RESEARCH Open Access Fast target detection method for high

Cao et al. EURASIP Journal on Advances in Signal Processing 2014, 2014:45http://asp.eurasipjournals.com/content/2014/1/45

RESEARCH Open Access

Fast target detection method for high-resolutionSAR images based on variance weightedinformation entropyZongjie Cao*, Yuchen Ge and Jilan Feng

Abstract

Since the traditional CFAR algorithm is not suitable for high-resolution target detection of synthetic apertureradar (SAR) images, a new two-stage target detection method based on variance weighted information entropy isproposed in this paper. On the first stage, the regions of interest (ROIs) in SAR image is extracted based on thevariance weighted information entropy (WIE), which has been proved to be a simple and effective quantitativedescription index for the complex degree of infrared image background. Considering that SAR images are nonuniform,an experiment is conducted ahead, in which the value of the variance WIE from a real SAR image in three areas withsignificant different uniform levels are tested and compared. The results preliminarily verified that the variance WIE isable to measure the complex degree of SAR images. After that, in order to make the segmentation efficient, the roughROIs are further processed with a series of methods which adjust ROIs into regular pieces. On the second stage, foreach of the ROIs, a variational segmentation algorithm based on the Split-Bregman algorithm is adopted to extractthe target. In our experiment, the proposed method is tested on two kinds of SAR images, and its effectiveness issuccessfully demonstrated.

Keywords: Synthetic aperture radar; Target detection; Variance weighted information entropy; Variational segmentation

1 IntroductionMan-made objects can be divided into linear targets andblob targets. For linear targets, like roads, railways, brid-ges, and airport runway, they often appear to have obviouscharacteristics of straight line or curve and can be de-scribed approximately through an accurate extraction ofthe line feature. However, blob targets like tanks, ships,vehicles, aircraft, bunkers, oil depots, power plants, andother types of construction usually have a reunion in thespatial distribution, which means that the targets are lo-cated in a rectangular area in the image as a whole.This paper is focused on the detection of blob targets.Among applications of synthetic aperture radar (SAR)

images, the automatic target recognition (ATR) systemis of great importance. The target detection of SAR im-ages, serving as the first stage of ATR systems, providesa basis for the validity of subsequent recognition.By now, the most widely used target detection algo-

rithm is the constant false alarm detection (CFAR).

* Correspondence: [email protected] of Electronic Engineering, UESTC, Chengdu 611731, China

© 2014 Cao et al.; licensee Springer. This is anAttribution License (http://creativecommons.orin any medium, provided the original work is p

CFAR algorithms detect targets by pixel-wise thresholdoperation, in which the threshold is adaptively computedfor each pixel according to the statistical property ofpixels within a local window [1-7].However, since CFAR is a kind of pixel level methods

of target detection, it is not suitable for SAR images ofhigh resolution in reality. First of all, the serious speckleimpact of high-resolution SAR image increases the prob-ability of false alarm. Secondly, in high-resolution SARimages, a target often occupies a lot of resolution cellsand is with multiple number of independent strong scat-tering. After CFAR detection, a target is likely to becomposed of a plurality of distributed pixels that cannotform a connected region, which increases the probabilityof false alarm and the loss of information of structureand shape.To overcome the problems of traditional CFAR algo-

rithm, this paper proposes a target extraction algorithmof high-resolution SAR image based on ‘location-seg-mentation’ stages and a new detection character. As isknown, traditional one-dimensional graph entropy is a

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Cao et al. EURASIP Journal on Advances in Signal Processing 2014, 2014:45 Page 2 of 11http://asp.eurasipjournals.com/content/2014/1/45

statistical form of characteristics which depicts the accu-mulation characteristics of gray level distribution and re-flects the average information of a figure. However, itcan only represent global information rather than theproperty of its spatial relationships [8,9]. To optimizethis character, a derivative named the variance weightedinformation entropy (WIE) was applied firstly in the de-tection of infrared images. Due to the fact that infraredobjects with different radiation usually appear to havedistinct gray value in real images, the variance WIE hasbeen proved to be a simple and effective quantitative de-scription index for the complex degree of infrared imagebackground [10]. Analogous to the infrared images, theSAR images are also nonuniform. Hence, in this paper,the value of the variance WIE from a real SAR image inits uniform area, area including ups and downs, and thearea which contains the target is primarily tested. Theresult preliminarily verifies that the values of highly non-uniform areas which allude to regions of interest (ROIs)are significantly greater than that of homogeneousbackgrounds.In the first stage, ROIs are obtained through an iterative

