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8/16/2019 Aili2006 Pending http://slidepdf.com/reader/full/aili2006-pending 1/4 SAR Image Compression Using V Model WANG Aili, ZHANG Ye, GU Yanfeng Department of Electronics and Communication Engineering, HarbinInstitute of Technology Abstract-Generally synthetic aperture radar (SAR) image compressionmethodsbased on wavelet t r a n s f o r m remove statistic redundancy of image data and neglect visual redundancy. In view of this problem, a new image compression method based on human visual system(HVS) is proposed in this paper. First SAR image is decomposed by wavelet transform, then wavelet coefficients in different subbands are weighted by the peak of contrast sensitivity function (CSF) curve in wavelet domain, at last set partitioning in hierarchical trees (SPIHT) algorithm is used to code the weightedwavelet coefficients to f o r m embedded bit stream. Compression results show that comparing with conventional SPIHT algorithm, the methodproposed in this paper gets better subject visual quality at the same compression ratio with almost equivalent objective evaluationresults. Key words: SA R image compression; human visual system(HVS); contrast sensitivity function(CSF); set partitioning in hierarchical trees(SPIHT) algorithm. I. INTRODUCTION Synthetic aperture radar (SAR) is a remote sensing technology that uses the motion of the radar transmitter to synthesize an antenna aperture much larger than the actual antenna aperture in order to yield high spatial resolution radar images. In the last few years, high-quality images of the earth produced by SAR systems have become increasingly available. And SA R systems are developing from Single waveband, singlepolarization, fixed incidence to multi-waveband, multi-polarization, changeable incidence and multi-mode. However, while the volume of image data collected by SA R systems is increasing rapidly, the ability to transmit them to the ground, or to store them, is not increasing so fast. Thus, there is astrong interest in developing data compression algorithms that can obtain higher compression ratios, while keeping image quality to an acceptable level for SAR imagedata. Wavelet transform has good local time and frequency analysis characteristic, structure similarity between subbands and energy concentration, so it is widelyused in image compression. The most representative image compression algorithm is the embedded zerotree wavelet EZW)E1 algorithm proposed by Shapiro through using zerotree to encode wavelet coefficients whichequal zero or approachzero and forming embedded bit easy to control compression ratio. Set partitioning in hierarchical trees (SPIHT) 21 algorithm proposed by Said and Pearlman efficiently utilized important coefficients similarity among different scale subbands and can obtain better performance than EZW at the samecompression ratio. The algorithms all aboveremoved statistic redundancy using wavelet transform neglecting visual redundancy. The method we proposedmainly focuses on removing visual redundancy at the meantime removing statistic redundancy in SAR image compression to increase subject quality of recontrusredimage. At first, wavelet transform is applied to SAR image, then each subband is weighted according to frequency sensitivity in human visual system(HVS) model and at last wavelet coefficients are coded by SPIHT algorithm. Experimental results showed that at the same compression ratio, the method proposed in this paper increases reconstructed image visual quality and keeps image edge and texture information efficiently. II. SAR IMAGE CHARACTERISTIC ANALYSIS SA R is a high resolution coherent imagingsystem and inherent multiplicative speckle noise increases entropy, severely affecting image visualinterpretation and compressibility. There are many scatter points in a space resolution cell in SAR system, so echo wave is vector sum of themwhich have different distances to receiver. Thus echo intensity is not determined by ground object scatter coefficients, but has great random fluctuation around scatter coefficients[3]. In SAR image, this appears that homogeneous region has non-uniform gray value as strong noise which we called speckle noise. Thus each pixel in SA R image can be expressed as a multiplicative product of backscatter intensity and speckle noise: I(i, j) = R(i, j) S(i, j) 1) where I is image containing noise, R is ground object scatter intensity and S is speckle noise produced during fading process which separately submit F distribution, single sideband exponential distribution and Gamma distribution and R is independent of S. In SA R image compression, it is waste to spend bits coding speckle noise. In order to get reconstructed image with high quality, we should use most possible bits to express useful information at a given compression ratio. By comparison of wavelet coefficients of image without and with speckle noise, it can be find that difference between them in low frequency is small but in high frequency is large, this sufficiently illustrates speckle noise distributes in high frequency subband. Reference [4] also analyzed in homogeneousregion, speckle noise is principal components of high frequency in wavelet domain. So coarse quantization is applied to speckle noise in high This work was supported in partly by the China National Science Foundation under Grant CNSF 60472048. 0-7803-9582-4/06/ 20.00 c2006 IEEE

