CN105261028A - Energy aggregation degree measure-based sea and land clutter scene segmentation method - Google Patents

Abstract

The invention discloses an energy aggregation degree measure-based sea and land clutter scene segmentation method. The main objective of the invention is to solve the problem of poor segmentation quality in the prior art. According to the technical schemes of the invention, the method includes the following steps that: 1, a radar transmitter is utilized to transmit pulse signals, a radar receiver receives echo data, and an echo sequence in each resolution unit of the echo data is X; 2, over-M sampling Fourier transform is performed on X, so that the frequency sequence Y of the echo sequence in each resolution can be obtained; 3, Y is utilized to calculate an energy aggregation degree matrix E; 4, E is converted into a gray image H, and median filtering is performed on the gray image H; 5, threshold initial segmentation is performed on an image H1 which is obtained after the median filtering through utilizing an Otsu threshold segmentation method, so that an initially-segmented image B can be obtained; and 6, morphological filtering is performed on the image B, so that a final clutter segmentation result Z can be obtained. With the energy aggregation degree measure-based sea and land clutter scene segmentation method of the invention adopted, the quality of sea and land clutter scene segmentation can be improved, and real-time scene segmentation requirements can be satisfied. The method can be applied to sea and land clutter scene segmentation under a moving or static coherent system platform.

Description

Based on the extra large land clutter Scene Segmentation that energy compaction measure is estimated

Technical field

The invention belongs to signal processing technology field, be specifically related to a kind of extra large land clutter Scene Segmentation, can be used for the extra large land clutter scene cut under motion or static coherent system platform.

Background technology

Target detection technique under sea clutter background is a vital research direction in radar application technology, is used widely in military and civilian field.When radar is when to extra large MODE of operation, scanning scene is complicated and scope is comparatively large, often contains various types of clutter, comprise sea clutter, land clutter, islands and reefs clutter, coastal waters clutter etc. in radar return.Land clutter and islands and reefs clutter echo strength are comparatively strong, drastically influence the target detection under sea clutter background, and complicated clutter scene and noise performance constitute the major obstacle that sea-surface target detects.Therefore, before sea-surface target detects, splitting extra large land clutter scene is necessary pre-service.By extra large land clutter scene cut by land and islands and reefs part in radar return clutter scene to separating, in the process of target detection, land clutter and islands and reefs clutter are excluded, and decrease land clutter and large-scale islands and reefs clutter to the impact of target detection under sea clutter background.The quality of sea land clutter scene cut will directly affect the quality of target detection performance under sea clutter background.

Land, sea clutter scene cut splits clutter scene on the basis of Analysis of Radar echo data.Be different from traditional image partition method, the segmentation of radar clutter scene includes conversion radar data and becomes gray level image and gray level image to split two parts.In the complex clutter scene that extra large land mixes, because the radar parameters such as the environmental factors such as extra large state, the depth of water, salinity, temperature and radar beam incident angle, beam angle are on the impact of noise intensity, clutter echo power changes in very large dynamic range, and it is infeasible for only rely on clutter power to estimate carrying out extra large land clutter scene cut.Due to aircraft motion, echo Doppler skew, along with azimuthal variation, relies on the extra large land clutter scene cut estimated of Doppler frequency high calculation cost often, is difficult to the requirement of satisfied real-time extra large land clutter scene cut.If utilize artificial method directly to carry out description segmentation to clutter scene, waste time and energy, will huge workload be brought, real-time scene cut requirement cannot be met.