algorithm based on WIE and averaging segmentation. Thismeans allowed us to roughly and quickly locate severalpotential targets. However, due to their restrictions, targetsare often not completely included in the ROI or are notcompletely at the center of the ROI. To get an effectivesegmentation in the next stages, this paper proposes aseries of processing methods subsequently, including falsealarm exclusion algorithm, rectangular-completing algo-rithm of ROI, and centroid alignment algorithm to refinethe ROIs and filter out false alarms. On the second stage,the variational SAR image segmentation method is utilizedto get ROI pieces. This target segmentation method of vari-ational model under the framework of partial differentialcan be both flexible and robust, expressing the boundaryregion naturally and integrating a variety of image informa-tion organically to complete the image segmentation. Forextracting SAR target segmentation based on partial differ-ential equation, it mainly involves two aspects: establishinga reasonable energy functional to describe SAR image char-acteristics and finding effective optimization methods ofthe energy functional. In the experiment part, this newalgorithm is tested with results that confirm the validity inboth high-resolution SAR images in real and composedSAR images with transcendental position information.The rest of the paper is organized as follows. Section 2

introduces the location stage of the high-resolution SARimages based on the variance weighted information en-tropy. In Section 3, the segmentation stage and the con-solidated flowchart of the whole algorithm are shown.Section 4 is devoted to experimental results for SAR tar-get extraction with the proposed techniques. Conclusionsare drawn in Section 5.

2 PositioningIn this section, the variance WIE is introduced and asmall experiment is conducted so as to preliminarilyprove that the variance WIE could measure the complexdegree of SAR images. After that, the flowchart of theentire location stage and the detailed steps are intro-duced respectively.

2.1 The variance weighted information entropyThe gray value of pixels in SAR Images can reflectspatial distribution of the energy emitted by electromag-netic waves. Accordingly, the concept of entropy in in-formation theory is extended in the field of imageprocessing. Serving as a derivative application of infor-mation entropy proposed by Shannon, graph entropy isa statistical form of characteristics, which reflects theaverage information of a figure.For a digital image of M rows and N columns with a

gray level i 0 < i ≤ 255, after getting its probability distri-bution of each gray level, let fi be the observed frequen-cies of gray level i, then the occurrence probability ofgray scale i is as follows:

Pi ¼ f iM � N

; i ∈ 0; 255½ �X255i¼0

f i ¼ M � N ð1Þ

and the one-dimensional graph entropy can beexpressed as

H ¼ −X255i¼0

pi log pi ð2Þ

when Pi = 0, let Pi log Pi = 0.By minimizing H, we can get pi = 1, i ∈ [0, 255] so that

the digital image only has a certain gray level which is i.This gives a uniform distribution.By analyzing its nature further, several conclusions can

be drawn as follows:

1. Graph entropy can reflect the degree of difference inthe gray values of pixels. With a smaller value ofgraph entropy, the spatial distribution of energywithin the window is more uniform and thedifference in the gray values of pixels is weaker.When the gray values of all pixels within thewindow are the same, the graph entropy reaches theminimum that is H = 0. On the contrary, when theentropy value is greater, the difference in the grayvalues of pixels within the window is big due to theexistence of blob targets.

2. The calculation of graph entropy is a kind ofnonlinear spatial filtering. When blob targetsappeared, the mutation of gray value in a localregion can lead to a mutation of graph entropy sincethe background of the SAR image is rather


homogeneous or slowly varying and the backgroundnoise can be fitted by a certain random noise modelwith similar entropy.

3. Being less sensitive to the imaging sensor andgeometric distortion, graph entropy can help tolocate potential target area correctly.

Although one-dimensional graph entropy can depictthe accumulation characteristics of gray level distribu-tion, it can only represent global information rather thanthe property of its spatial relationships [8,9]. Hence, thevariance WIE is introduced in this paper to deal with lo-cation processing.The variance WIE was first applied to the detection of

infrared images. Due to the fact that infrared objectswith different radiation usually appear to have distinctgray value in real images, the variance WIE which hasbeen proved to be a simple and effective quantitative de-scription index for the complex degree of infrared imagebackground [10] is introduced.For a subimage extracted from the original with a gray

level i (0 < i ≤ 255),

H ið Þ ¼ −X255i¼0

i−�ið Þ2Pi log Pi ð3Þ

When Pi = 0, let Pi log Pi = 0, where Pi is the probabil-ity of the gray levels in the infrared image, and �i is themean intensity of the infrared image.Equation 3 shows that the impact of pixels with gray

levels i on the weighted entropy can be reflected by a

certain weight. By defining it into i−�ið Þ2 , pixels that aresignificantly different from the local average can be givena big influence on the entropy, and the local area withpotential target can be better highlighted.Considering that SAR images are also nonuniform and

that graph entropy is insensitive to the imaging sensorand geometric distortion, in this paper, WIE is importedto the detection stage of SAR images.Analogous to the infrared image, the variance WIE of

