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2 11 Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues Lpng Hu 1, Hongwe Lu and Hongcheng Yn 1 1 Natonal Key Laboatoy of aget and Envonment Electomagnetc Scatteng and Radaton, Bejng Insttute of Envonmental Chaactestcs, Bejng, Natonal Key Laboatoy of Rada Sgnal Pocessng, Xdan Unvesty, X an, Chna 1. Intoducton In ecent yeas, ada Automatc aget Recognton (AR) based on taget synthetc apetue ada (SAR) mages has eceved moe and moe attentons. So fa, many lteatues based on SAR publc dataset ae eleased, whch focus on the SAR taget ecognton elated technques ncludng taget segmentaton, featue extacton, classfe desgn, and so on. A template matchng was poposed (Ross et al., 1998). Suppot Vecto achne (SV) has been appled to SAR AR (Zhao & Pncpe, 001; Byant & Gabe, 1999). he dawbacks of them ae that none of them have any pe-pocessng and featue extacton. Howeve, effcent pe-pocessng and featue extacton may help to mpove ecognton pefomance. Pncpal Component Analyss (PCA) s a classcal featue extacton technque. But when PCA s used fo mages featue extacton, D mage matces must be pevously tansfomed nto 1D mage vectos. hs usually leads to a hgh dmensonal vecto space, whee t s dffcult to evaluate the covaance matx accuately. o solve ths poblem, -dmensonal PCA (DPCA) fo mage featue extacton s poposed (Yang et al., 004). As opposed to PCA, DPCA constucts the covaance matx dectly usng D mage matces athe than 1D vectos, and evaluates the covaance matx moe accuately. oeove, the sze of the covaance matx s much smalle. A dawback of DPCA s that t only elmnates the coelatons between ows. So t needs moe featues, and ths wll lead to lage stoage equements and cost moe tme n classfcaton phase. o futhe compess dmenson of featues, two-stage DPCA s appled n ths chapte. he emande of ths chapte s oganzed as follows: n Secton, the SAR mages pepocessng method s descbed. DPCA s fst evewed, and two-stage DPCA s descbed n Secton 3. In Secton 4, classfes ae descbed. In Secton 5 and 6, expemental esults based on ovng and Statonay aget Acquston and Recognton (SAR) data and conclusons ae pesented.

3 00 Pncpal Component Analyss Engneeng Applcatons. SAR mage pe-pocessng he ognal SAR mages povded by SAR contan not only tagets of ou nteest, but also backgound cluttes, as shown n Fg. 1 (a). If tagets ae ecognzed based the ognal mages, cluttes would depess the system pefomance. hus, t s necessay to pe-pocess the ognal mages to segment tagets fom backgound cluttes..1 Logathmc tansfomaton We tansfom the ognal mages usng logathm conveson, whch can convet speckles fom multple model to addtonal model and make the mage hstogam moe sutable be appoxmated wth a Gaussan dstbuton. he logathmc tansfomaton s gven by xy xy G, 10lg F, (1) whee Fdenotes the magntude matx of the ognal SAR mage. Snce the logathm s not defned at 0, we add an abtay constant (fo example 0.001) to the ognal mage befoe the logathm. o ensue the pxel values to be nonnegatve, we add a coespondng constant (30).. Adaptve theshold segmentaton In ode to obtan the taget mage, the adaptve theshold segmentaton method s adopted. Fst of all, estmatng the mean and the vaance of the cuent mageg, fo each pxel x, y of G, a, a, 1, G, xy, B, xy, 0, else x y x y f x y c ac a Whee a, Bac denote the taget and the backgound espectvely, c can be obtaned statstcally fom tanng samples..3 ophologcal flte and geometc clusteng opeaton Due to the pesence of speckles, the esult of theshold segmentaton contans not only taget, but smalle objects nevtably, as shown n Fg. 1 (b). o emove these small objects and obtan smoothng the taget mage, mophologcal flte (Gonzalez &Woods, 00) and geometc clusteng opeaton (usman & Ke, 1996) ae adopted to a. ophologcal flte ams to smooth bounday, emove shap potusons, fll small concaves, emove small holes, jont gaps, and so on. In geneal, flteed mage a may also contan some non-taget egons, whch ae much smalle than taget tself, as shown n Fg. 1 (c). o emove small egons, we apply geometc clusteng opeaton: fstly, detect and label all the ndependent connected egons n a. hen, compute aeas fo each egon. he lagest egon s of ou nteest. In ths way, we obtan the esultng a, as shown n Fg. 1 (d). Ovelayng the esultng a on the logathmc mage G obtans taget ntensty. ()