In recent years, Many researchers carries out deep research to extra large land dividing method, proposes some extra large land dividing methods based on particular theory.Document " Ai Guohong; Rossa Verona; Yue Lihua. based on extra large land partitioning algorithm [J] of multiple features dynamic fusion model. electronic technology .2011; 3:52-57. " in the feature of image to be extracted and fusion obtains comprehensive characteristics figure, then Threshold segmentation and mapping and edge treated are carried out to comprehensive characteristics image.The comprehensive characteristics mentioned in the method only includes textural characteristics and gray feature, when image is comparatively complicated, when namely sea gray-scale value is close with land gray-scale value, is just difficult to distinguish water area and land area from textural characteristics and gray feature.Document " list power, Wang Chao, Zhang Hong. based on SAR image land dividing method research, sea [J] optimizing movable contour model. computer utility research .2011, 28 (6). " in propose a kind of extra large land automatic division method based on movable contour model, the edge of image and regional statistical information are fused in energy function, carry out Iamge Segmentation on this basis, the method carries out the segmentation of extra large land by some feature extracted in image, but when sea condition is comparatively complicated, the characteristics of image that they extract all cannot well distinguish land and ocean, be difficult to obtain good segmentation effect.

Summary of the invention

The object of the invention is to propose a kind of extra large land clutter Scene Segmentation estimated based on energy compaction measure, under realizing motion or static coherent system platform, quick, the Real-time segmentation of extra large land clutter scene, improve the quality of segmentation.

For realizing above-mentioned technical purpose, technical scheme of the present invention comprises the steps:

(1) utilize radar transmitter transponder pulse signal, utilize radar receiver to receive the echo data formed through surface scattering, the echo sequence in each resolution element of this echo data is X:

X＝[x ₁,x ₂,...,x _i,...,x _N]，

Wherein x _irepresent i-th echo data, N indicating impulse number;

(2) the echo sequence X in each resolution element was carried out to the Fourier transform of M sampling, obtained the frequency sequence Y of echo sequence in each resolution element:

Y＝[y ₁,y ₂,...,y _k,...,y _MN]

Wherein y _krepresent a kth frequency, MN represents Doppler's port number;

(3) utilize the frequency sequence Y of echo sequence in each resolution element, calculate the energy compaction measure in each resolution element, obtain the energy compaction measure matrix E of all resolution elements:

3a) calculate the energy in all Doppler's passages of the frequency sequence Y of echo sequence in each resolution element, obtain the maximal value s of energy in each resolution element _jl:

$s_{jl}=M\×\underset{k=1,2,...,MN}{max}\left\{\right|y_k{|}^2\},j=1,2,...,W,l=1,2,...,L$

Wherein || ²represent mould square, max{} represents and gets maximal value, and j represents distance dimension, and l represents that ripple position tie up, and W represents distance sum, and L represents that ripple position is total;

3b) calculate the gross energy p of the frequency sequence Y of echo sequence in each resolution element _jl:

$p_{jl}=\underset{k=1}{\overset{MN}{\Σ}}|y_k{|}^2,j=1,2,...,W,l=1,2,...,L;$

3c) calculate the maximal value s of energy in each resolution element _jlwith gross energy p _jlratio, obtain the energy compaction measure e of each resolution element _jl:

$e_{jl}=\frac{M\×\underset{k=1,2,...,MN}{max}\left\{\right|y_k{|}^2\}}{\underset{k=1}{\overset{MN}{\Σ}}|y_k{|}^2},$

3d) utilize the energy compaction measure e of each resolution element _jl, obtain the energy compaction measure matrix E of all resolution elements:

(4) energy compaction measure matrix E is converted into gray level image H;

(5) medium filtering is carried out to gray level image H, obtain the image H1 after medium filtering;

(6) utilize maximum between-cluster variance thresholding method to carry out threshold value to the image H1 after medium filtering just to split, obtain the image B after just segmentation;

(7) morphologic filtering is carried out to the image B after just segmentation, obtain final clutter scene segmentation result Z.

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

1) because the present invention utilizes energy compaction measure estimating as extra large land clutter scene cut, fully demonstrate and to have plunged into the commercial sea the otherness of land clutter in motion or static coherent system platform, and computing velocity is fast, can meet the real time handling requirement of actual radar system;

2) because the present invention utilizes maximum between-cluster variance thresholding method to carry out just segmentation to gray level image, its process obtaining threshold value is adaptive, do not need any parameter of artificial setting, fast operation, image grey level histogram not significantly bimodal or trough time, also can obtain satisfied segmentation effect;

3) because the present invention utilizes morphologic filtering to process the image after just segmentation, ensure that the connectedness of territory, segmentation result Mid-continent and sea area, improve the quality of extra large land clutter scene cut.