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Page 1: Aili2006 Pending

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SAR Image Compression Using V Model

WANG A i l i ,ZHANGY e ,

GUYa n f e n g

D e p a r t m e n t o fE l e c t r o n i c sa n d C o m m u n i c a t i o nE n g i n e e r i n g ,H a r b i nI n s t i t u t eo f T e c h n o l o g y

A b s t r a c t - G e n e r a l l ys y n t h e t i c a p e r t u r e r a d a r ( S A R ) i m a g ec o m p r e s s i o nm e t h o d sb a s e d o n w av el e tt r a n s f o r m removes t a t i s t i cr e d u n d a n c y o f i m a g e d a t a a n d n e g l e c t v i s u a lr e d u n d a n c y . I n v i e w o f t h i s p r o b l e m , a new i m a g ec o m p r e s s i o nm e t h o db a s e d o n human v i s u a ls y s t e m( H V S )i sp r o p o s e di n t h i sp a p e r . F i r s tSAR i m a g e i s d e c o m p o s e d b yw a v e l e t t r a n s f o r m , t h e n w a v e l e t c o e f f i c i e n t si n d i f f e r e n ts u b b a n d s a r e w e i g h t e db y t h e p e a k o f c o n t r a s t s e n s i t i v i t yf u n c t i o n ( C S F ) c u r v e i n w a v e l e t d o m a i n , a t l a s t s e t

p a r t i t i o n i n g i n h i e r a r c h i c a l t r e e s ( S P I H T )a l g o r i t h mi s u s e dt o c o d e t h e w e i g h t e dw a v e l e t c o e f f i c i e n t st o f o r m embeddedb i t s t r e a m . C o m p r e s s i o nr e s u l t ss h o w t h a t c o m p a r i n gw i t hc o n v e n t i o n a lSPIHT a l g o r i t h m , t h e m e t h o dp r o p o s e di n t h i sp a p e r g e t s b e t t e r s u b j e c t v i s u a lq u a l i t ya t t h e samec o m p r e s s i o n r a t i o w i t h a l m o s t e q u i v a l e n t o b j e c t i v ee v a l u a t i o nr e s u l t s .

K e y w o r d s : SA R i m a g e c o m p r e s s i o n ; human v i s u a ls y s t e m ( H V S ) ; c o n t r a s t s e n s i t i v i t yf u n c t i o n ( C S F ) ; s e tp a r t i t i o n i n g i nh i e r a r c h i c a l t r e e s ( S P I H T )a l g o r i t h m .

