Ant Colony Fuzzy Clustering Algorithm Applied toSAR Image Segmentation
Li Chunmao', Wang Lingzhi2, Wu Shunjun31,3.National Lab. of Radar Signal Processing, Xidian University; 2.Xi'an Institute of Post and Telecommunications,
Xi'an, Shannxi, 710071, P.R. China
stucatg 163 .com, [email protected], [email protected]
Abstract A method of dynamic fuzzy clustering analysis
based on ant colony algorithm for SAR image segmentation
is proposed. The method confirms dynamically the
clustering number and center by the stronger fuzzy
clustering ability of ant colony algorithm. Texture feature of
SAR image is calculated according to gray level
co-occurrence matrix (GLCM), and the proper feature
vector is selected through statistic analysis. The
measurement SAR image segmentation experiment indicates
that the algorithm can segment the target fast and exactly,
and is an effective SAR image segmentation method.
Key words ant colony algorithm, fuzzy clustering, SAR
image segmentation, gray level co-occurrence matrix
I. INTRODUCTION
The interpreted SAR image in military applicationmainly encircles how to detect and identify syntheticobject from the image containing complex background,typical synthetic object is such as tank, vehicle, fleet andso on. To the detection and identification of this synthetictarget, common dealing method of SAR system firstlydetect interesting target area fast in the image containingcomplex background, called by ROI[1][2]. Then exact
segment is taken to obtain ROI in order to obtain theobject area from obtained ROI. Whether the segmentationis good will directly influence the characteristicsextraction of target and the target classification andidentification.
Ant colony algorithm[3][4] is a kind of bionicevolution one, and is a random searching method which isof discrete, parallel, robust, positive-going feedback andfuzzy clustering ability. It is successfully applied to suchassemble optimization question as traveler question andworkshop task dispatch. J.Casillas proposed an automaticstudy method for fuzzy rules using ant colony algorithm.The discrete and parallel characteristic of ant colony
algorithm is very practical to discrete SAR image. Thepath selection method based on probability is of wideapplication prospect in fuzzy clustering question.
II. ANT COLONY ALGORITHM
Ant Colony Algorithm is also called Ant Algorithm,which is a bionic evolution algorithm proposed by ItalianScholar M.Dorigo who is enlightened by path selectionbehavior of ants in theirs searching food process in 1992.It is found by observing that ants always can find anoptimal path to food source in searching food process.After the optimal path is interdicted, Ants can steer clearof obstacle quickly and find the optimal path again. Thekind ability of ants is produced through the informationexchange and mutual cooperation between ant colonies.Every ant releases a kind of information hormone duringrandom sashaying process, and this hormone volatilizesconstantly with time lasting. If there is more ants selectthis path, the hormone on this path will be strengthened.While every ant is of the ability to perceive thisinformation hormone strength, they will select the pathwith stronger information hormone, leading to added ants
selecting this path. Thus a positive-going feedback isformed.
A. The step ofAnt Colony Algorithm
The ecumenic step of ant colony algorithm appliedto solve problem is as follows:
1) Problem analysis: making the problem to besolved abstract, and giving the problem space parametersvariables specific implication.
2) Initialization: giving every variable the initialvalue, ants all wait in the hole for starting out to searchfood.
3) Optimal process: ants make a dynamicselection in accordance with given path length and
0-7803-9582-4/06/$20.00 c2006 IEEE
information hormone strength, and release informationhormone during moving.
4) Cessation conditions: if given conditions aremeet, The algorithm will cease; otherwise step onto 3.
Step 3 is an adaptive process, and it embodies theessence of ant colony algorithm: selecting mechanismand updating mechanism.
B. Mathematic Describe ofAnt Colony Fuzzy ClusteringAlgorithm
to the following formula:
phij (t) = pphij (t) +Aph (4)
Where, p is the waning extent of information
amount with time going on, Aphii is the augmentation
of path information amount in this circulation.
N
Aphi = E AphAk=l
Give the initial SAR image X, and look every pixel
Xj (j = 1,2,.* , N) as an ant, every ant stands for the
feature vector of the pixel. Image segmentation is the
process that these ants with different feature vector
searching food source. The distance of any pixel Xi to
Xi is di,, using euclidean distance to calculate:
m
di Z,Pk(Xik Xjk) (1)
k=l
Where, m is the number of feature vector, Pk is
weight factor, which is set in terms of the influence
extent of every feature vector of pixel to clustering.
r is set as clustering radius, ph as information
amount, Then:
phi = ldi.<r
The probability of the path Xito Xj isp:
ph (t)< (t)
pa
pha(t)<7 (t)scS
otherwise
j ES
Here, 17i (t) is apocalyptic
function, a , is respectively the influence
the accumulated information and apocalyptic
guida
factor
guida
tunction to path selection.
S = Xs dsj < r, s = 1,2, * , N} is ambulatory path
set.