Accompanying drawing explanation

Fig. 1 is realization flow figure of the present invention;

Fig. 2 adopts the present invention and existingly estimates the extra large land clutter scene cut comparison diagram obtained.

Embodiment

Below in conjunction with accompanying drawing, the invention will be further described:

With reference to Fig. 1, performing step of the present invention is as follows:

Step 1, utilizes radar transmitter transponder pulse signal, utilizes radar receiver to receive the echo data formed through surface scattering.

Echo data is one and comprises pulse dimension, the three-dimensional matrice of distance peacekeeping ripple position dimension, each distance peacekeeping ripple position dimension formation resolution element, and the echo sequence in each resolution element is X:

X＝[x ₁,x ₂,...,x _i,...,x _N]，

Wherein x _irepresent i-th echo data, N indicating impulse number.

Step 2, carried out the Fourier transform of M sampling, obtained the frequency sequence of echo data to echo data.

Here the value of M is 2 exponentials, and namely in 2,4,8... examples of the present invention, the value of M is 4.

Echo sequence X in each resolution element of echo data was carried out to the Fourier transform of M sampling, obtained the frequency sequence Y of echo sequence in each resolution element:

Y＝[y ₁,y ₂,...,y _k,...,y _MN]，

Wherein y _krepresent a kth frequency, MN represents Doppler's port number;

Step 3, utilizes the frequency sequence of echo data to calculate energy compaction measure matrix E.

(3.1) calculate the energy in all Doppler's passages of the frequency sequence Y of echo sequence in each resolution element, obtain the maximal value s of energy in each resolution element _jl:

$s_{jl}=M\&times;\underset{k=1,2,...,MN}{max}\left\{\right|y_k{|}^2\},j=1,2,...,W,l=1,2,...,L---<1>$

Wherein || ²represent mould square, max{} represents and gets maximal value, and j represents distance dimension, and l represents that ripple position tie up, and W represents distance sum, and L represents that ripple position is total;

(3.2) the gross energy p of the frequency sequence Y of echo sequence in each resolution element is calculated _jl:

$p_{jl}=\underset{k=1}{\overset{MN}{\&Sigma;}}|y_k{|}^2,j=1,2,...,W,l=1,2,...,L---<2>$

(3.3) the maximal value s of energy in each resolution element of representing of calculating formula <1> _jlwith the gross energy p of each resolution element that formula <2> represents _jlratio, obtain the energy compaction measure e of each resolution element _jl:

$e_{jl}=\frac{M\&times;\underset{k=1,2,...,MN}{max}\left\{\right|y_k{|}^2\}}{\underset{k=1}{\overset{MN}{\&Sigma;}}|y_k{|}^2},---<3>$

(3.4) the energy compaction measure e of each resolution element utilizing formula <3> to represent _jl, obtain the energy compaction measure matrix E of all resolution elements:

The physical significance of energy compaction measure is: backward energy is gathered in the precision of Doppler's passage.

Step 4, is converted into gray level image H by energy compaction measure matrix E.

In MATLAB, call H=mat2gray (E) order, energy compaction measure matrix E is converted into gray level image H, and in gray level image H, the gray-scale value of land area is greater than the gray-scale value of sea area.

Step 5, carries out medium filtering to gray level image H, obtains the image H1 after medium filtering.

Here filtering method can adopt mean filter, medium filtering, Wiener filtering, and what example of the present invention was selected is medium filtering.The key of medium filtering selects suitable window shape and size, and its step is as follows:

(5.1) window of medium filtering is set to the square window of 3 × 3;

(5.2) in gray level image H all 3 × 3 square window in all grey scale pixel values sort;

(5.3) intermediate value of getting ranking results, as the gray-scale value of square window central spot pixel of 3 × 3, obtains the image H1 after medium filtering.

Step 6, utilizes maximum between-cluster variance thresholding method to carry out threshold value to the image H1 after medium filtering and just splits, and obtains the image B after just segmentation.

