Ω with the entire image zone Ω of SAR image I to be split ₁With perimeter Ω ₂

(2) according to described two regional Ω ₁With _Ω2, the regional area energy function E that structure is corresponding _L:

$E_L={\mathrm{\λ}}_1{\∫\∫}_{{\mathrm{\Ω}}_1}K\left(\mathrm{\φ}\right)*{|I-f_1\left(\mathrm{\φ}\right)|}^{2}H\left(\mathrm{\φ}\right)+{\mathrm{\λ}}_2{\∫\∫}_{{\mathrm{\Ω}}_2}K\left(\mathrm{\φ}\right)*{|I-f_2\left(\mathrm{\φ}\right)|}^2(1-H\left(\mathrm{\φ}\right)),$

Wherein, λ ₁And λ ₂Be the weights of local energy item, φ is a level set function, and K (φ) is a gaussian kernel function, and I is an image to be split, f ₁(φ) be regional Ω ₁The local gray level average, f ₂(φ) be regional Ω ₂The local gray level average, H (φ) is the Heaviside function;

(3) according to described two regional Ω ₁And Ω ₂, the global area energy function E that structure is corresponding _G:

$E_G=-{\mathrm{\λ}}_1{\∫\∫}_{{\mathrm{\Ω}}_1}\log \left(p_1\left(I\right)\right)-{\mathrm{\λ}}_2{\∫\∫}_{{\mathrm{\Ω}}_2}\log \left(p_2\left(I\right)\right),$

Wherein, λ ₁And λ ₂Be the weights of global energy item, p ₁(I) be regional Ω ₁Estimated probability density, p ₂(I) be regional Ω ₂Estimated probability density;

(4) the regional area energy function E of integrating step two structures _LGlobal area energy function E with the step 3 structure _G, the structure total energy function:

4a) according to the formula of asking level set length L (φ) in the local two-value match LBF method, calculated level collection length item L (φ):

$L\left(\mathrm{\φ}\right)={\∫\∫}_{\mathrm{\Ω}}|\▿H\left(\mathrm{\φ}\right)|,$

Wherein is gradient operator, and H (φ) is the Heaviside function;

4b) according to the formula of asking the penalty term P (φ) that avoids reinitializing in the local two-value match LBF method, calculate penalty term P (φ):

$P\left(\mathrm{\φ}\right)={\∫\∫}_{\mathrm{\Ω}}\frac{1}{2}{\left(\right|\▿\mathrm{\φ}|-1)}^2,$

Wherein

is that level set function φ is asked gradient;

4c) the regional area energy function E that tries to achieve of integrating step two _L, the global area energy function E that step 3 is tried to achieve _G, step 4a) and the bound term L (φ) and the step 4b that try to achieve) the penalty term P (φ) that tries to achieve, construct complete total energy function E _SAR:

E _SAR＝αE _L+(1-α)E _G++μL(φ)+νP(φ)，

Wherein α is used for regulating the ratio of local and global energy item, and μ is the weights of bound term, is used for regulating the value of length constraint item, and ν is the weights of penalty term, is used for regulating the value of penalty term, and the value of μ and ν obtains through experiment.

(5) the total energy function E that constructs according to step (4) _SARThe SAR image I is cut apart:

5a) to total energy function E _SARUtilize the variational method, obtain gradient katabatic drainage equation

$\frac{\∂\mathrm{\φ}}{\∂t}=\mathrm{\δ}\left(\mathrm{\φ}\right)(\mathrm{\α}(-({\mathrm{\λ}}_1{e}_1-{\mathrm{\λ}}_2{e}_2))+(1-\mathrm{\α})({\mathrm{\λ}}_1\log \left(P_1\left(I\right)\right)-{\mathrm{\λ}}_2\log \left(P_2\left(I\right)\right))$

$+\mathrm{\μ\δ}\left(\mathrm{\φ}\right)\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)+v({\▿}^2\mathrm{\φ}-\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)),$

Wherein: δ (φ) is the Dirac function, e ₁And e ₂Be intermediate variable, its expression formula is respectively:

e ₁＝∫∫ _ΩK(φ)*|I-f ₁(φ)| ²，e ₂＝∫∫ _ΩK(φ)*|I-f ₂(φ)| ²，

5b), obtain following expression formula to gradient katabatic drainage equation

discretize:

$\frac{{\mathrm{\φ}}^{n+1}-{\mathrm{\φ}}^n}{\mathrm{\Δt}}=\mathrm{\δ}\left(\mathrm{\φ}\right)(\mathrm{\α}(-({\mathrm{\λ}}_1{e}_1-{\mathrm{\λ}}_2{e}_2))+(1-\mathrm{\α})({\mathrm{\λ}}_1\log \left(P_1\left(I\right)\right)-{\mathrm{\λ}}_2\log \left(P_2\left(I\right)\right))$

$+\mathrm{\μ\δ}\left(\mathrm{\φ}\right)\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)+v({\▿}^2\mathrm{\φ}-\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)),$

Wherein: φ ^N+1Represent the level set function after the iteration the n+1 time, φ ⁿRepresent the level set function after the iteration the n time, Δ t is an iteration step length;

5c) according to step 5b) discretization equation that obtains

Try to achieve φ ^N+1Expression formula be:

${\mathrm{\φ}}^{n+1}={\mathrm{\φ}}^n+\mathrm{\Δt}\·(\mathrm{\δ}\left(\mathrm{\φ}\right)(\mathrm{\α}(-({\mathrm{\λ}}_1{e}_1-{\mathrm{\λ}}_2{e}_2))+(1-\mathrm{\α})({\mathrm{\λ}}_1\log \left(P_1\left(I\right)\right)-{\mathrm{\λ}}_2\log \left(P_2\left(I\right)\right))$

$+\mathrm{\μ\δ}\left(\mathrm{\φ}\right)\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)+v({\▿}^2\mathrm{\φ}-\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right))),$

5d) according to step 5c) try to achieve new level set function φ ^N+1, by φ ^N+1Positive negative value promptly obtain new cut zone

With

5e) whether the determined level set function is restrained and is reached maximum iterations 100 times, does not then forward step (2) to if do not satisfy, and uses

With

Substitute Ω ₁And Ω ₂Continue iteration, otherwise stop iteration, obtain

With

It promptly is final segmentation result.

The present invention has the following advantages compared with prior art:

1, the gaussian kernel function K (φ) of utilization of the present invention with fine local effect makes convolution to field strength information inside and outside the SAR image, as local region information, can guarantee to obtain enough segmenting edge.

2, the present invention has combined the Gamma statistical probability density information of SAR image coherent speckle noise; Directly constructed the global area energy function of level set function; Parted pattern need not carried out under the pretreated situation coherent speckle noise, can obtain relatively accurate segmentation result the SAR image that the property taken advantage of coherent speckle noise influences.

3, the present invention had both used local region information, used global area information again, made the region energy item structure of total energy function model more reasonable.The present invention makes full use of the area information of image; Not only the image border is had and well cut apart the localization effect; Improved accuracy again, also improved segmentation effect and robustness simultaneously edge fog and the uneven SAR image of gray scale to the object edge location.

Emulation through with existing level set dividing method contrasts, and has verified that the present invention can obtain better segmentation effect to the SAR image segmentation.

Description of drawings

Fig. 1 is a process flow diagram of the present invention;

Fig. 2 is first width of cloth test pattern;

Fig. 3 is with the present invention and the existing method segmentation effect comparison diagram to Fig. 2;

Fig. 4 is second width of cloth test pattern;

Fig. 5 is with the present invention and the existing method segmentation effect comparison diagram to Fig. 4;

Embodiment

With reference to Fig. 1, concrete performing step of the present invention is following:

Step 1 is initialized to the symbolic distance functional form with level set function φ, and positive and negative according to the level set function value is divided into two regional Ω with the entire image zone Ω of SAR image I to be split ₁And Ω ₂

The detailed process statement that realizes this step is as follows: on image I to be split, do a rectangle with PC Tools, this rectangle equation is as initialization level set function φ, when φ＞0, representes rectangle perimeter Ω ₁, when φ＜0, expression rectangle interior zone Ω ₂, therefore, positive and negative according to the level set function value is divided into two regional Ω with the SAR image ₁And Ω ₂

Step 2 is according to described two regional Ω ₁And Ω ₂, the regional area energy function E that structure is corresponding _L:

2a) choose the karyomerite function:

Suppose that K () is a kernel function, have local characteristics, this kernel function need meet the following conditions:

K(-x)＝K(x)

K(x)≤K(y)，if|x|＞|y|，

K(x)＝0，if|x|→∞

Wherein x and y are two independents variable, and y is to be the point of the regional area at center with x;