4 Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues 01 (a) (b) (c) (d) Fg. 1. SAR mage pe-pocessng (takng 7 fo example). (a) Ognal mage, (b) Logathmc mage, (c) heshold segmented mage, (d) Result of the mophologcal flteng, (e) Result of geometc clusteng. (e)

5 0 Pncpal Component Analyss Engneeng Applcatons (a) (b) (c) (d) Fg.. SAR mage pe-pocessng (takng BR70 fo example). (a) Ognal mage, (b) Logathmc mage, (c) heshold segmented mage, (d) Result of the mophologcal flteng, (e) Result of geometc clusteng. (e)

6 Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues 03 (a) (b) (c) (d) Fg. 3. SAR mage pe-pocessng (takng BP fo example). (a) Ognal mage, (b) Logathmc mage, (c) heshold segmented mage, (d) Result of the mophologcal flteng, (e) Result of geometc clusteng. (e)

7 04 Pncpal Component Analyss Engneeng Applcatons.4 Image enhancement and nomalzaton Image enhancement (Gonzalez &Woods, 00) can weaken o elmnate some useless nfomaton and gve pomnence to some useful nfomaton, whch ams to enhance mage qualty by adoptng a cetan technology fo a specfc applcaton. Hee, we apply the powe-law tansfomaton to enhance the taget mage xy, xy, K H (3) wheeh,k denotes the fome and latte tansfomed mage espectvely, s an constant. In pactce, due to the dffeence of the dstance between a taget and ada, the ntensty of echoes dffes geatly. hus, t s necessay to nomalze the mage. Hee, a nomalzed method adopted s J xy, K x y xy, K xy, (4) whee J, K denotes the fome and latte nomalzed mage espectvely. Due to the uncetanty of taget locaton n a scene, -dmensonal fast Foue tansfom (DFF) s appled. Only half of the ampltude of Foue s used as nputs of featue extacton due to ts tanslaton nvaance and symmetc popety, so that t can decease the dmenson of samples and educe computaton. 3. Featue extacton Featue extacton s a key pocedue n SAR AR. If all pxels of an mage ae egaded as featues, ths would esult n lage equements, hgh computaton and pefomance loss. heefoe, t s necessay to extact taget featues. 3.1 Featue extacton based DPCA Suppose that we have pe-pocessed tanng samples m n I R, 1,,,. Cente them Ι II, whee I 1 1 I1, I,, I wth I s the mean of total tanng samples. Fo each centeed sample Ι, let poject t onto W by the followng lnea tansfomaton: A ΙW (5) whee the pojecton matx W R n satsfes: WW I, and I s dentty matx. Rec Let us econstuct the samplei : I I AW I ΙWW, the econstuct eo s Rec II. he optmal pojecton matx should mnmze the sum of the econstuct eos sum of all the tanng samples

8 Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues 05 W opt ag mn W Rec F ag mn I I ΙWW W ag mn IIWW Ι W ag mn W I I Ι ΙWW F F F (6) whee F denotes matx F-nom. We have t Ι ΙWW F Ι ΙWW Ι ΙWW 1 1 Ι ΙWW ΙWW Ι t 1 t ΙΙ ΙWW Ι ΙWW Ι ΙWW WW Ι 1 t ΙΙ ΙWW Ι ΙWW Ι ΙWW Ι 1 t ΙΙ t W ΙΙW 1 1 (7) So, equaton (6) s equvalent to the followng fomula W opt ag max W 1 W W ag max t W 1 ag maxt W t WIIIIW WGW t ΙΙ (8) wheegt 1 I I I I s the covaance matx of tanng samples. So, the optmal n pojecton matx Wopt w1, w,, w R ( n) wth 1,,, egenvectos ofg coespondng to the lagest egenvalues. t Fo each tanng magei, ts featue matx s w s the set of B y 1,, y IIW opt IIww 1,,, w IIwIIw 1,,, IIw R m (9)