SAR images can be expressed as Equation 3 as well, butwith the denotations of Pi revised as the probability ofthe gray levels in the SAR image and Pi as the mean in-tensity of the SAR image.To verify the conjecture, the value of WIE from a real

SAR image in its uniform area, area including the upsand downs, and area which contains the target is tested.The result is shown in Figure 1.

2.2 Target extraction by a location-segmentation strategyBriefly, this average WIE algorithm based on ‘position-ing-segmentation’ is reflected in a ‘rough-fine’ structure:First of all, extract blocks pieces which may contain thetarget through ROIs [11]. Then, refine the ROIs and

conduct the variational segmentation. In fact, the roughextraction process involves multiple iteration and imageupdating. The accurate ROIs are gained through con-tinuous reciprocating operational steps in the local parttoo.As mentioned before, one-dimensional graph entropy

can only represent global information rather than theproperty of its spatial relationships. In order to obtainthe target position information, the SAR image is dividedinto pieces, using both global WIE as well as local WIEto reflect the information.Here is the consolidated flowchart of the whole algo-

rithm (Figure 2).

2.2.1 PretreatmentFirstly, measure the size of the input image F and denoteit by X × Y Due to the fact that an averaging segmenta-tion method is proposed to the subsequent stage of ROIrough extractions which demands for the size of theimage, the image boundary needs to be intercepted orexpanded.Suppose the closest power of two items for X is 2M

and closest power of two items for Y is 2N, then theimage boundary is intercepted or expanded into2M × 2N. After that, the global WIE from the pretreatedimage F is got through formula (3).According to the conjecture of SAR images prelimin-

arily verified before, areas which contain a target have ahigher WIE value, so the SAR image is divided intoblocks with certain size. Serving as potential blocks forROIs, the seed block can be gained through getting itslocal WIE and a given threshold. Particularly, in theROI detection algorithm, the idea of ROI iteration isintroducedFor images that contain multiple objectives, in order

to get the complete ROIs through iteration, a globalthreshold Th is needed to be set as follows:

Th ¼ k � E Hð Þ ð4Þ

where E(H) is the mean of the variance WIE values ofthe whole image, and k is a constant which defines thenumber of detected target. An inappropriate value of kwill cause the phenomenon of false alarm or missedalarm. The experience range of k is [0.5, 3].

2.2.2 Rough extractionAfter pretreatment, the size of the image F has been ad-justed to 2M × 2N in the following steps:Step 1 Evenly divide image F into four subregions of

2M − 1 × 2N − 1 and label them (Figure 3).Step 2 For subregions from 1 to 4, calculate their WIE

value respectively.

(a)

(b)

(c)

(d)Figure 1 The WIE value of a real SAR image in different areas. (a) The original image. (b) A uniform area, WIE = 0.58. (c) An area includingthe ups and downs, WIE = 0.76. (d) An area which contains the target, WIE = 0.82. The sizes of (b,c,d) are all 100 × 100 pixels.


Step 3 Label the subregion with maximum WIE valueand denote its value into Hmax.Step 4 Get the mean μ and the standard deviation σ of

the labeled subregion in step 3, so the subthreshold Htof it is measured as follows:

Ht ¼ μþ α� σ ð5Þ

where α is a constant which defines the size of the seedblock. If the value of α is too small, it may result in toosmall sized ROIs, which cannot contain the entire tar-gets, and a too big α may lead to too big sized ROIs,which contains too much background. The range of α isset as [0.5, 3].Step 5 Judge whether Hmax is greater than Ht, if Hmax ≥

Ht, then the labeled area in step 3 is a seed block; ifHmax <Ht, let m =m/2, n = n/2 and repeat step 1 to step4 until the maximal value in the variance weighted infor-mation SAR image satisfies Hmax ≥Ht, m = 2, or n = 2.The effect of iteration is similar to the following

(Figure 4):Step 6 Rough extraction of ROI - judge the seed block

attended by step 5 again; if Hmax > Th, the seed block istruly a ROI and then go to step 7; otherwise, the ROIdetection stops.Step 7 Update image - the gray values of the pixels in

the ROI are set to the mean gray value, and then go backstep 1 using the updated image.

