H (u,v) = 1+ [ 0 2 ] 1 2

Transcription

1 I nterntionl Journl of E ngineering Reserh And Mngement (IJERM) ISSN : , Volume-1, Issue-4, July 2014 Destriping remote sensing imges using frequeny d omin Butterworth noth filter E rfn mrei, M ohmmd Rez Mosheri A strt Nowdys, stellite imges re widely used for lol phenomen monitoring. Unfortuntely, some striping noises re oserved in these imges used y imperfet lirtion of detetors, imperfet imge proessing in ground r eeiving sttions, detetor filure nd so on whih led to redued dt qulity. Thus, for ny useful pplition of dt ontined in these imges, noises s hould e removed firstly. In this pper, frequeny domin Butterworth noth filter hs een used for r emovl of periodi striping noises in stellite imges. This method is simpler nd of high ury ompred to some existing methods. Therefore, it is proposed to use this method for removl of periodi s triping noise in stellite imges I ndex Terms p eriodi striping noise; Butterworth n oth filter; Fourier trnsform; remote sensing. I. I NTRODUCTION Nowdys, remote sensing imges re widely used for environmentl reserhes nd sientifi studies. Unfortuntely, existene of some noises mkes using d t ontined in these imges diffiult or even impossile. Therefore, noise removl should e rried out in order to use the dt in these imges. The most ommon type of imge noises in stellite imges, re striping noise whih re introdued into n imge s r esult of ftors suh s imperfet lirtion of detetors, imperfet imge proessing in ground reeiving sttions nd detetor filure [8].Noise removl proess should e so tht does not influene on imge min ontents nd only remove the noise. In [1] r diometri equliztion tehniques hve een pplied primrily to remove non- periodi striping in imge dt. In these tehniques simple gin nd is model is developed for ll detetors nd then used for ompenstion. This method does not ount for n onlinerity i n sensor vritions. [2] for orreting striping noise from imges hs employed histogrm m odifition. In this pproh the histogrm of the Mnusript reeived July 21, E rfn mrei, eletril engineering deprtment, k hvrn institute of h igher edution, Mshhd, Irn M ohmmd Rez Mosheri, Remote sensing Dept., K NToosi University of Tehnology, Tehrn, Irn noisy dt is mde to mth histogrm of suitle referene dt. This suitle referene histogrm n e t he glol histogrm of the imge, n e from some other omptile imge, or n e from one of the detetors onsidered s the referene with histogrm dt from other detetors mthed to it. These tehniques re esily implemented ut n involve tril n d error nd inonsistent results. [3] h s u sed the prinipl omponents nlysis to remove noisy sn l ines from TM imges. After the prinipl omponents re otined, the noisy higher order omponents re s imply set to zero nd n inverse trnsformtion is performed. Though this method is effetive, it involves n enormous mount of omputtion to ompute sttistis nd eigenvetors nd then pply the forwrd nd inverse trnsformtions. [4] hs employed power s petr filtering method to remove periodi striping noise in Lndst imges. In this method, imge is divided into severl prts nd Fourier trnsform is tken from eh prt nd power spetr of the imge is lulted. After series of enhnements on power s petr, inverse Fourier trnsform is tken in order to otin onvolution kernel. However, the prolem with this method is tht mny enhnements should e done on power spetr to otin onvolution kernel. [5] hs employed line trking nd edge detetion lgorithms f or loting striping ptterns in stellite imges. After deteting these ptterns, striping noise is removed using sptil urve funtions. The dvntge of this method is tht it n remove non- periodi striping noise from the i mge s well. The prolem with this method is tht it detets some dditionl strips when deteting striping ptterns lotions in imges. [7] hs used sptil domin imge filtering for removl of periodi striping noise in pnhromti imges quired y SPOT s eond High Resolution Visile (HRV2). This method is reltively fst method. However, the prolem with this method is tht the filter my ffet other imge f tors s well s noise removl. In this pper, frequeny domin Butterworth noth filter h s een used for removl of periodi striping noise in stellite imges. This method is of high ury s it removes only noise omponents in frequeny domin. Finlly, to ompre the proposed method here with m ethods provided in [4] nd [7], Pek Signl to Noise R tio (PSNR) h s een lulted nd resulting findings show the proposed method ury. It should e noted tht for implementing the model nd noise 145 w ww.ijerm.om

