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![SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model](https://pdfprof.com/Listes/16/22214-16document.pdf.jpg)
ISSN 0249-6399 ISRN INRIA/RR--5493--FR+ENG
apport de rechercheThèmeCOG
SARImageFilteringBasedonthe
Heavy-TailedRayleighModel
AlinAchim-ErcanE.Kuruoglu-JosianeZerubia
N°5493
February2005
UnitéderechercheINRIASophiaAntipolis
Heavy-TailedRayleighModel
ThèmeCOGSystèmescognitifs
ProjetAriana
Filtraged'ImagesRadarRSOFondésur
leModèledeRayleighàQueueLourde enleverlebruitdechatoiement.Contents
1Introduction4
2StatisticalmodelingofSARimages5
3AdaptiveMAPlteringofspecklenoise9
4ExperimentalResults14
5Conclusions18
6Acknowledgement18
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4A.Achim,E.E.Kuruoglu&J.Zerubia
1Introduction
limitationsofinfraredimagers. transform[11,12,13,14]. INRIA2StatisticalmodelingofSARimages
RCS.2.1Statisticsoflog-transformedspeckle
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components,respectively,onecanwrite: y(u;v)=x(u;v)(u;v)+(u;v);(u;v)2Z2(1) rewrite(1)as y(u;v)=x(u;v)(u;v)(2) functiononbothsidesof(2): logy(u;v)=logx(u;v)+log(u;v):(3)Expression(2)canberewrittenas
Y(u;v)=X(u;v)+N(u;v);(4)
2.1.1IntensityImage
INRIA pI()=LLL1eL
(L)(5) kI(1)=(L)log(L) kI(2)=(1;L)(6) pI(N)=LLeNLeLeN
(L)(7)2.1.2AmplitudeImage
pA(x)=2xpI(x2)(8)
kA(r)=(12)r~kI(r)(9)
pA()=2LL2L1eL2
(L)(10) kA(1)=12((L)log(L))
kA(2)=14(1;L)(11)
pA(N)=2LLe2NLeLe2N
(L)(12)RRn°5493
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2.2ThegeneralizedRayleighmodel
2.2.1SymmetricAlpha-StableDistributions
tion '(!)=exp(|! j!j)(13) locationparameter,and determinesGaussiandistribution.
2.2.2AHeavy-TailedRayleighmodel
INRIA p(x)=xZ 1 0 uexp( u)J0(ux)du(14) obtain p(x)=x 2 exp(x24 )(15) model p(x)=x (x2+2)3=2(16)
pA(x)=2xpI(x2).Thus,oneobtain
pI(x)=1
2Z 1 0 uexp( u)J0(upx)du(17) pA(X)=e2XZ
1 0 uexp( u)J0(ueX)du(18) pI(X)=eX
2Z 1 0 uexp( u)J0(ueX2)du(19)
whereX=lnx.3AdaptiveMAPlteringofspecklenoise
Y=X+N(20)
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012345670
0.1 0.2 0.3 0.4 0.5 0.6 0.7Data, x
P(x) a=0.5 a=1 a=1.5 a=2 =1.X(Y)=argmaxXPXjY(XjY)(21)
PXjY(XjY)=PYjX(YjX)PX(X)
PY(Y);(22)
INRIA =argmaxXPN(N)PX(X)(23) parametersXand cumulants.3.1.1Mellintransform
(s)=M[f(u)](s)=Z +1 0 us1f(u)du(24) f(u)=M1[(s)](u)=1 2jZ c+j1 cj1us(s)ds(25)Thetransform(s)existsiftheintegralR+1
0jf(x)jxk1dxisboundedforsomek>0,in
Transform[21,22]
Second-kindrstcharacteristicfunction
(s)=Z +1 0 xs1p(x)dx(26) (s)=log((s))(27)RRn°5493
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rthordersecond-kindmoments
~mr=dr(s) dsr s=1=Z +1 0 (logx)rp(x)dx(28)rthordersecond-kindcumulants
kr=dr(s) dsr s=1(29) asfollows ~k1=1 NN X i=1[log(yi)] ~k2=1 NN X i=1[(log(yi)^ ~k1)2](30) [0;1]as (f^ g)(y)=Z +1 0 f(x)g(y x)dxx=Z +1 0 f(yx)g(x)dxx(31) (s)=2s(s+1 2) s1(1s) (1s2)(32) second-kindcumulantsofthemodel kA(1)= (1)1 +log(2 1 kA(2)= (1;1) 2(33) INRIA followingexpressionsforthelog-cumulants kI(1)=2 (1)1 +log(4 2 kI(2)=4 (1;1) 2(34) metersand p p y(y)=Z +1 0 p yjx(yjx)px(x)dx=Z +1 0 p (y x)px(x)dxx=p^ px(35) kindcumulantsofthesameorderofxand[21] ky(r)=~kx(r)+~k(r)(36) ^=2v u u t (1;1) ~k(2) (1;L) =[exp(^ ~k(1)+2 (1)1 (L)+log(L)4]=2(37)
empiricallog-cumulantsin(30)weget ^=v u u t (1;1) ~k(2)14 (1;L) =[exp(^ ~k(1)+ (1)112( (L)log(L))
2](38)
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4ExperimentalResults
4.1SyntheticDataExamples
(S/MSE)ratio,denedas[25]:S=MSE=10log10(KX
i=1S 2i=KX i=1(^SiSi)2)(39) =(SS;dSdS)q
(SS;SS)(dSdS;dSdS)(40) (S1;S2)=KX i=1S1iS2i:(41)
INRIAENL=1ENL=3ENL=9ENL=12
MethodS=MSES=MSES=MSES=MSE
4.2RealSARImageryExamples
wasacquiredinApril1993byERS. meansofKuan,Frost,MAPandMBD[33]lters. theheavy-tailednatureofSARdata.RRn°5493
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(a) (b) (c) (d) (e) (f) tailedRayleighmodel. INRIA (a) (b) (c) (d) (e)RRn°5493
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