Y usung-Gu Tae-Jon.Shi 300-3 1 Korea KAIST-KIT/EML Spckle r.duction has lay ben rec0gnIr.d as the u i n problem of coh.rent it. nn vast u j o r i t y of then todmipus. -r, are of a scalar nature s i q l y b c a u n n c t o r / u t r i x I u g h q data are so s p r u ud have hcor available ml w y -r m n a y . w. do haw ~c k data, m s taken with the raJh/JpL ~v -9 9 8 ~I M p l a r i s a t i m &bud (1.225 O a l SWSynthatic Warture Radar) system, have bem M& available to U. W. 1nwstig.t. the potential of M exclwiwly p 1 a r i r t r i c Ilvye filtering ;/ holch, which takes full advantage Of the m t r i x data provided on a pixel-ky-pfxel basis. ud cclp1-t~ the existing scalar cmtrast optbization and s p c k l e reduction t . c h d q u s . W. wish t o streaa f r a the outset that our so.1 Is contrast o p t b i m t i m wiAtkart the help of 1ncOh.rent ~r a q i n g w a r pixels/look, because of tha correspading loss of spatial or t a p o r a l resolution. 1. P " m I c rmMRm6 ilvqing ud mny pronssing techniqus hm b..n advmced t o w . r m i o l l d n g [ I ], w define the receivinq a n t e m tanina1s with the &+Is directed C a r d tha target (pixel1 as Shoun i n ( 2 rig 11 Note that i n SAR a p p l i u t i a u eh. r m i v e r and t r a n d t t e r are co-l&ated'tat' lvy te different anterms so that eh. situation is slightly bistatic (31. Ru received -$ ud tha tr.nrritted -5 uaws, together With tha antmm height h (polarization of the r m i v i n g a n t e m vhen used as tr-tt e r [ ) ] I , CM J 1 b polarIration ration r t~-~lE/E~l, .nd + ard I I are the relative and absolute phases.respectively [31, ud related by the 2x2 ccqlex Sinclair scatterinq mdtrlx IS1 origin of the mOrdiMte system a t the written as pluu mves in terms of the caplex $ -lSl+ . (1) % voltage a t the receiving antenna terminals as a function of transmitter and receiver p l a r i u t i a u is given by v . $5 -$IS& , 121 where suparrcript f denotes the transpose (as oppcsed to hernutian conjuqate . +. , . n 12, pp. 1471-14731, (31 for details). Since a Coqlete scattering mtrix ( S I W ) ] is available for w e r y pixel, one can s i d a t e tha resof U. img. to m y t r a n b t t e d polarization -5 by u l a t d for an a r b i t r a p receinr p l s r l z a t i m h via (21. Bath +timu m i t b. 1 q l . m t . d for each pixel of UW entire imp.: RI. brightruss is than aisiqned to each pixel sCeOCdlng to P m V*V -I h ' s ) * ( _ k s ) . CalCUhtiV -5 Via (11. ? U C t h e m C * , the W S p U Of the lug. C M alS0 h Si-2. -8EKiL -2-W a V 5 Rl(n lRpux IG] Ih. TSP addresus t h f o l l d n g p r o b l a ( M [2,3] for -re detailsl: For a giwn pixel ( i . e . , know scattering matrix), find such t r W l t t t i n g .nd receiving polariratiau, for which tha receiwd p a r is r u i m a l ( r l n i r s l l . ruthmatically it translates into: find 5 _h such that v -$[SI% is optilvll for a qiwn [SI, subject t o the c a u t r a i n t s ll_hll -ll-$ll -1. nN w uUqli.hn this in t h r a up.rata st.9.s [1,21: st.9. 1 1 Ru m r g y &tufty in tha reflected uave (before it reachas tha I...
We focus on image contrast optimization between two rough surface classes, which is based strictly on polarimetric filtering and, therefore, no digital image processing techniques are employed.The approach is tested on a complete polarimetric synthetic aperture radar image of the San Francisco Bay area (NASA/JPL CV-990 Gband POGSAR data). Optimal transmitted polarizations are found for each image pixel and the results are analyzed statistically via a set of joint 2D histograms. This is done for both of the rough surface classes. The image response to the "optimal" incident polarization is then simulated digitally by adjusting the receiver polarization according to the modes of the histograms.The corresponding images are computed and displayed with significant image contrast improvement. I. 1NTRoDUcT1mThis paper addresses the problem of coherent image contrast optimization between two rough surface classes.we focus on such contrast which is due to the differences in polarimetric scattering from one rough surface to another. The novelty of this problem is due to the combination of coherent imaging and polarimetric scattering. The former introduces speckle reduction as a major issue while the latter provides the full scattering matrix (i.e., complete polarization information) per image pixel. The second and equally important task of this work is to develop efficient statistical tools for polarimetric image data analysis and speckle reduction techniques.Speckle has long been recognized as the main problem of coherent imaging (1) and many processing techniques have been advanced to overcome it.The vast majority of these techniques, however, are of a scalar nature sinply because vector/laatrix imaging data are 80 sparse and have become available only very recently. Such data, taken with the W J P L CV-990 dual-polarization L-band (1.225 GHZ) SAR (Synthetic Aperture Radar) system, have been made available to us.Here, we investigate the potential of a strictly polarimetric image filtering which takes full advantage of the matrix data provided on a pixel by pixel basis, and complements the existing scalar contrast optimization and speckle reduction techniques.We wish to stress from the outset that our goal is contrast optimization (with the corresponding speckle reduction) withut the help of incoherent averaging over pixels or "looks", because of the corresponding loss of spatial or temporal resolution.At first glance, speckle reduction is impossible without incoherent averaging but further consideration shows that it is so only for scalar data. Indeed, taking "projections" onto the receiver direction in the polarization space decreases amplitude fluctuations and an image appears less speckled. The goal of this paper is to find such a choice of the polarization projection which makes a given rough surface class least speckled and, by doing so, to improve the image contrast between two given classes.The paper is structured as follaws: a brief description of basic polarimetric definitions is provided in Section 11, while in...
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