InputSAR image

PretreatmentRough extraction

of ROI

Figure 2 Consolidated flowchart of the whole algorithm.

2.2.3 ROI adjustmentThe averaging segmentation based on mean WIE canroughly determine the location of ROIs; however, due totheir restrictions, targets are often not completely includedin the ROI or are not completely at the center of the ROI.To get an effective segmentation in the next stages, thispaper proposes a series of processing methods, includingfalse alarm exclusion algorithm, rectangular-completingalgorithm of ROI, and centroid alignment algorithm to ad-just the ROI.First of all, a false alarm exclusion algorithm based on

the knowledge of target size is applied to all ROIs in theimage. Among ROIs, whose area is less than the settingvalue, level will be ruled out.Next, a rectangular-completing algorithm of ROIs is

conducted, adjusting ROI with irregular shape to a rect-angle (Figure 5).Finally, let the geometric center of the ROIs be aligned

with the mass center of the corresponding area on theimage F.In a two-dimensional space, the coordinate formula of

the mass center is expressed particularly as

rm ¼X

mirimi

ð6Þ

where r ∈ [x, y]; mi denotes the weighting of pixel i; andri denotes the coordinate of pixel i;

ROI AdjustmentVariational

SegmentationOutput

SAR image

Figure 3 Subregions of image F.


Let rg be the geometric center of a piece of ROI, thenthe displacement distance v is as follows:

v ¼ rm − rg ð7Þ

Repeat the steps above until that v is less than a cer-tain standard (two units for instance).

3 SegmentationWhen ROIs are selected and adjusted, targets are ex-tracted from ROIs with image segmentation approach.Due to the impact of speckle noise, SAR image segmen-tation is known to be a difficult problem. In this paper,this problem is addressed with a variational SAR imagesegmentation approach which mainly involves two as-pects: establishing a reasonable energy functional to de-scribe SAR image characteristics and finding effectiveoptimization methods of the energy functional [12].

1 2

3 4

Figure 4 The schematic diagram of iteration.

3.1 Energy functionalThe definition of the energy functional is the key pointto the level set segmentation, which shall be able to ex-press the characteristic of SAR images properly and re-flect the differences between target and background. Sowhen the target and the background are separated, theenergy functional could reach the minimum value [12].For variational segmentation based on partial differentialequation, its energy functional has the general form asfollows:

E ¼ ED þ λEP ð8Þwhere ED is the energy term driven by the characteristicof SAR images which is usually defined through regionalprobability statistics or texture information of the SARimages, and EP is the regular energy term based on apriori constraint information [13,14].In the framework of probability and statistics, assume

that the characteristic of the two areas within a ROIunder segmentation obeys the probability density func-tion: pi(I(x); θi) respectively. Let I(x) :Ω→ R be the in-tensity SAR image, where Ω ⊂ R2 is the image domain.i ∈ {T, B} represents either target or background. θi, i =1,…,N is the parameter vector of distribution. Mean-while, define the regional information functions to beri(x;Ωi) = − log pi(I(x); θi), i = 1,…,N so that the regionalenergy term shall be [15].

ED ¼ −X

i∈ T ;Bf gβi

ZΩi

log pi I xð Þ; θið Þdx

¼XN

i∈ T ;Bf gβi

ZΩi

ri x;Ωið Þdx ð9Þ

By selecting different probabilistic models, different re-gional energy items can be defined and distinct SARimage segmentation methods can be gotten as well. Inthis paper, G0 distribution is adopted [15,16] so that theregional information function can be defined as

1 2

3 4

Figure 5 The schematic diagram of rectangular completing.


rG0

i xð Þ¼ − log pG0

i x; αi; γ i; Li� �¼ − log

LiLiΓ Li−αið Þγαii Γ Lið ÞΓ −αið Þ

� �

− Li− 1ð Þ log I xð Þð Þ; i ¼ 1;…;N þ Li−αið Þ log γi þ LiI xð Þ� �ð10Þ

As for EP, a common definition of it is the length ofthe boundary curve C:

EP ¼ZCds ð11Þ

where ds is the unit arc length, which if minimized, thesplit curve can been smoothed, small isolated areas canbe eliminated, and boundary information could alsobeen brought in. The boundary indicator function is de-fined as follows:

e xð Þ ¼ 11þ ∇ Gσ � I xð Þð Þj j ð12Þ

where Gσ is a Gaussian kernel function, convolving withI to enhance the robustness of edge detection againstnoise. Also, the boundary energy can be defined throughother edge detection algorithms, for instance, algorithmsbased on ratio operator, ROEWA operator, and so on.So the energy term can be defined as follows:

EP ¼ZCeCi sð Þds ð13Þ

where eCi sð Þ represents the value of e(x) on the boundarycurve Ci. When the active curve moves towards theedge, both the accuracy of edge location and thesmoothness of the curve are achieved by importing theedge information.With energy terms based on region and edge informa-

tion, the energy functional of the segmentation of ROIscan be get as follows:

E ¼X

i∈ T ;Bf gβi

ZΩi

ri x;Ωið ÞdxþZCeCi xð Þds ð14Þ

3.2 Minimization of the energy functionalUsually, an appropriate choice to minimize the prede-fined energy functional of SAR image segmentation is

necessary. In this paper, two means are adopted: level setmeans and fast optimization methods based on dualsolution.By bringing level set function ϕi :Ω→ R, i = 1,…,N in

formula (14), different areas can be represented as

ϕ xð Þ > 0 if x ∈Ωi

ϕ xð Þ < 0 if x ∉Ωi

ϕ xð Þ ¼ 0 if x ∈Ci

8<: ð15Þ

with Heaviside function H(ϕi) and Dirac function δ(ϕi):

H zð Þ ¼ 12

1þ 2πarctan

zε

� �� ; δ zð Þ ¼ H 0 zð Þ

¼ 1π

ε

ε2 þ z2ð16Þ

If ϕi > 0, then H(ϕi) ≈ 1; otherwise, if ϕi < 0, then H(ϕi) ≈ 0, where ε is usually a constant with small value tocontrol the speed for H(ϕi) to change from 0 to 1. Withno information of a priori probabilities, the energy func-tion can be represented as

E ¼X

i∈ T ;Bf g

ZΩH ϕið Þri x;Ωið Þdx

þXNi¼1

λi

ZΩe xð Þ ∇H ϕið Þj jdx ð17Þ

If distribution parameters θi, i = 1,…,N are determined

according to variational principle ∂ϕi∂t ¼ − ∂E

∂ϕi, the curve

evolution equation can be get which makes the energyfunctional minimal:

∂ϕi

∂txð Þ ¼ δ ϕi xð Þð Þ

� λdiv e xð Þ ∇ϕi xð Þ∇ϕi xð Þj j

� �þ ri xð Þ

� �ð18Þ

Let n be the number of iterations and τ be the step oftime, then the iteration of level set function can be

ϕinþ1 xð Þ ¼ ϕi

n xð Þ þ τ∂ϕn

i

∂txð Þ ð19Þ

By selecting different probabilistic models, different re-gional energy items can be defined and distinct SARimage segmentation methods can be gotten as well.Although level set segmentation can get a satisfactory

result, its evolution speed is relatively slow. To promotethe efficiency of the segmentation of SAR images andenhance the real-time property of the system, this paperdoes further research on iteration method based onSplit-Bregman algorithm [17,18].Since the energy function in (17) is nonconvex, the

level set evolution equation in (18) is likely to fall intolocal minima, which brings about erroneous segmenta-tion results.


So to achieve a fast SAR image segmentation globally,the energy function of SAR image segmentation shall bedesigned as a convex function with a global minimum.For the level set evolution equation in (19), the termin-ation condition of evolution is as follows:

δ ϕið Þ αdiv eCi xð Þ ∇ϕi

∇ϕij j� �

þ ri xð Þ� �

¼ 0 ð20Þ

Formula (20) can be seen as the minimization condi-tion of the convex energy functional:

EG x;uið Þ ¼ α

ZΩeCi xð Þ ∇uij jdxþ

ZΩri xð Þui½ �dx; ui ∈ 0; 1½ � ð21Þ

The energy functional in (21) is convex, with globalminimum. Because formula (20) and (18) are actuallythe same conditions, the minimum of (14) can be get bysolving the minimum in (21), which, in the meantime,

Input SARimage

Pretreatment

Rough extraction ofROI

ROI adjustment

Variationalsegmentation

Output SARimage

Calculate the global thresh

Set the size of sub-images in

When m>2 and n>2

Split the image domain intoimages of size m×n

Calculate the variance WIE veach sub-image

Select out the sub-image

WIEmax

Calculate its sub-thresh

WIEmax>Ht?