I . I N T R O D U C T I O N

S y n t h e t i ca p e r t u r er a d a r( S A R ) i s a r e m o t e s e n s i n gt e c h n o l o g yt h a tu s e s t h em o t i o no f t h er a d a rt r a n s m i t t e rt os y n t h e s i z ea n a n t e n n a a p e r t u r em u c hl a r g e rt h a nt h ea c t u a la n t e n n a a p e r t u r ei n o r d e rt o y i e l dh i g hs p a t i a lr e s o l u t i o nr a d a ri m a g e s .I nt h el a s tf e w y e a r s ,h i g h - q u a l i t yi m a g e so ft h e e a r t hp r o d u c e d b y SAR s y s t e m s h a v e b e c o m ei n c r e a s i n g l ya v a i l a b l e .And SA R s y s t e m s a r ed e v e l o p i n gf r o m S i n g l ew a v e b a n d ,s i n g l e p o l a r i za t i on ,f i x e di n c i d e n c et o m u l t i - w a v e b a n d ,m u l t i - p o l a r i z a t i o n ,c h a n g e a b l ei n c i d e n c ea n d m u l t i - m o d e .H o w e v e r ,w h i l e t h ev o l u m eo fi m a g ed a t ac o l l e c t e db y SA R s y s t e m si s i n c r e a s i n gr a p i d l y ,t h ea b i l i t yt o t r a n s m i tt h e mt o t h eg r o u n d ,o rt o s t o r et h e m ,i s n o t i n c r e a s i n gs of a s t .T h u s ,t h e r ei s a s t r o n gi n t e r e s ti nd e v e l o p i n gd a t ac o m p r e s s i o na l g o r i t h m st h a tc a n o b t a i nh i g h e rc o m p r e s s i o nr a t i o s ,w h i l e k e e p i n g i m a g eq u a l i t yt oa n a c c e p t a b l el e v e lf o rSAR i m a g ed a t a .

W a v e l e tt r a n s f o r mh a s g o o dl o c a lt i m e a n d f r e q u e n c ya n a l y s i sc h a r a c t e r i s t i c ,s t r u c t u r es i m i l a r i t yb e t w e e ns u b b a n d sa n d e n e r g y c o n c e n t r a t i o n ,s o i t i s w i d e l yu s e d i ni m a g e c o m p r e s s i o n .T h e m o s t r e p r e s e n t a t i v ei m a g ec o m p r e s s i o na l g o r i t h mi s t h ee m b e d d e dz e r o t r e ew a v e l e t E Z W ) E 1a l g o r i t h mp r o p o s e db y S h a p i r ot h r o u g hu s i n gz e r o t r e et o e n c o d ew a v e l e tc o e f f i c i e n t sw h i c he q u a lz e r o o ra p p r o a c hz e r o a n d f o r m i n ge m b e d d e db i te a s y t o c o n t r o lc o m p r e s s i o nr a t i o .S e tp a r t i t i o n i n gi n h i e r a r c h i c a lt r e e s( S P I H T )2 1 a l g o r i t h mp r o p o s e d b y S a i d a n d P e a r l m a ne f f i c i e n t l yu t i l i z e di m p o r t a n tc o e f f i c i e n t ss i m i l a r i t ya m o n g

d i f f e r e n ts c a l es u b b a n d sa n d c a n o b t a i nb e t t e rp e r f o r m a n c et h a nEZW a tt h es a m ec o m p r e s s i o nr a t i o .

T h e a l g o r i t h m sa l la b o v er e m o v e ds t a t i s t i cr e d u n d a n c yu s i n gw a v e l e tt r a n s f o r mn e g l e c t i n gv i s u a lr e d u n d a n c y.T h em e t h o dwe p r o p o s e dm a i n l y f o c u s e so n r e m o v i n gv i s u a lr e d u n d a n c ya t t h em e a n t i m er e m o v i n gs t a t i s t i cr e d u n d a n c yi n SAR i m a g e c o m p r e s s i o nt o i n c r e a s es u b j e c t q u a l i t yo fr e c o n t r u s r e d i m a g e .A t f i r s t ,w a v e l e tt r a n s f o r mi sa p p l i e dt oSAR i m a g e ,t h e ne a c h s u b b a n di s w e i g h t e da c c o r d i n gt of r e q u e n c ys e n s i t i v i t yi nh u m a nv i s u a ls y s t e m( H V S )m o d e la n d a t l a s t w a v e l e t c o e f f i c i e n t sa r e c o d e d b y S P I H Ta l g o r i t h m .E x p e r i m e n t a lr e s u l t ss h o w e dt h a ta t t h es a m ec o m p r e s s i o nr a t i o ,t h e m e t h o dp r o p o s e d i n t h i s p a p e ri n c r e a s e sr e c o n s t r u c t e di m a g e v i s u a l q u a l i t ya n d k e e p si m a g ee d g e a ndt e x t u r ei n f o r m a t i o ne f f i c i e n t l y.