Here threshold value is just split and can be adopted: grey level histogram thresholding method, maximum entropy threshold split plot design, maximum between-cluster variance thresholding method, process of iteration thresholding method, and what example of the present invention was selected is maximum between-cluster variance thresholding method.

(6.1) get background and the two-part inter-class variance of target in the image H1 after medium filtering maximum time corresponding gray-scale value be optimal threshold;

(6.2) gray-scale value of pixel larger than optimal threshold for gray-scale value in the image H1 after medium filtering is set to 1, namely the gray-scale value of land area is 1;

(6.3) gray-scale value of pixel less than optimal threshold in the image H1 after medium filtering is set to 0, namely the gray-scale value of sea area is 0, obtains the image B after just segmentation.

In image B after first segmentation, sea area is contained the isolated point needs such as large-scale target or islands and reefs and is removed, and has a lot of holes to need to fill in land area.

Step 7, carries out morphologic filtering to the image B after just segmentation, obtains final clutter scene segmentation result Z.

(7.1) in the image B after just segmentation, find out in sea area isolated points such as needing removed large-scale target or islands and reefs, find out in land area the hole needing to fill;

(7.2) structural element arranged in morphologic filtering to be the length of side be 5 eight-sided formation element;

(7.3) the image B after just segmentation is carried out to the opening operation in morphologic filtering, by burr, isolated point remove less than structural element in sea area;

(7.4) closed operation in morphologic filtering is carried out to the image after opening operation, by holes filling less than structural element in land area, obtain final extra large land clutter scene segmentation result Z.

Based on step 1 to step 7, achieve the extra large land clutter scene cut estimated based on energy compaction measure.

Below in conjunction with emulation experiment, effect of the present invention is described further.

1. simulation parameter

The data adopted in emulation experiment are the Observed sea clutters on certain island that airborne radar obtains, and packet is containing 2921 range units, and 373 ripple positions, each resolution element has 5 pulses.

2. emulation experiment content

Adopt the inventive method in emulation experiment respectively and obtain the extra large land segmentation result of Observed sea clutter based on the extra large land clutter Scene Segmentation that phase linearity is estimated, compared the segmentation quality of two kinds of dividing methods by segmentation result figure.

Emulation experiment, to the Observed sea clutter that airborne radar obtains, adopt the inventive method respectively and obtain extra large land segmentation result based on the extra large land clutter Scene Segmentation that phase linearity is estimated, the Comparative result of two kinds of methods as shown in Figure 2, in Fig. 2, the transverse axis of two width subgraphs all represents that ripple position is tieed up, and the longitudinal axis all represents distance dimension, and white represents land, black represents ocean, wherein:

Fig. 2 (a) represents the extra large land clutter scene segmentation result adopting the present invention to obtain;

Fig. 2 (b) represents the segmentation result adopting the extra large land clutter Scene Segmentation estimated based on phase linearity to obtain.

As can be seen from Figure 2, the segmentation result adopting the inventive method to obtain obviously is better than the segmentation result that existing method obtains.

In sum, the extra large land clutter Scene Segmentation estimated based on energy compaction measure that the present invention proposes, under motion or static coherent system platform condition can be improved, the quality of land, sea clutter scene cut, and computing velocity is fast, the real time handling requirement of actual radar system can be met, be conducive to the raising of target detection performance under follow-up sea clutter background.

Claims (5)

1., based on the extra large land clutter Scene Segmentation that energy compaction measure is estimated, it is characterized in that, comprising:

(1) utilize radar transmitter transponder pulse signal, utilize radar receiver to receive the echo data formed through surface scattering, the echo sequence in each resolution element of this echo data is X:

X＝[x ₁,x ₂,...,x _i,...,x _N]，

Wherein x _irepresent i-th echo data, N indicating impulse number;

(2) the echo sequence X in each resolution element was carried out to the Fourier transform of M sampling, obtained the frequency sequence Y of echo sequence in each resolution element:

Y＝[y ₁,y ₂,...,y _k,...,y _MN]

Wherein y _krepresent a kth frequency, MN represents Doppler's port number, and the value of M is 4;