WIEmax>Th?

end

YE

NO

NO

Figure 6 Architecture of the proposed fast target detection method of

can be proved to be equivalent to the followingminimization problem:

minui∈ 0;1½ �

ZΩ

eCi xð Þ ∇uij j þ λei xð Þuið Þdx ð22Þ

It is hard to solve for the partial differential, becausein formula (22), it has both the level set function ui andits first order differential form |∇ui|. To achieve quickly,let ⇀di ¼ ∇ui so formula (22) can be transformed intothe following constraint minimization problem:

minui∈ 0;1½ �

ZΩ

eCi xð Þ dVi þ λei xð Þui

� �dx sub d

V

i ¼ ∇ui

ð23ÞWith Lagrange multiplier method, the problem in (23)

can be transformed into the following unconstrainedminimization problem:

old Th

to m×n

sub-

alue of

with

old Ht

Set the gray value of ROI into themean value of the region

m=m/2,n=n/2

Mark the location of ROI

S

YES

NO

high-resolution SAR image based on location-segmentation stages.

minui∈ 0;1½ �

ZΩ

eCi xð Þ dVi þ λei xð Þui

� �þ η

2dV

i−∇ui�� 2

2dx ð24Þ d

V

ik þ 1; uikþ1� �

¼ minui∈ 0;1½ �; d

V

i

ZΩ

�eCi d

V

i

k

þ λeiuik�

η V k V k�� 2 V kþ1

8>>>><


þ2

di −∇uik−bi�� dxbi

¼ bV

i

kþ ∇uikþ1−d

V

i

kþ1Þ; k > 0

>>>>:ð25Þ

It can be proved that the problem in (24) could be

solved through the iteration method of Bregman by

introducing the error vector bV

i ¼ dV

i − ∇ui:

a the original S

b ROIs after ro

ROIs adj

d variational se

c

Figure 7 Target detection results of simulated images. (a) Original SATVariational segmentation results.

AR image

ugh extraction

ustment

gmentation

image. (b) ROIs after rough extraction. (c) Adjustment of ROIs. (d)


The first equation above can be solved in two steps.Firstly, get the variational calculus of u and make itequal to 0:

ηΔuikþ1 ¼ λei þ ηdiv b

V

i

k−d

V

i

k� �

; ui ∈ 0; 1½ � ð26Þ

Discretize the Laplacian operator and divergence oper-ator as follows:

Δuis;t ¼ uisþ1;t þ uis;tþ1 þ uis−1;t þ uis;t−1−4uis;tdiv pVi

� �s;t

¼ Vp ixs−1;t −

Vp ixs;t þ V

p iys;t−1 −

Vp iys;t

ð27Þ

where pVix; pViy are two components of the two-dimensional vector pVi . In order to distinguish betweenthemselves, let ui = ui and solve (26) through the iter-ation of Gauss-Seidel:

uukþ1s:t ¼

uiks−1:t þ uiksþ1:t þ uiks:t−1 þ uiks:tþ1−div bV

i

k� �

s;tþ div d

V

i

k� �

s;t−λ

ηei;s;t

4uikþ1 ¼ max min uukþ1; 1

� �; 0

� �ð28Þ

where the second expression above is to meet the condi-tions that ui ∈ [0, 1]. To solve the minimization problemfor d

V

i

kin the first expression, we have

the original SAR image

ROIs adjustment

a

cFigure 8 Target detection results of real SAR image. (a) Original SAR im(d) Variational segmentation results.

dV

i

kþ1¼ shrinkeCi b

V

i

kþ ∇uikþ1; η

� �

¼ bV

i

kþ ∇uikþ1

bV

i

kþ ∇uikþ1

max b

V

i

kþ ∇uikþ1

−η−1eCi ; 0

� �

ð29ÞSo the iterative calculation of (25), (28), and (29) can

achieve the purpose of fast solution of the minimizationof (14) and the segmentation of ROI.

3.3 The overall processIn summary, the architecture of the proposed fast targetdetection method of high-resolution SAR image basedon location-segmentation stages is shown in Figure 6.