I I . S A RIMAGEC H A R A C T E R I S T I CA N A LY S I S

SA R i s a h i g hr e s o l u t i o nc o h e r e n ti m a g i n gs y s t e ma n di n h e r e n tm u l t i p l i c a t i v es p e c k l e n o i s ei n c r e a s e se n t r o p y ,s e v e r e l ya f f e c t i n gi m a g e v i s u a li n t e r p r e t a t i o na n dc o m p r e s s i b i l i t y .

T h e r ea r emany s c a t t e rp o i n t si na s p a c e r e s o l u t i o nc e l li nSAR s y s t e m ,s oe c h o w a v ei sv e c t o rs u mo f t h e mw h i c h

h a v e d i f f e r e n td i s t a n c e st o r e c e i v e r .T h u se c h o i n t e n s i t yi sn o t d e t e r m i n e db y g r o u n do b j e c ts c a t t e rc o e f f i c i e n t s ,b u th a s g r e a tr a n d o mf l u c t u a t i o na r o u n ds c a t t e rc o e f f i c i e n t s [ 3 ] .I n SAR i m a g e ,t h i sa p p e a r s t h a th o m o g e n e o u sr e g i o nh a sn o n - u n i f o r mg r a y v a l u ea s s t r o n gn o i s ew h i c hw e c a l l e ds p e c k l en o i s e .T h u s e a c h p i x e li n SA R i m a g e c a n b ee x p r e s s e da s a m u l t i p l i c a t i v ep r o d u c to f b a c k s c a t t e ri n t e n s i t ya n d s p e c k l en o i s e :

I ( i ,j ) = R ( i ,j ) S ( i ,j ) 1 )

w h e r e I i s i m a g e c o n t a i n i n gn o i s e ,R i s g r o u n d o b j e c ts c a t t e ri n t e n s i t ya n d S i s s p e c k l en o i s ep r o d u c e dd u r i n gf a d i n gp r o c e s s w h i c h s e p a r a t e l ys u b m i t F d i s t r i b u t i o n ,s i n g l es i d e b a n d e x p o n e n t i a ld i s t r i b u t i o na n d Gammad i s t r i b u t i o na n d R i s i n d e p e n d e n to f S .

I n SA R i m a g e c o m p r e s s i o n ,i t i s w a s t e t o s p e n db i t sc o d i n gs p e c k l en o i s e .I n o r d e rt o g e tr e c o n s t r u c t e di m a g ew i t h h i g hq u a l i t y ,we s h o u l d u s e m o s t p o s s i b l eb i t st oe x p r e s su s e f u li n f o r m a t i o na t a g i v e nc o m p r e s s i o nr a t i o .B yc o m p a r i s o no f w a v e l e tc o e f f i c i e n t so f i m a g e w i t h o u t a n dw i t h s p e c k l en o i s e ,i t c a n b e f i n dt h a td i f f e r e n c eb e t w e e nt h e m i n l o w f r e q u e n c yi s s m a l l b u ti n h i g h f r e q u e n c yi sl a r g e ,t h i ss u f f i c i e n t l yi l l u s t r a t e ss p e c k l en o i s ed i s t r i b u t e si nh i g h f r e q u e n c ys u b b a n d . R e f e r e n c e [ 4 ]a l s oa n a l y z e d i nh o m o g e n e o u sr e g i o n ,s p e c k l en o i s e i s p r i n c i p a l

c o m p o n e n t so f h i g h f r e q u e n c yi n w av e le t d o ma in .S oc o a r s e q u a n t i z a t i o ni s a p p l i e dt o s p e c k l en o i s ei n h i g h

T h i s w o r kw a s s u p p o r t e di np a r t l yb y t h eC h i n aN a ti o n a l S c i e nc eF o u n d at i o n u n d e rG r a n tCNSF 6 0 4 7 2 0 4 8 .