Analyzing above character draws: if decentering point x is far away more, can know that by the karyomerite function property its kernel function value is more little, if instead decentering point x is near more, its kernel function value is big more.Usually the kernel function that satisfies this character is a gaussian kernel function, so the present invention chooses gaussian kernel function commonly used as the karyomerite function, and its expression formula is following:

$K\left(\mathrm{\φ}\right)=\frac{1}{{\left(2\mathrm{\π}\right)}^{n/2}{\mathrm{\σ}}^n}\exp (-\frac{{\left|\mathrm{\φ}\right|}^2}{2{\mathrm{\σ}}^2}),$

Wherein φ is a level set function, and n is a positive integer, and σ is the standard variance of gaussian kernel function;

2b) according to karyomerite function K (φ), zoning Ω ₁And Ω ₂Local strength's average:

$f_1\left(\mathrm{\φ}\right)=\frac{K\left(\mathrm{\φ}\right)*\left[H\left(\mathrm{\φ}\right)I\right]}{K\left(\mathrm{\φ}\right)*H\left(\mathrm{\φ}\right)}$

$f_2\left(\mathrm{\φ}\right)=\frac{K\left(\mathrm{\φ}\right)*\left[\left(1-H\left(\mathrm{\φ}\right)\right)I\right]}{K\left(\mathrm{\φ}\right)*[1-H\left(\mathrm{\φ}\right)]},$

Wherein H (φ) is the Heaviside function, and I is a SAR image to be split, and * is a convolution algorithm;

2c) integrating step 2b) the local strength's average that obtains, it is following to obtain the regional area energy function:

$E_L={\mathrm{\λ}}_1{\∫\∫}_{{\mathrm{\Ω}}_1}K\left(\mathrm{\φ}\right)*{|I-f_1\left(\mathrm{\φ}\right)|}^{2}H\left(\mathrm{\φ}\right)+{\mathrm{\λ}}_2{\∫\∫}_{{\mathrm{\Ω}}_2}K\left(\mathrm{\φ}\right)*{|I-f_2\left(\mathrm{\φ}\right)|}^2(1-H\left(\mathrm{\φ}\right)),$

λ wherein ₁And λ ₂Be the weights of local energy item.

Step 3 is according to described two regional Ω ₁And Ω ₂, the global area energy function E that structure is corresponding _G:

3a) according to the formula that calculates SAR image intensity average in the statistical method, zoning Ω ₁Strength mean value c ₁With regional Ω ₂Strength mean value c ₂:

$\left\{\begin{array}{c}{c}_1=\frac{{\∫\∫}_{{\mathrm{\Ω}}_1}I(x,y)\mathrm{dxdy}}{{\∫\∫}_{{\mathrm{\Ω}}_1}\mathrm{dxdy}}\\ c_2=\frac{{\∫\∫}_{{\mathrm{\Ω}}_2}I(x,y)\mathrm{dxdy}}{{\∫\∫}_{{\mathrm{\Ω}}_2}\mathrm{dxdy}}\end{array}\right.,$

Wherein (x y) is the coordinate of SAR image I to be split;

3b) according to step 3a) the strength mean value c that tries to achieve ₁, c ₂With the formula that calculates SAR image probability density function in the statistical method, zoning Ω ₁And Ω ₂Estimated probability density p ₁(I) and p ₂(I):

$\left\{\begin{array}{c}{p}_1\left(I\right)=\frac{L^L}{c_1\mathrm{\Γ}\left(L\right)}{\left(\frac{I}{c_1}\right)}^{L-1}{e}^{-\mathrm{LI}/c_1}\\ p_2\left(I\right)=\frac{L^L}{c_2\mathrm{\Γ}\left(L\right)}{\left(\frac{I}{c_2}\right)}^{L-1}{e}^{-\mathrm{LI}/c_2}\end{array}\right.,$

Wherein L is the number of looking of SAR image, and Γ () is the Gamma function;

3c) integrating step 3a)-3b), construct energy function model E based on the global area _G:

$E_G=-{\mathrm{\λ}}_1{\∫\∫}_{{\mathrm{\Ω}}_1}\log \left(p_1\left(I\right)\right)-{\mathrm{\λ}}_2{\∫\∫}_{{\mathrm{\Ω}}_2}\log \left(p_2\left(I\right)\right),$

λ wherein ₁And λ ₂Be the weights of global energy item, p ₁(I) be regional Ω ₁Estimated probability density, p ₂(I) be regional Ω ₂Estimated probability density.