2 D estriping remote sensing imges using frequeny domin Butterworth noth filter removl, digitl imge proessing toolox of MATLAB softwre e mployed. (MATLAB (R2011)) hs een I I. m teril nd methods A. d t The dt used in this pper, hve een quired y Lndst 5 Themti Mpper, Lndst 4 Multi Spetrl Snner (MSS) nd SPOT 1 seond High Resolution Visile. Figure 1() shows smple imge quired in nd 6 of Lndst 5 Themti Mpper. Figure 1() s hows smple imge quired in nd 2 of Lndst 4 MSS whih inludes periodi striping noise. This kind o f noise is oserved in Lndst 4 nd 5 MSS nd TM L evel 0 nd level 1T dt [ 8]. B F ig: 1. (): 360*360 pixels smple of striping in L ndst 5 Themti Mpper level 1T dt. (): 360*360 pixels smple of striping in Lndst 4 Multi S petrl Snner level 0 dt. (): 128*128 pixels of S POT 1 sene. Figure 1() shows prt of the quired imge in pnhromti nd of SPOT 1 HRV2 whih hs een pre- proessed t level 1A nd inludes periodi striping n oise [7]. B. D estriping lgorithm O ne of the importnt topis in remote sensing is noise removl from imges so tht noise removl proess does not influene on imge min ontents nd only remove the noise. Sptil domin noise omponents detetion is diffiult tsk. Therefore, to detet noise omponents, the imge should e trnsferred to spe in whih noise omponents will e distinguishle from other omponents. In this pper, Fourier trnsform ws tken from the imge nd Fourier spetrum of the imge w s lulted to detet noise omponents. Fourier spetru m w s lulted ording to the f ollowing e qution: F (u,v) = [R 2 (u,v) + I 2 (u,v)] 1 2 ( 1) R (u,v) nd I (u,v) denotes the rel nd imginry prts of Fourier trnsform respetively. Existene of p eriodi striping noise in imge use right spots in Fourier spetrum. Thus, periodi striping noise omponents n e deteted using Fourier spetrum. After d eteting noise omponents in Fourier spetrum, the mgnitude of these omponents in imge Fourier t rnsform n e redued to zero using Butterworth n oth filter. Trnsfer funtion of Butterworth noth f ilter hs een shown in Eqution (2): H (u,v) = 1 ( 2) 1+ [ D0 2 ] n D 1(u,v) D 2(u,v) D 0 denotes noth rdius nd n denotes the filter order. D 1(u,v) nd D 2(u,v) hve een shown in Equtions ( 3) nd (4) respetively: D 1(u,v) = [ u M 2 u v N 2 v 0 2 ] 1 2 ( 3) D 2(u,v) = [ u M v N ] 1 2 ( 4) M nd N re imge dimensions nd ( u 0, v 0 ) is the noth lotions. After loting the noise omponents in Fourier spetrum (determining v 0 nd u 0 ), filter redues the mgnitude of striping noise omponents in imge Fourier trnsform to zero. After tht, we tke inverse Fourier trnsform from justified Fourier trnsform of imge to restore the orreted i mge. Frequeny response of the designed filters re shown in figure 2. A s n e seen i n the urves, Filters h ve the l est mount of r ipple A nd lso hve shrp edges. It 146 w ww.ijerm.om