0 - 7 8 0 3 - 9 5 8 2 - 4 / 0 6 / 2 0 . 0 0c 2 0 0 6I E E E

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f r e q u e n c yt o t r a n s m i tc o e f f i c i e n t so f e d g et o r e m o v ev i s u a lr e d u n d a n c yf u r t h e r .M o r e o v e r ,i nt e r m s o f SA R i m a g e , t h eh i g h e s tf r e q u e n c yi s w e i g h t e db y l e a s tv a l u e j u s ts h r i n k i n gw a v e l e tc o e f f i c i e n t so f s p e c k l en o i s ew h i c hd i s t r i b u t e si nh i g h f r e q u e n c y ,t h u sa l l o c a t i n gm o r e b i t st o e d g e s .T h i ss u b b a n dw e i g h t i n gm e t h o dc a n b e c o n s i d e r e dt o u s e CSF

m a s k t o m u l t i p l yw a v e l e t c o e f f i c i e n t s ,t h e nS P I H Ta l g o r i t h mi s u s e d t o p e r f o r m q u a n t i z a t i o na n d b l o c kd i a g r a mo f c o d i n gs c h e m ea ss h o w ni nF i g . 3 :

Image-> W a v e l e t SPIHT B i tD e c o m p o s i t i o n* 0 - Q u a n t i z a t i o n- S t r e a m

C S FMask

F i g u r e 3 . B l o c kd i a g r a mo f S P I H Tc o d i n gs c h e m ew i t h CSF w e i g h t i n g

I no r d e rt o t r a n s f o r mt h eCSF c u r v e w h i c hh a sr e l a t i v e l yu n i f o r m s p a t i a lf r e q u e n c y i n F i g u r e1 t o m a t c h w a v e l e tc o e f f i c i e n t s ,we p e r f o r ma w a v e l e t d e c o m p o s i t i o no f t h eCSF c u r v e .A t i - l e v e l d e c o m p o s i t i o n ,w e i g h t o fd i a g o n a ls u b s p a c ei s d e t e r m i n e db y t h ep e a k o f CSF c u r v e

i n w a v e l e t d o m a i n l a b e l e da s p i ; i f t h ep e a k o f l o w

f r e q u e n c y i s l a b e l e da s q , , w e i g h t o f h o r i z o n t a la n d

v e r t i c a ls u b b a n di s d e t e r m i n e db y p T h e l o w e s tf r e q u e n c y i s w e i g h t e db y t h ep e a k o f CSF c u r v e i n i t ss u b s p a c e .

V . E X P E R I M E N T A LR E S U L T SAN DA N A LY S I S

F o r o u r e x p e r i m e n t s ,we c h o o s ea s c e n e o f a r u r a la r e ai n J a p a n o b t a i n e db y P I - S A Rw i t h 3m r e s o l u t i o n( s i z eo f5 1 2X 5 1 2 ) ,s h o w ni nF i g u r e4 ( a ) .F i g u r e4 ( b )i s e n l a r g e dr e s u l to f o r i g i n a lSAR i m a g e i n u p p e r - r i g h ts q u a r e .T h es c e n e c o n t a i n sm a n yu r b a n f e a t u r e st h a tt y p i c a l l ya p p e a r i nh i g h - r e s o l u t i o na i r b o r n eSA R i m a g e r y .C o n s i d e r i n gt h ee f f i c i e n c yo f c o m p r e s s i o n a l g o r i t h m ,w e a d o p t e db i o r t h o g o n a l9 / 7w a v e l e t t o d e c o m p o s ea n d r e c o n s t r u c tSAR i m a g e a n d d e c o m p o s i t i o nl e v e li s c h o s e n a s f i v e .We i g h t sa r e1 1u n i q u e v a l u e :3 . 7 8 , 3 . 4 8 , 3 . 4 8 ,3 . 2 1 , 3 . 5 5 ,3 . 5 5 ,3 .4 8 , 5 . 3 0, 5 . 3 0 , 7 . 2 0,4 . 7 4 , 4 . 7 4 ,3 . 7 5 ,2 . 3 3 ,2 . 3 3 ,1 . 0 0i n c o r r e s p o n d e n c ew i t h e l e v e ns u b b a n d s f r o m l o wf r e q u e n c y t o h i g hf r e q u e n c y a n d a t t h e s a m e l e v e lt h ep r e c e d e n c es e q u e n c e i s h o r i z o n t a l ,v e r t i c a la n d d i a g o n a ls u b b a n d .