Step 4, the regional area energy function E of integrating step two structures _LGlobal area energy function E with the step 3 structure _G, the structure total energy function:

4a) according to the formula of asking level set length L (φ) in the local two-value match LBF method, calculated level collection length item L (φ):

$L\left(\mathrm{\φ}\right)={\∫\∫}_{\mathrm{\Ω}}|\▿H\left(\mathrm{\φ}\right)|,$

Wherein

is gradient operator, and H (φ) is the Heaviside function;

4b) according to the formula of asking the penalty term P (φ) that avoids reinitializing in the local two-value match LBF method, calculate penalty term P (φ):

$P\left(\mathrm{\φ}\right)={\∫\∫}_{\mathrm{\Ω}}\frac{1}{2}{\left(\right|\▿\mathrm{\φ}|-1)}^2,$

Wherein

is that level set function φ is asked gradient;

4c) the regional area energy function E that tries to achieve of integrating step two _L, the global area energy function E that step 3 is tried to achieve _G, step 4a) and the bound term L (φ) and the step 4b that try to achieve) the penalty term P (φ) that tries to achieve, construct complete total energy function E _SAR:

E _SAR＝αE _L+(1-α)E _G++μL(φ)+νP(φ)，

Wherein α is used for regulating the ratio of local and global energy item, and μ is the weights of bound term, is used for regulating the value of length constraint item, and ν is the weights of penalty term, is used for regulating the value of penalty term, and the value of μ and ν obtains through experiment.

Step 5 is according to the total energy function E of step 4 structure _SARThe SAR image I is cut apart:

5a) to total energy function E _SARUtilize the variational method, try to achieve gradient katabatic drainage equation and be:

$\frac{\∂\mathrm{\φ}}{\∂t}=\mathrm{\δ}\left(\mathrm{\φ}\right)(\mathrm{\α}(-({\mathrm{\λ}}_1{e}_1-{\mathrm{\λ}}_2{e}_2))+(1-\mathrm{\α})({\mathrm{\λ}}_1\log \left(P_1\left(I\right)\right)-{\mathrm{\λ}}_2\log \left(P_2\left(I\right)\right))$

$+\mathrm{\μ\δ}\left(\mathrm{\φ}\right)\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)+v({\▿}^2\mathrm{\φ}-\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)),$

Wherein: δ (φ) is the Dirac function, e ₁And e ₂Be intermediate variable, its expression formula is respectively:

e ₁＝∫∫ _ΩK(φ)*|I-f ₁(φ)| ²，e ₂＝∫∫ _ΩK(φ)*|I-f ₂(φ)| ²，

5b), obtain following expression formula to gradient katabatic drainage equation

discretize:

$\frac{{\mathrm{\φ}}^{n+1}-{\mathrm{\φ}}^n}{\mathrm{\Δt}}=\mathrm{\δ}\left(\mathrm{\φ}\right)(\mathrm{\α}(-({\mathrm{\λ}}_1{e}_1-{\mathrm{\λ}}_2{e}_2))+(1-\mathrm{\α})({\mathrm{\λ}}_1\log \left(P_1\left(I\right)\right)-{\mathrm{\λ}}_2\log \left(P_2\left(I\right)\right))$

$+\mathrm{\μ\δ}\left(\mathrm{\φ}\right)\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)+v({\▿}^2\mathrm{\φ}-\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)),$

φ wherein ^N+1Represent the level set function after the iteration the n+1 time, φ ⁿRepresent the level set function after the iteration the n time, Δ t is an iteration step length;

5c) according to step 5b) discretization equation that obtains

Try to achieve φ ^N+1Expression formula be:

${\mathrm{\φ}}^{n+1}={\mathrm{\φ}}^n+\mathrm{\Δt}\·(\mathrm{\δ}\left(\mathrm{\φ}\right)(\mathrm{\α}(-({\mathrm{\λ}}_1{e}_1-{\mathrm{\λ}}_2{e}_2))+(1-\mathrm{\α})({\mathrm{\λ}}_1\log \left(P_1\left(I\right)\right)-{\mathrm{\λ}}_2\log \left(P_2\left(I\right)\right))$

$+\mathrm{\μ\δ}\left(\mathrm{\φ}\right)\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)+v({\▿}^2\mathrm{\φ}-\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right))),$

5d) according to step 5c) try to achieve new level set function φ ^N+1, by φ ^N+1Positive and negative value obtain new cut zone

With

Be φ ^N+1＞0, obtain cut zone

φ ^N+1＜0 obtains cut zone

Step 6 judges whether convergence.