3 Interntionl Journl of Engineering Reserh And Mngement (IJERM) ISSN : , Volume-1, Issue-4, July 2014 should e noted tht in these urves, the frequeny xis hs een normlized. After loting the noise omponents in Fourier spetrum, noise n e removed from the imges using frequeny domin Butterworth noth filter. The results of noise removl for imges in figure 1 re shown in Figure 4. Visul investigtions show the effetiveness of the proposed method in noise removl. Also, Fourier spetrum of the orreted imges hve een shown in Figure 5. As it n e seen, right spots in Fourier spetrum of rw imges do not exist in Fourier spetrum of the orreted imges, inditing noise removl from imges. Fig: 2. The frequeny response urve of the filters to remove the noise in imges tht shown in figure 1 (), () nd (). III. result nd disussion Proposed method in previous setion ws pplied to the imges in Figure 1. As indited in Figure 3, periodi striping noise omponents in imge Fourier spetrum re oserved s series of right spots. Therefore, periodi striping noise omponents n e loted using Fourier spetrum. It should e noted tht the right spot in the enter of Fourier spetrum represents zero frequeny omponents. Figure 3() shows Fourier spetrum of the imge in figure 1. nd similrly, Figure 3() nd 3() shows Fourier spetrum of the imges in Figure 1() nd 1() respetively. 147 Fig: 3. (): Fourier spetrum of the imge in Fig. 1 (). ():Fourier spetrum of the imge in Figure 1(). ():Fourier spetrum of the imge in Figure 1(). As it n e seen, noise omponents re seen s right spots in Fourier spetrum.

4 Destriping remote sensing imges using frequeny domin Butterworth noth filter Fig: 5. (): Fourier spetrum of the imge in Figure 3( ). (): Fourier spetrum of the imge in Figure 3().( ): Fourier spetrum of the imge in Figure 3(). As it n e seen, right spots relted to noise re not seen in Fourier spetrum of enhned imges. Fig: 4.( ), () nd (): The result of noise removl for the imges in Figure 1(), () nd (), respetively 148

5 I nterntionl Journl of E ngineering Reserh And Mngement (IJERM) ISSN : , Volume-1, Issue-4, July 2014 F ig: 6. A ) Rw dt. B) Simulted dt for vertil striping n oise. C ) The result of striping noise removl in figure () To demonstrte the effiy of the proposed method in noise removl, simulted dt hs een used for p eriodi striping noise. Rw dt is s hown in Figure 7 () tht fter dding the periodi striping noise, it is shown in Figure 7 (). Also in Figure 7 () the result of d estriping hs een shown. To ompre the results otined from the suggested model in this pper (figure 6 ( ) ) with the originl imge (figure 6 ()), we used the Root Men Squre Error (RMSE) (Eqution 5 ). RMSE ws lulted etween orresponding pixels of the modified imge nd originl imge. N i=1 N 1 RMSE = x i y i 2 (5), x i i s pixels of the orreted imge, y i is pixels of the originl imge, nd N is the totl nume r of p ixels. The vlue of the lulted RMSE is equl to To lulte the reltive error, RMSE is divided y verge o f the pixels in the filtered imge y our model ( equtions 6 nd 7 ). Averge = N i=1 x i N Reltive- Error = RMSE Averge (7) (6) q udrnt shows the ury of the model in noise r emovl nd little error of the method in noise removl. F igure 8 shows the histogrm of the simulted dt. As this hrt suggests, the noise removl lgorithm hs negligile impt on the ontent of the o riginl i mge, ut it ws somehow suessful in the n oise removl. In Tle 1, the sttistil hrteristis of imges ( figure1 nd 4) efore nd fter the des triping re g iven. T he redution seen in the stndrd devition is d ue to the noise removl, euse presene of the noise i n the imge inreses this quntity. Figure 8. A) histogrm of imge in Figure 6 (). B) histogrm of imge in Figure 6 (). C) histogrm of imge in Figure 6 ( ) Figure 7. The Stter plot drwn etween Figure 6 ( ) nd 6 ( ) The error rte lulted for the result of the model proposed in this pper s ompred to originl imge is equl to 6.31 perent, whih indites tht the model h s n ury of 93.7 perent in noise removl. To e vlute the model, stter plot of pixels is drwn etween the initil imge (Fig. 6 ()) nd the orreted i mge (Fig. 6 ()). This digrm is shown in Figure 8. Dispersion of points round the isetor of the first The imge in f igure 1() The imge in f igure 4() The imge in f igure 1() The imge in f igure 4() The imge in f igure 1() The imge in f igure 4 () T le 1: m en The lulted men nd Stndrd rw nd orreted imges Stndrd d evition devition for the In ddition to s tndrd devition, the PSNR lso lulted. It is ovious tht more PSNR vlue, show more urte of the noise removl method. PSNR 149 w ww.ijerm.om