I no r d e rt o a s s e s st h e e f f ec t i v e n e s so f o u r c o m p r e s s i o na l g o r i t h mo n SA R i m a g e ,t w o s c h e m e sa r ea p p l i e d :o u rm e t h o dw i t h CSF w e i g h t i n ga n d c o n v e n t i o n a lS P I H T .T h er e s u l t so f e a c h t w o e n c o d i n g m e t h o d s a r e s h o w n i nF i g u r e . 4 ( c ) ,( d ) a t 0 . 5 b p p a n d t h e q u a l i t ya s s e s s m e n tp a r a m e t e r s a r eg i v e ni n Ta b l e I h e r ew e u s e PSNR t od e s c r i b e r e c o n s t r u c t e di m a g e s 'd e f l e c t i o nt oo r i g i n a li m a g e .I t c a n b e o b s e r v e dt h a to u r m e t h o dg e t sPSNRa l i t t l el o w e rt h a n c o n v e n t i o n a lS P I H Ta l g o r i t h m ,b u tv i s u a lq u a l i t yo fr e c o n s t r u c t e di m a g e a p p a r e n t l yo u t p e r f o r mS P I H T , f o re x a m p l ed i v i d i n gl i n e si n t h e f i e l d sp r e s e n tc l e a ra n dc o n t i n u o u s .

s p e c k l en o i s e .C o m p a r i n gt o S P I H T ,o u r m e t h o di n c r e a s e s3 E N L o n a n a v e r a g e i n d i c a t i n gt h i sm e t h o de f f e c t i v e l ys u p p r e s s e ss p e c k l e n o i s ed u r i n gc o m p r e s s i o np r o c e s s .

TA B L EI . COMPARISONO F PSNR DB) RESULTSWITHD I F F E R E N TALGORITHMS

I I ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~Method

Bi t R a t e ( b p p )1 . 0 0 0 . 5 0 0 . 2 5 0 . 2 0

CSF o f f 2 7 . 5 0 2 4 . 7 1 2 3 . 2 3 2 2 . 7 5CSF o n 2 6 . 9 1 2 4 . 4 5 2 2 . 9 6 2 2 . 5 9

a b )

C ) U I )

i g u r e4 . ( a )O r i g i n a lSA R i m a g e . ( b )C l o s e - u po f o r i g i n a li m a g ei nupper r i g h tcorner. c )R e c o n s t r u c t e di m a g ew i t h o u tCSF m a s ka t 0 . 5 b p p ,

P S N R = 2 4 . 7 1 d B .( d )R e c o n s t r u c t e di m a g ew i t h CSF m a s ka t0 . 5 b p p ,P S N R = 2 4 . 4 5 d B .