The fundamental purpose of this step is the iteration situation according to level set, and whether the determined level set function is restrained and reached maximum iterations, if gradient katabatic drainage equation does not reach steady state (SS) and do not reach maximum iterations, then forwards step 2 to, uses

With

Substitute Ω ₁And Ω ₂Continue iteration, otherwise stop iteration, obtain final zone

With

It promptly is final segmentation result.

Effect of the present invention can further specify through following emulation:

1, simulated conditions

Hardware platform is: Intel Core2 Duo CPU E65502.33GHZ, 2GB RAM.

Software platform is: MATLAB 7.9.

2, emulation content and interpretation of result

Emulation one: use the present invention and existing local two-value match LBF method and existing statistical method and respectively the test pattern of Fig. 2 is carried out split-run test; Its segmentation result is as shown in Figure 3, and wherein: Fig. 3 (a) is the segmentation result of local two-value match LBF method to Fig. 2; Fig. 3 (b) is the segmentation result of existing statistical method to Fig. 2; Fig. 3 (c) is the segmentation result of the inventive method to Fig. 2.

Can find out that by Fig. 3 (a) existing local two-value match LBF method is very sensitive to SAR image coherent speckle noise, has detected many false edges.Can find out that by Fig. 3 (b) existing statistical segmentation method can only detect partial contour to the SAR image of regional rich, and can not obtain complete segmentation result.Comparison diagram 3 (a) and Fig. 3 (c); Because the present invention has combined to suppress the global area information of SAR picture noise, and is inhibited to noise, so the present invention too much false edge can not occur; Comparison diagram 3 (b) and Fig. 3 (c); Because the present invention has combined local region information, can guarantee to obtain enough edges, thereby obtain relatively accurate segmentation result.

Emulation two: use the present invention and existing local two-value match LBF method and existing statistical method and respectively the test pattern of Fig. 4 is carried out split-run test; Its segmentation result is as shown in Figure 5, and wherein: Fig. 5 (a) is the segmentation result of local two-value match LBF method to Fig. 4; Fig. 5 (b) is the segmentation result of existing statistical method to Fig. 4; Fig. 5 (c) is the segmentation result of the inventive method to Fig. 4.

Can find out that by Fig. 5 (a) existing local two-value match LBF method is very sensitive to SAR image coherent speckle noise, so detected many false edges.Can find out that by Fig. 5 (b) existing statistical segmentation method can only detect partial contour to the SAR image, and can not obtain complete segmentation result.Can find out by Fig. 5 (c), inhibited and can obtain enough edges because the present invention has combined local and global area information to noise, the positioning performance at enhancing edge, thus obtain relatively accurate segmentation result.

Claims (3)

1. level set SAR image partition method that combines based on local and global area information comprises 0 step:

(1) level set function φ is initialized to the symbolic distance functional form, positive and negative according to the level set function value is divided into interior zone Ω with the entire image zone Ω of SAR image I to be split ₁With perimeter Ω ₂

(2) according to described two regional Ω ₁And Ω ₂, the regional area energy function E that structure is corresponding _L:

$E_L={\mathrm{\λ}}_1{\∫\∫}_{{\mathrm{\Ω}}_1}K\left(\mathrm{\φ}\right)*{|I-f_1\left(\mathrm{\φ}\right)|}^{2}H\left(\mathrm{\φ}\right)+{\mathrm{\λ}}_2{\∫\∫}_{{\mathrm{\Ω}}_2}K\left(\mathrm{\φ}\right)*{|I-f_2\left(\mathrm{\φ}\right)|}^2(1-H\left(\mathrm{\φ}\right)),$

Wherein, λ ₁And λ ₂Be the weights of local energy item, φ is a level set function, and K (φ) is a gaussian kernel function, and I is an image to be split, f ₁(φ) be regional Ω ₁The local gray level average, f ₂(φ) be regional Ω ₂The local gray level average, H (φ) is the Heaviside function;

(3) according to described two regional Ω ₁And Ω ₂, the global area energy function E that structure is corresponding _G:

$E_G=-{\mathrm{\λ}}_1{\∫\∫}_{{\mathrm{\Ω}}_1}\log \left(p_1\left(I\right)\right)-{\mathrm{\λ}}_2{\∫\∫}_{{\mathrm{\Ω}}_2}\log \left(p_2\left(I\right)\right),$

Wherein, λ ₁And λ ₂Be the weights of global energy item, p ₁(I) be regional Ω ₁Estimated probability density, p ₂(I) be regional Ω ₂Estimated probability density;

(4) the regional area energy function E of integrating step two structures _LGlobal area energy function E with the step 3 structure _G, the structure total energy function:

4a) according to the formula of asking level set length L (φ) in the local two-value match LBF method, calculated level collection length item L (φ):

$L\left(\mathrm{\φ}\right)={\∫\∫}_{\mathrm{\Ω}}|\▿H\left(\mathrm{\φ}\right)|,$

Wherein

is gradient operator, and H (φ) is the Heaviside function;

4b) according to the formula of asking the penalty term P (φ) that avoids reinitializing in the local two-value match LBF method, calculate penalty term P (φ):

$P\left(\mathrm{\φ}\right)={\∫\∫}_{\mathrm{\Ω}}\frac{1}{2}{\left(\right|\▿\mathrm{\φ}|-1)}^2,$

Wherein

is that level set function φ is asked gradient;

4c) the regional area energy function E that tries to achieve of integrating step two _L, the global area energy function E that step 3 is tried to achieve _G, step 4a) and the bound term L (φ) and the step 4b that try to achieve) the penalty term P (φ) that tries to achieve, construct complete total energy function E _SAR:

E _SAR＝αE _L+(1-α)E _G++μL(φ)+νP(φ)，

Wherein α is used for regulating the ratio of local and global energy item, and μ is the weights of bound term, is used for regulating the value of length constraint item, and ν is the weights of penalty term, is used for regulating the value of penalty term, and the value of μ and ν obtains through experiment.

(5) the total energy function E that constructs according to step (4) _SARThe SAR image I is cut apart:

5a) to total energy function E _SARUtilize the variational method, obtain gradient katabatic drainage equation

$\frac{\∂\mathrm{\φ}}{\∂t}=\mathrm{\δ}\left(\mathrm{\φ}\right)(\mathrm{\α}(-({\mathrm{\λ}}_1{e}_1-{\mathrm{\λ}}_2{e}_2))+(1-\mathrm{\α})({\mathrm{\λ}}_1\log \left(P_1\left(I\right)\right)-{\mathrm{\λ}}_2\log \left(P_2\left(I\right)\right))$

$+\mathrm{\μ\δ}\left(\mathrm{\φ}\right)\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)+v({\▿}^2\mathrm{\φ}-\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)),$

Wherein: δ (φ) is the Dirac function, e ₁And e ₂Be intermediate variable, its expression formula is respectively:

e ₁＝∫∫ _ΩK(φ)*|I-f ₁(φ)| ²，e ₂＝∫∫ _ΩK(φ)*|I-f ₂(φ)| ²，

5b), obtain following expression formula to gradient katabatic drainage equation

discretize:

$\frac{{\mathrm{\φ}}^{n+1}-{\mathrm{\φ}}^n}{\mathrm{\Δt}}=\mathrm{\δ}\left(\mathrm{\φ}\right)(\mathrm{\α}(-({\mathrm{\λ}}_1{e}_1-{\mathrm{\λ}}_2{e}_2))+(1-\mathrm{\α})({\mathrm{\λ}}_1\log \left(P_1\left(I\right)\right)-{\mathrm{\λ}}_2\log \left(P_2\left(I\right)\right))$

$+\mathrm{\μ\δ}\left(\mathrm{\φ}\right)\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)+v({\▿}^2\mathrm{\φ}-\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)),$

Wherein: φ ^N+1Represent the level set function after the iteration the n+1 time, φ ⁿRepresent the level set function after the iteration the n time, Δ t is an iteration step length;

5c) according to step 5b) discretization equation that obtains

Try to achieve φ ^N+1Expression formula be:

${\mathrm{\φ}}^{n+1}={\mathrm{\φ}}^n+\mathrm{\Δt}\·(\mathrm{\δ}\left(\mathrm{\φ}\right)(\mathrm{\α}(-({\mathrm{\λ}}_1{e}_1-{\mathrm{\λ}}_2{e}_2))+(1-\mathrm{\α})({\mathrm{\λ}}_1\log \left(P_1\left(I\right)\right)-{\mathrm{\λ}}_2\log \left(P_2\left(I\right)\right))$