9 06 Pncpal Component Analyss Engneeng Applcatons Gven an unknown samplei R mn, ts featue matxb : By,, y 1 IIW opt IIwIIw 1,,, IIw R m (10) 3. Featue extacton based two-stage DPCA DPCA only elmnates the coelatons between ows, but dsegads the coelatons between columns. So t needs moe featues. hs wll lead to lage stoage equements and cost much moe tme n classfcaton phase. o futhe compess the dmenson of featue matces, two-stage DPCA s appled n ths chapte. Its detaled mplementaton s descbed as follows (shown n Fg.4): DPCA tanspose DPCA Fg. 4. Illustaton of two-stage DPCA. (1) anng mages R m I n wth 1,,,, calculate G t by the secton 3.1, and then n obtan the ow pojecton matx W opt R 1 1 n. Compute featue matces m A I IW fo each tanng mage. opt R 1 () Regad the matces ZA 1,,, m DPCA and get the column pojecton matx m Featue matx of each tanng mage s obtaned as new tanng samples, epeat the couse of W. copt R BZW AW copt copt W I I W opt copt R 1 1,,, (11) Gven a unknown magei R mn, ts featue matxb : 4. Classfe desgn opt copt R 1 BW IIW (1) In ths chapte, the neaest neghbo classfe based Eucld dstance s used. Compute dstances of featue matces between unknown and all tanng samples. hen, the decson s that ths test belongs to the same class as the tanng sample, whch mnmzes the dstance.

10 Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues Classfcaton based DPCA featues DPCA featues of tanng mage I and test I ae opt R 1,, B y y I IW m, 1,, m opt R whee m y I Iw R 1 m k k, y I Iw R 1 By y IIW k k, k 1,,,, 1,,,. Fom the expessons, we see that column vectos y k and y k of B and B deve fom the pojectons of I and I onto egenvecto w k coespondng to egenvalue k. heefoe, the dstance of featue matces between the test and th tanng mage s defned as d BB, y y (13) k k k1 4. Classfcaton based two-stage DPCA featues of a testi and tanng magei by two- Gven featue matces R 1 B and R 1 B stage DPCA. (1) Defnton Dstance along ow Featue matces R 1 B and R 1 B ae wtten the followng fom B x1, x,, x, k 1 1 B x1, x,, x, 1 x, x k ae ow vectos wth the dmenson of 1. Defne the dstance between the two featue matces () Defnton Dstance along column d 1 1 BB, x x (14) k1 k1 k1 1 Featue matces R 1 B and R 1 B ae wtten as B y,, 1 y, y k, yk ae ow vectos wth the dmenson of 1. Defne the dstance B,, y 1 y, (3) Defnton Dstance along ow and column d BB, y y (15) k k k 1 he dstance between the test and the th tanng mage s defned 5. Expemental esults,,, 1 dbb d BB d BB (16) Expements ae made based on the SAR publc elease database. hee ae thee dstnct types of gound vehcles: BP, BR70, and 7. Fg.5 gves the optcal mages of the thee classes of vehcles, and Fg.6 shows the SAR mages.

11 08 Pncpal Component Analyss Engneeng Applcatons hee ae seven seal numbes (.e., seven taget confguatons) fo the thee taget types: one BR70 (sn-c71), thee BP s (sn-c1, sn-9593, and sn-9566), and thee 7 s (sn-13, sn- 81, and sn-s7). Fo each seal numbe, the tanng and test sets ae povded, wth the taget sgnatues at the depesson angles 17 and 15, espectvely. he tanng and test datasets ae gven n able 1. he sze of taget mages s conveted nto by ou pe-pocessng descbed n secton. (a) BP (b) BR70 (c) 7 Fg. 5. Optcal mages of the thee types of gound vehcles. (a) BP (b) BR70 (c) 7 Fg. 6. SAR mages of the thee types of gound vehcles. anng set Numbe of samples estng set Numbe of samples BPsn-c1 196 BPsn BPsn BPsn BR70sn-c71 33 BR70sn-c sn sn sn sn-s7 191 able 1. anng and testng datasets.