6 D estriping remote sensing imges using frequeny domin Butterworth noth filter mthemtil ( 8): PSNR = 10 log ( reltionship hs een shown in Eqution D 2 M N M 1 j=0 N 1 i=0 g (i,j) f (i,j) 2 ) (8) D for n- ite quntiztion equls to 2 n. M nd N re imge dimensions, f i,j is the rw imge nd g i,j is the orreted imge. Results of omprison etween the p roposed method here nd the methods provided in [4] nd [7] hve een desried in Tles 2 nd 3. [4] hs used power spetrum filtering to remove noise from Lndst imges. Also, [7] hs used sptil domin f iltering to orret SPOT p nhromti imges. As it n e seen, the otined vlues for PSNR show the ury of the proposed model in this pper. It should e noted tht the otined vlues re in db. T he method used in [4] T he method proposed in this p per T le 2: The imge in Figure 4 () The imge in F igure 4() The lulted PSNR for the orreted imges in this p per nd the model provided in [4] T he method used in [7] The method proposed in this t le 3: p per The lulted PSNR for p per nd the model provided T he imge in Figure 4() orreted imges in [7] in this on lusion One of the most importnt tsks in remote sensing is noise removl from stellite imges. Noise removl p roess should e so tht does not influene on min ontents of imge nd only remove the noise. Therefore, providing n urte method for noise removl from remote sensing imges seems gretly importnt. Existene of noise in imges mkes extrting dt from these imges diffiult or even i mpossile. Therefore, noises should e removed to llow using the dt ontined in these imges. One of the most ommon types of noises in stellite imges, re striping noises. These kinds of noises re introdued to imges s result of different ftors inluding i mperfet lirtion of detetors, detetor filure nd some other uses. The ide employed in this pper ws tht the imge e trnsferred to spe in whih noise omponents n e deteted nd in turn removed. P eriodi striping noises in imge Fourier spetrum re seen s right spots. Thus, periodi stripi ng noise omponents n e deteted with trnsferring imge to frequeny domin nd lulting Fourier spetrum. After Noise omponents detetion, frequeny domin B utterworth noth filter ws pplied. Then, inverse Fourier trnsform ws tken from justified Fourier t rnsform of t he imge to restore the orreted imge. To ompre nd evlute the proposed method in this pper, stndrd devition nd PSNR rtio were used. T he results of omprison etween the proposed method in this pper nd the methods provided in [4] nd [7] showed tht effetiveness rte of this method ws higher thn the previous methods. Therefore, using this method for removl of periodi striping noises is p roposed. R EFERENCES [ 1] V. R. Algzi nd G. E. Ford (1981), "Rdiometri equliztion of non periodi striping in stellite dt," Comp. Grph. Imge Pro. 16(3), [2] B. K. P. Horn nd R. J. Woodhm (1979), "Destriping Lndst MSS imges y histogrm modifition," C omp. Grph. Imge Pro. I0(I), [ 3] Rm. S (1986), "Noise removl y the Krhunen - Loeve trnsform," in Pro. Int. So. for Photogrmmetry nd Remote Sensing Symposium, Vol [ 4] R m. S, Mihel. C, Jmes. W (1988), "Lndst dt destriping using power spetr filtering", Optil Engineering 27(11), , Novemer. [ 5] F. Tsi, W. Chen, (2008) "Striping Noise Detetion nd Corretion of Remote Sensing Imges", IEEE TRANSACTIONS ON GEOSCIENCE AND R EMOTE SENSING, VOL. 46, NO. 12, DECEMBER. [ 6] R C. Gonzlez, R E. Woods, S. L. Eddins (2009), "Digitl Imge Proessing Using MA TLAB". Beijing: P ulishing House of Eletronis Industry. [ 7] WESTIN, T(1990), filters for removing oherent n oise of period 2 in SPOT imgery. INT. J. remote sensing, Vol.11 Issue 2, [8] 15. Mr [online]. Aville: l ndst.usgs.gov/ Detetor Striping.php w ww.ijerm.om