$+\mathrm{\μ\δ}\left(\mathrm{\φ}\right)\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right)+v({\▿}^2\mathrm{\φ}-\mathrm{div}\left(\frac{\▿\mathrm{\φ}}{|\▿\mathrm{\φ}|}\right))),$

5d) according to step 5c) try to achieve new level set function φ ^N+1, by φ ^N+1Positive negative value promptly obtain new cut zone

With

5e) whether the determined level set function is restrained and is reached maximum iterations 100 times, does not then forward step (2) to if do not satisfy, and uses

With

Substitute Ω ₁And Ω ₂Continue iteration, otherwise stop iteration, obtain With

It promptly is final segmentation result.

2. level set SAR image partition method according to claim 1, the corresponding regional area energy function of the described structure of step (2) wherein, carry out as follows:

2a) choose gaussian kernel function commonly used as the karyomerite function, its expression formula is following:

$K\left(\mathrm{\φ}\right)=\frac{1}{{\left(2\mathrm{\π}\right)}^{n/2}{\mathrm{\σ}}^n}\exp (-\frac{{\left|\mathrm{\φ}\right|}^2}{2{\mathrm{\σ}}^2}),$

Wherein φ is a level set function, and n is a positive integer, and σ is the standard variance of gaussian kernel function;

2b) according to the karyomerite function, zoning Ω ₁And Ω ₂Local strength's average:

$f_1\left(\mathrm{\φ}\right)=\frac{K\left(\mathrm{\φ}\right)*\left[H\left(\mathrm{\φ}\right)I\right]}{K\left(\mathrm{\φ}\right)*H\left(\mathrm{\φ}\right)}$

$f_2\left(\mathrm{\φ}\right)=\frac{K\left(\mathrm{\φ}\right)*\left[\left(1-H\left(\mathrm{\φ}\right)\right)I\right]}{K\left(\mathrm{\φ}\right)*[1-H\left(\mathrm{\φ}\right)]},$

Wherein H (φ) is the Heaviside function, and I is a SAR image to be split, and * is a convolution algorithm;

2c) integrating step 2b) the local strength's average that obtains, it is following to obtain the regional area energy function:

$E_L={\mathrm{\λ}}_1{\∫\∫}_{{\mathrm{\Ω}}_1}K\left(\mathrm{\φ}\right)*{|I-f_1\left(\mathrm{\φ}\right)|}^{2}H\left(\mathrm{\φ}\right)+{\mathrm{\λ}}_2{\∫\∫}_{{\mathrm{\Ω}}_2}K\left(\mathrm{\φ}\right)*{|I-f_2\left(\mathrm{\φ}\right)|}^2(1-H\left(\mathrm{\φ}\right)),$

λ wherein ₁And λ ₂Be the weights of local energy item.

3. level set SAR image partition method according to claim 1, the corresponding global area energy function of the described structure of step (3) wherein, carry out as follows:

3a) zoning Ω ₁And Ω ₂Strength mean value:

$c_i=\frac{{\∫\∫}_{{\mathrm{\Ω}}_i}I(x,y)\mathrm{dxdy}}{{\∫\∫}_{{\mathrm{\Ω}}_i}\mathrm{dxdy}},$

I=1 wherein, 2, (x y) is the coordinate of SAR image I to be split;

3b) according to strength mean value c _i, zoning Ω ₁And Ω ₂Estimated probability density p ₁(I) and p ₂(I):

$p_i\left(I\right)=\frac{L^L}{c_i\mathrm{\Γ}\left(L\right)}{\left(\frac{I}{c_i}\right)}^{L-1}{e}^{-\mathrm{LI}/c_i},$ i＝1，2，

Wherein L is the number of looking of SAR image, and Γ () is the Gamma function;

3c) integrating step 3a)-3b) construct energy function model E based on the global area _G:

$E_G=-{\mathrm{\λ}}_1{\∫\∫}_{{\mathrm{\Ω}}_1}\log \left(p_1\left(I\right)\right)-{\mathrm{\λ}}_2{\∫\∫}_{{\mathrm{\Ω}}_2}\log \left(p_2\left(I\right)\right),$

λ wherein ₁And λ ₂Be the weights of global energy item, p ₁(I) be regional Ω ₁Estimated probability density, p ₂(I) be regional Ω ₂Estimated probability density.

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