With the ants moving, the information amount on
every path is changing. Through one circulation, the
information amount on every path is adjusted according
Aphfk is the information amount remained by the k
ant in this circulation.
C. Set ofthe Guidance Function
Guidance function embodies the resemblance extent
between the pixel and clustering center, and is expressedthrough the following formula:
1
111=-r
m
Pk(Xik Xjk)k=l
(6)
Here, r is clustering radius. The bigger theclustering radius is, and the bigger Guidance functionvalue is, with which the probability of this clusteringcenter becomes bigger.
III. SAR IMAGE FEATURES EXTRACTION
(2) A. Gray Level Co-occurrence Matrix
Texture gray level co-occurrence matrix[5] is a
statistics method based on 2-Order assembly condition
probability density function of image. It is different fromgray level statistics analysis, image pixel isn't considered
(3) separately, but the frequency between pixels with some
relations is described. Generally crudity and direction isthe most predominant characteristic when texture is
Lnce distinguished. Two characteristics are corresponding to
r of the step size d and direction 0 in gray level
ince co-occurrence matrix. Gray level co-occurrence matrix isdefined as follows:
P(h, k) = [p(h, k d, )] (7)
Its meaning is that the satisfied position condition on
the image is that step size is d and direction is 0, and
gray level is the emerging times of pixel h and pixel k
pairs at the same time. The paper select Contrast, Energy,
(5)
r~~~~~~~~~_.-._ __. _ __1 __
Entropy, Inverse Difference Moment as feature vector,defined as follows:
Contrast: CONTR = E (h - k)2 M(h, k) (8)h k
Energy: ASM = M(h,k)2 (9)h k
Entropy: ENTRO = E M(h, k) log M(h, k) (10)h k
Inverse Difference Moment:
IDM=ZZ M(h,k)h k (11)
M(h, k) is the normalization processing to
gray level co-occurrence matrix P(h, k).
M(h k)= ZP(h ,k) (12)
h k
B. Features Extraction
Selecting an 11 * 11 pixel window with step size as 1and direction as 0 degree, making statistic analysis toevery feature in the experimental area selected onexperiment image. The calculation of every feature isneeded to select several experiment samples. Result issuch as table I.
Table I. Typical Texture Feature Values
ASM CONTR ENTRO IDM
Field 0.0729 1.3000 -2.9617 0.6290
Village 0.0175 5.2424 -4.5546 0.4741
City 0.0849 2.6499 -3.5037 0.6568
Forest 0.1132 0.6396 -2.5506 0.7486
Road 0.1092 1.4383 -2.7940 0.6719
Runway 0.3402 0.3174 -1.3415 0.8426
Lawn 0.0869 1.0921 -2.7958 0.6598
different food source Cj according to (1). If di is
zero, then the membership of the pixel to this kind is 1,
otherwise if d.. < r, calculating guidance function
according to (6) and information amount to every path
through (4).
4) Calculating the membership of pixel according to
(3). Judge if he membership is bigger than A, if yes,
calculate information augmentation Aphi, according to
formula and update information amount. Update the j
kind clustering center referring to the following formula.
J is the number of element in the kind Cj:
(13)k=l
Otherwise, record the ant into set SS, SS is the pixel
assembly unclassified.
5) Calculating the distance of every kind. When
the distance is smaller than threshold 8, combine two
kinds into one, updating the new clustering center.
6) If there is still the pixel to be classified, return to
step 2. Otherwise end.
B. Experimental Results andAnalysis
IV. EXPERIMENT
A. Algorithm Flow
Initial SAR image as 256 colors gray chart, shown
in figure I(a) and figure 11(a).
1) Initializing the parameters a, ,6, phi , r .
2) Calculating every feature vector of pixel Xi in
accordance with (8)-(15).
3) Calculating the distance d f pixel Xi
Figure I(a). SAR image of airport runway(572 X 790)
Figure I(b). Result of segmentation
Figure 11(a). SAR image of fields(348 X 298)
Fig. I (a) and Fig. 11 (a) are two SAR images cut off,and they are respectively airport runway and field,resolution is 3 meter.
Fig. I (b) and Fig. II (b) are respectively the
segmentation results using ant colony fuzzy clustering
algorithm when a =1,/3 =1 , r = 50 X = 0.9,£ = 0.5. It is seen from figures that the proposed image
segmentation algorithm the detected edge is more
continuous, edge detail structure embodies good, and the
part with low gray level value is also detected, So the
segmentation result are more accurate. The segmentation
result in Fig. 11(b) is a little worse than that in Fig. 1(b) as
a result of speckle effect. So there will be a good result if
speckle reduction is taken before image segmentation.
V. CONCLUSION
Method of dynamic fuzzy clustering analysis basedon ant colony algorithm for SAR image segmentation isproposed in the paper. Experiment results verify theeffectivity of the algorithm. Ant colony algorithm are ofthe discrete, parallel, and fuzzy clustering abilities, andthese features are more applied to the further SARinformation extraction such as texture classification, edgedetection and so on.
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