(3) utilize the frequency sequence Y of echo sequence in each resolution element, calculate the energy compaction measure in each resolution element, obtain the energy compaction measure matrix E of all resolution elements:

3a) calculate the energy in all Doppler's passages of the frequency sequence Y of echo sequence in each resolution element, obtain the maximal value s of energy in each resolution element _jl:

$s_{jl}=M\&times;\underset{k=1,2,...,MN}{max}\left\{\right|y_k{|}^2\},j=1,2,...,W,l=1,2,...,L$

Wherein || ²represent mould square, max{} represents and gets maximal value, and j represents distance dimension, and l represents that ripple position tie up, and W represents distance sum, and L represents that ripple position is total;

3b) calculate the gross energy p of the frequency sequence Y of echo sequence in each resolution element _jl:

$p_{jl}=\underset{k=1}{\overset{MN}{\&Sigma;}}|y_k{|}^2,j=1,2,...,W,l=1,2,...,L;$

3c) calculate the maximal value s of energy in each resolution element _jlwith gross energy p _jlratio, obtain the energy compaction measure e of each resolution element _jl:

$e_{jl}=\frac{M\&times;\underset{k=1,2,...,MN}{max}\left\{\right|y_k{|}^2\}}{\underset{k=1}{\overset{MN}{\&Sigma;}}|y_k{|}^2},$

3d) utilize the energy compaction measure e of each resolution element _jl, obtain the energy compaction measure matrix E of all resolution elements:

(4) energy compaction measure matrix E is converted into gray level image H;

(5) medium filtering is carried out to gray level image H, obtain the image H1 after medium filtering;

(6) utilize maximum between-cluster variance thresholding method to carry out threshold value to the image H1 after medium filtering just to split, obtain the image B after just segmentation;

(7) morphologic filtering is carried out to the image B after just segmentation, obtain final clutter scene segmentation result Z.

2. the extra large land clutter Scene Segmentation estimated based on energy compaction measure as claimed in claim 1, it is characterized in that, echo data in step (1) is one and comprises pulse dimension, the three-dimensional matrice of distance peacekeeping ripple position dimension, each distance peacekeeping ripple position dimension formation resolution element.

3. the extra large land clutter Scene Segmentation estimated based on energy compaction measure as claimed in claim 1, it is characterized in that, step carries out medium filtering to gray level image H in (5), obtains the image H1 after medium filtering, carries out as follows:

5a) window of medium filtering is set to the square window of 3 × 3;

5b) in gray level image H all 3 × 3 square window in all grey scale pixel values sort;

The intermediate value of 5c) getting ranking results, as the gray-scale value of square window central spot pixel of 3 × 3, obtains the image H1 after medium filtering.

4. the extra large land clutter Scene Segmentation estimated based on energy compaction measure as claimed in claim 1, it is characterized in that, utilize maximum between-cluster variance thresholding method to carry out threshold value to the image H1 after medium filtering in step (6) just to split, be get background and the two-part inter-class variance of target in image maximum time corresponding gray-scale value as optimal threshold, and the gray-scale value of pixel larger than optimal threshold for gray-scale value in image is set to 1, namely the gray-scale value of land area is 1, the gray-scale value of the pixel less than optimal threshold is set to 0, namely the gray-scale value of sea area is 0, obtain the image B after just segmentation.

5. the extra large land clutter Scene Segmentation estimated based on energy compaction measure as claimed in claim 1, is characterized in that, carries out morphologic filtering, carry out as follows in step (7) to the image B after just segmentation:

(7.1) in the image B after just segmentation, find out in sea area isolated points such as needing removed large-scale target or islands and reefs, find out in land area the hole needing to fill;

(7.2) structural element arranged in morphologic filtering to be the length of side be 5 eight-sided formation element;

(7.3) the image B after just segmentation is carried out to the opening operation in morphologic filtering, by burr, isolated point remove less than structural element in sea area;

(7.4) closed operation in morphologic filtering is carried out to the image after opening operation, by holes filling less than structural element in land area, obtain final extra large land clutter scene segmentation result Z.