4 Experiments and resultsFigure 7 shows a group of synthetic images. Due to thefact that the positions of the targets are already known,it is used to the test algorithm. Figure 7a shows threesimulated SAR images which contain five different tar-gets by using the MSTAR measured data respectively.After extracting ROIs from the SAR image based on thevariance WIE on first stage, the location results areshown in Figure 7b. It can be seen that the methodachieves comparatively good positioning of the targetarea. Nevertheless, the irregular shape of the ROIs andthe fact that some targets are lying at the boundary ofthe ROIs are not conducive to subsequent processing.

ROIs after rough extractionb

d variational segmentation

age. (b) ROIs after rough extraction. (c) Adjustment of ROIs.


Hence, the rectangular-completing algorithm of ROI andcentroid alignment algorithm area conducted to refinethe ROI region. The results in Figure 7c include twotypes of lines which are red and green; the former typeshows the outline of original ROIs without any furtherprocessing, and the latter one sketches the outline of ROIsafter the rectangular-completing stages and the centroidalignment stages which confirm that the target areas aregetting better positions. For the extracted ROIs, a segmen-tation algorithm based on variational model is adoptedto accurately extract the target. The results are shown in

(a) the original SAR image

(c) ROIs adjustment

(e) traditional CFAR method (f) CFAR+ mor

operati

Figure 9 Comparison between the proposed methods and the traditiextraction. (c) Adjustment of ROIs. (d) Variational segmentation results. (e)(g) Two-stage target detection method.

Figure 7d with all targets preferably positioned and ex-tracted and the shape information remains relatively intact.Figure 8 shows a group of tanks in the wild which con-

tains trees, grass, road, tanks, military facilities, and othercomplex objects types. Figure 8a defines the original SARimage of 512 × 1,024 pixels. The yellow lines in Figure 8bsketch the outline of ROI that has deformity shapes afterrough extraction. Figure 8c includes two types of lineswhich are red and green; the former type shows the out-line of ROIs after the rectangular-completing stages, andthe latter one sketches the outline of ROIs after the

(b) ROIs after rough extraction

(d) variational segmentation

phological

on

(g) two-stage target detection

method

onal CFAR methods. (a) Original SAR image. (b) ROIs after roughTraditional CFAR methods. (f) CFAR with morphological operation.


centroid alignment stages. The obvious changes betweenFigure 8b and Figure 8c verified the effectiveness of ROIadjustment. The last picture in Figure 8 shows the outputimage after variational segmentation. The whole programtakes 6.93 s in this picture.In order to further illustrate the superiority of this pro-

posed method in Figure 9, a comparison between it andthe traditional CFAR method is shown. For the details ina specific object, it can be seen clearly in Figure 9e thatthe result from the traditional CFAR algorithm is incom-plete with some parts of a same object been detecteddisconnectedly. Although these discrete parts can getconnected by conducting morphological operation inFigure 9f, the shape distortion of the target is rather ser-ious. Figure 9g shows the detection result from the pro-posed method with idea extraction of the ROIs, and theshape of the targets is comparatively complete with ac-curate shape information.

5 ConclusionsThis paper proposed a novel two-stage target detectionmethod for high-resolution SAR images. Since the vari-ance WIE characteristic has been preliminarily verifiedto measure the complex degree of the SAR images, thedefects for traditional CFAR algorithm to conduct detec-tion for SAR images with high resolution have beenovercome. Besides, the adjustment stage which includesthree parts guaranteed the precise segmentation of ROIsconsequently. Moreover, the variational segmentationstage gave rise to a better outcome compared with trad-itional level set segmentation method by running faster andgiving more accurate result. The experiment in Section 4verified the effectiveness of our algorithm.

AbbreviationsATR: automatic target recognition; CFAR: constant false alarm rate; ML: meanlevel; ROIs: regions of interest; SAR: synthetic aperture radar; WIE: varianceweighted information entropy.

Competing interestsThe authors declare that they have no competing interests.

AcknowledgementThis work was supported in part by the National Natural Science Foundationof China under Projects 60802065, 61271287 and 61371048.

Received: 7 January 2014 Accepted: 14 March 2014Published: 3 April 2014

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doi:10.1186/1687-6180-2014-45Cite this article as: Cao et al.: Fast target detection method forhigh-resolution SAR images based on variance weighted informationentropy. EURASIP Journal on Advances in Signal Processing 2014 2014:45.

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