12 Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues he effects of logathm conveson and powe-law tansfomaton wth dffeent exponents on the ecognton ates n ou pe-pocessng method Let us llustate the effects of logathm conveson and powe-law tansfomaton wth dffeent exponents n ou pe-pocessng usng an mage of 7, shown n Fg.7. (a) (b) (c) (d) (e) (f) (g) (h) Fg. 7. he effects to mage qualty wth dffeent. (a) Ognal mage, (b) Logathmc mage, (c) Segmented bnay taget mage, (d) Segmented aget ntensty mage, (e) Enhanced mage wth, (f) Enhanced mage wth 3, (g) Enhanced mages wth 4, (h), Enhanced mages wth 5. Fom Fg.7 (a), we see that the total gay values ae vey low, and many detals ae not vsble. On the one hand, logathmc tansfomaton convets speckles fom multple to addtonal model and makes mage hstogam moe sutable be appoxmated wth a Gaussan dstbuton. On the othe hand, t enlages the gay values and eveals moe detals. Howeve, mage contast n the taget egon deceases as shown n Fg.7 (d). heefoe, t s necessay to enhance mage contast, whch can be accomplshed by powe-law tansfomaton wth 1. he values of coespondng to Fg.7 (e) ~ (h) ae, 3, 4, and 5. We note that as nceases fom to 4, mage contast s enhanced dstnctly. But when contnues to ncease, the esultng mage become dak and lose some detals. By compasons of these esultng mages, we thnk that the best mage enhancement esult s at takng 4 appoxmately.

13 10 Pncpal Component Analyss Engneeng Applcatons We use the 698 samples of BPsn-9563, BR70sn-c71, and 7sn-13 fo tanng, 973 mages of BPsn-9563, BPsn-9566, BR70sn-c71, 7sn-13, 7sn-81, and 7sn-s7 fo testng. he vaaton of ecognton pefomance wth of DPCA s gven n Fg. 8. We can see that DPCA obtans the hghest ecognton ate at 3.5 ( s equal to 3.5 by default). Fg. 8. Recognton ate wth dffeent. 5. Compasons of dffeent pe-pocessng methods In SAR ecognton system, pe-pocessng s an mpotant facto. Let us evaluate the pefomance of seveal pe-pocessng appoaches as follows. ethod 1: the ognal mages ae tansfomed by logathm. hen, half of the ampltudes of the -dmensonal fast Foue tansfom ae used as nputs of featue extacton. ethod : ovelayng the segmented bnay taget a on the ognal mage F gets taget mage, nomalze t. Half of the ampltudes of -dmensonal fast Foue tansfom ae used. ethod 3: ovelayng a on F obtans taget mage. Fst, enhance t usng powe-law tansfomaton wth an exponent 0.6. hen nomalze t. Half of the ampltudes of - dmensonal fast Foue tansfom ae used.

14 Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues 11 ethod 4: ovelayng a on the logathmc mage G obtans taget mage, nomalze t. Half of the ampltudes of -dmensonal fast Foue tansfom ae used. ethod 5 (ou pe-pocessng method n secton ): hat s, ovelayng a on G obtans taget mage. Fst, enhance t usng powe-law tansfomaton wth an exponent 3.5. hen nomalze t. Half of the ampltudes of -dmensonal fast Foue tansfom ae used. Fg. 9. Pefomances of DPCA wth fve pe-pocessng methods. We also use the 698 samples of BPsn-9563, BR70sn-c71, and 7sn-13 fo tanng, 973 mages of BPsn-9563, BPsn-9566, BR70sn-c71, 7sn-13, 7sn-81, and 7sn-s7 fo testng. he ecognton ates of DPCA wth these fve pe-pocessng methods ae gven n Fg. 9. We can see that the pefomance of method 1 s the wost, because t does not segment the taget fom backgound cluttes, whch dstub ecognton pefomances. Compang method 3 wth and 5 wth 4, we easly fnd that mage enhancement based on powe-law tansfomaton s vey effcent. he dffeence between method 3 and method 5 (ou pe-pocessng method) s that the fome s obtaned by ovelayng. a. on the ognal magef, and then enhanced by powe-law tansfomaton wth a factonal exponent 0.6. he latte s obtaned by ovelayng a on the logathm mageg, and then enhanced by powe-law tansfomaton