V I . C O N C L U S I O N

T h i s paper a n a l y z e d h u m a nv i s u a lc h a r a c t e r i s t i ca n dw e i g h t e d w a v e l e t c o e f f i c i e n t si n d i f f e r e n ts u b b a n d sa c c o r d i n g t o CSF f u n c t i o n t o c o m p l e t e SA R i m a g ep r o g r e s s i v et r a n s m i s s i o na n d c o m p r e s s i o n .E x p e r i m e n t a l

r e s u l t ss h o w e d t h a ta t t h esame c o m p r e s s i o nr a t i o ,t h i sm e t h o d ca n a c h i e v e b e t t e rs u b j e c tv i s u a lq u a l i t ya n de f f i c i e n t l yk e e p t e x t u r e a n d e d g e i n f o r m a t i o ns u p p r e s s i n gs p e c k l en o i s e a t a c e r t a i nd e g r e e .S i n c e SPIHT a l g o r i t h mu t i l i z e su n i f o r mq u a n t i z a t i o nt o e n c o d e ,t h ef u r t h e rr e s e a r c hmay f o c u s on u t i l i z i n gv a r i a b l eq u a n t i z a t i o ns t e pt o h i g he f f i c i e n t l ycompress SA R i m a g e s .

ACKNOWLEDGMENT

T h ea u t h o r sw o u l dl i k et o t h a n kC H E NYu s h if o ru s e f u ld i s c u s s i o n sa n d h e l pi np r o g r a m m i n g .

R E F E R E N C E S

M e a n t i m e ,w e a d o p te q u i v a l e n tn u m b e ro f l o o k s( E N L )t o m e a s u r e c o m p r e s s i o na l g o r i t h m s 'a b i l i t yt o s u p p r e s s

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[ 1 ] S h a p i r o J M , E m b e d e di m a g ec o d i n g u si ng z e ro t r e e so f w av e le tc o e f f i c i e n t s[ J ] .IEEE T r a n s o n S i g n a l P r o c e s s i n g , v o l 4 1 ( 1 2 ) :p p .3 4 4 5 - 3 4 6 2 ,1 9 9 3 .

[ 2 ] A . S a i d a n d W. A . P e a l r l m a n , A n e w, f a s t a n d e f f i c i e n ti m a g ec o de c b as e ds e tp a r t i t i o n i n gi nh i e r a r c h i c a lt r e e s ,IEEE T r a n s .o nC i r c u i t sa n d S ys te mf o rV i d e o Te c h n o l o g y ,v o l .6 , p p .2 4 3 - 2 5 0 ,J u n e 1 9 9 6 .

[ 3 ] K a n e v e v s k yM B . New s p e c t r a le s t i m a t ef o rSA R i l m a g i n go f t h eo c e a n , I n t e r n a t i o n a lJ o u r n a lo f R e m o t eS e n s i n g ,v o l1 0 ( 2 6 ) ,p p .3 7 0 7 - 3 7 1 5 ,2 0 0 5 .

[ 4 ] M a r c S , G i a n f a n c oD a n d K e i t h P B . A n a l y s i s o f s p ec kl e n o is ec o n t r i b u t i o no n w av e l et d ec o mp o s i t io n o fSA R i m a g e s , IEEET r a n s a c t i o n so n G eo s ci en ce a ndR e m o t eS e n s i n g ,v o l 1 1 (3 6 ) , p p ,1 9 5 3 - 1 9 6 2 , 1 9 9 8 .

[ 5 ] M a n n o sJ L a n d S a k r i s o n D J . T h e e f f e c t so f a v i s ua lf i d e l i t yc r i t e r i o no n t h e e n c o d i n g o f i m a g e s , .IEEE t r a n s a c t i o n so nI n f o r m a t i o nT h e o r y ,v o l2 0 ( 3 ) ,p p .5 2 5 - 5 3 6 ,1 9 7 4 .

[ 6 ] A l b a n e s iM, G r a z i aa n d G u e r r i r uF . An H V S - b a s e da d a p t i v ec o d e rp e r c e p t u a l l yi m a g e c o m p r e s s i o n , P a t t e r nr e c o g n i t i o n ,v o l 4 ( 3 6 ) ,p p 9 97 - 10 0 7 , 2 0 0 2.