15 1 Pncpal Component Analyss Engneeng Applcatons wth an exponent 3.5. Due to the effects of logathm n ou method, the pefomance of method 5 (ou pe-pocessng method) s bette than that of method 3. All the fve expemental esults testfy that ou pe-pocessng method s vey effcent. 5.3 Compasons of DPCA and PCA o futhe evaluate ou featue extacton method, we also compae DPCA wth PCA. he flow chat of expements s gven n Fg.10. Fg. 10. Flow chat of ou SAR AR expements. Fo all the tanng and testng samples n able 1, Fg.11 gves the vaaton of ecognton ates of PCA wth featue dmensons, that s, the numbe of pncpal components. PCA acheves the hghest ecognton ate when the numbe of pncpal components (d) equal 85. Fg. 11. Vaaton of ecognton ates of PCA wth the numbe of pncpal components.

16 Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues 13 Fo all the tanng and testng samples n able 1, Fg.1 gves the vaaton of ecognton ates of DPCA wth the numbe of pncpal components. DPCA acheves the hghest ecognton ate when the numbe of pncpal components () equal 8. Fg. 1. Vaaton of ecognton ates of DPCA wth the numbe of pncpal components. able shows the hghest ecognton ates of PCA and DPCA. We see that the hghest ecognton pefomance of DPCA s slghtly bette than that of PCA. Hghest ecognton ate (dmenson of featue vectos o featue matces) PCA+NNC (85) DPCA+NNC (18 8) able. Compasons of the hghest ecognton ates of PCA and DPCA By Compang Fg.11 wth Fg.1, we fnd that ecognton pefomance of DPCA s bette than PCA. hs s due to the facts that -dmensonal mage matces must be tansfomed nto 1-dmensonal mage vectos when PCA used n mage featue extacton. he mage matx-to-vecto tansfomaton wll esult n some poblems: (1) hs wll destoy - dmensonal spatal stuctue nfomaton of mage matx, whch bngs on pefomance loss; () hs leads to a hgh dmensonal vecto space, whee t s dffcult to evaluate the covaance matx accuately and fnd ts egenvectos because the dmenson of the

17 14 Pncpal Component Analyss Engneeng Applcatons covaance matx s vey lage ( m nm n). DPCA estmates the covaance matx based on -dmensonal tanng mage matces, whch leads to two advantages: (1) -dmensonal spatal stuctue nfomaton of mage matx s kept vey well; () the covaance matx s evaluated moe accuately and the dmensonalty of the covaance matx s vey small ( n n). So, the effcency of DPCA s much geate than that of PCA. able 3 gves the computaton complexty and stoage equements of PCA and DPCA, n whch the stoage equements nclude two pats: the pojecton vectos and featues of all tanng samples ( 698, m18, n64, 8, l 0 ). Fom ths table, we see that although the stoage equements ae compaatve, the computaton complexty of DPCA s much smalle than that of PCA when seekng the pojecton vectos. So, we thnk that DPCA s much geate than PCA n computaton effcency. Sze of covaance matx Complexty of fndng pojecton vectos Complexty of fndng featues Stoage of featues PCA m nmn o d mn ,30 DPCA n n n 3 64 m n ,536 mn dd n m = =755,650 =715,64 able 3. Compasons of the computaton complexty and stoage equements of PCA and DPCA Fom able and able 3, we can conclude that DPCA s bette than PCA n computaton effcency and ecognton pefomance. Fom able, we also see that featue matx obtaned by DPCA s consdeably lage. hs may lead to massve memoy equements and cost too much tme n classfcaton phase. So, we poposed two-stage DPCA to educe featue dmensons. 5.4 Compasons of DPCA and two-stage DPCA DPCA only elmnates the coelatons between ows, but dsegads the coelatons between columns. he poposed two-stage DPCA can elmnate the coelatons between mages ows and columns smultaneously, thus educng featue dmensons damatcally and mpovng ecognton pefomances.

18 Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues 15 able 4 shows the hghest ecognton ates of DPCA and two-stage DPCA. able 5 gves the computaton complexty and stoage equements of DPCA and twostage DPCA. he stoage equements also nclude two pats: pojecton matces and featue matces of all tanng samples ( 698, m18, n64, 8, l 0, 1 1, ). Fom able 4, we see that two-stage DPCA acheves the hghest ecognton pefomance wth smalle featue matces. Fom able5, we fnd that the stoage equements of two-stage DPCA ae smalle than those of DPCA. Fom able 4 and able 5, we can conclude that two-stage DPCA s bette than DPCA n ecognton pefomance and stoage equements. Fom able 4, we also see that the esults of two-stage DPCA ae compaatve no matte how the dstance between two featues s defned and the ecognton pefomance of the way of the dstance along ow and column s slghtly bette. Recognton appoaches Recognton ate (featue dmenson) DPCA (18 8) wo-stage DPCA (Defnton Dstance along column) 97.1 (1 ) wo-stage DPCA (Defnton Dstance along ow) 97.3 (10 30) wo-stage DPCA (Defnton Dstance along ow and (1 ) column) able 4. Compasons of ecognton pefomances of DPCA and two-stage DPCA (%). Complexty of fndng pojecton vectos DPCA 3 n wostage DPCA m n Complexty of fndng featues m n ,536 nm m 1 1 = =13,096 Stoage of featues n m = =715,64 n m 1 1 = =187,856 able 5. Compasons of stoage equements of DPCA and wo-stage DPCA. 5.5 Compasons of DPCA and two-stage DPCA unde dffeent azmuth ntevals In some cases, we can obtan taget azmuth. Usng t, ecognton pefomances may be mpoved. Goup tanng samples wth equal ntevals fo each class wthn 0 ~ 360, then extact featues wthn the same azmuth ange fo the thee types of tanng samples n the phase of tanng. In the phase of testng, the test sample s chosen to be classfed n the coespondng azmuth ange accodng to ts azmuth. In ths expement, tanng samples of each class ae gouped wth equal ntevals by 180,90, 30 espectvely.

19 16 Pncpal Component Analyss Engneeng Applcatons Recognton esults of DPCA and two-stage DPCA unde dffeent azmuth ntevals ( 180, 90, and 30 ) ae gven n able 6. Fom t, we obtan that pefomances of the twostage DPCA method s bette than those of DPCA. oeove, two-stage DPCA s obust to the vaaton of azmuth. hs table futhe poves that two-stage DPCA combnng wth ou pe-pocessng method s effcent. Recognton appoaches DPCA wo-stage DPCA (Defnton Dstance along column) wo-stage DPCA (Defnton Dstance along ow) wo-stage DPCA (Defnton Dstance along ow and column) able 6. Pefomances of DPCA and two-stage DPCA unde dffeent azmuth ntevals (%) 5.6 Compasons of two-stage DPCA and methods n lteatues he ecognton ates of two-stage DPCA and methods n lteatues ae lsted n able 7. Recognton appoaches Recognton ate (featue dmenson) emplate matchng (Zhao & Pncpe, 001) SV (Byant & Gabe, 1999) 90.9 PCA+SV (Han et al., 003) KPCA+SV (Han et al., 003) KFD+SV (Han et al., 004) (D) PCA [9] combnng ou pe-pocessng (Defnton Dstance along ( 1) column) (D) PCA [9] combnng ou pe-pocessng (Defnton Dstance ( 1) along ow) (D) PCA [9] combnng ou pe-pocessng (Defnton Dstance ( 1) along ow and column) GDPCA [10] combnng ou pe-pocessng (Defnton Dstance ( 1) along column) GDPCA [10] combnng ou pe-pocessng (Defnton Dstance 97.3 (4 1) along ow) GDPCA [10] combnng ou pe-pocessng (Defnton Dstance ( 1) along ow and column) wo-stage DPCA (Defnton Dstance along column) 97.1 (1 ) wo-stage DPCA (Defnton Dstance along ow) 97.3 (10 30) wo-stage DPCA (Defnton Dstance along ow and column) (1 ) able 7. Pefomances of two-stage DPCA and seveal methods n lteatues (%) We see that pefomances of lteatues (Zhao et al., 001; Byant & Gabe, 1999) ae the wost, snce they do not have any pe-pocessng and featue extacton. Howeve, effcent pe-pocessng and featue extacton can help to mpove ecognton pefomances.

20 Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues 17 In lteatues (Han et al., 003; Han et al., 004), PCA, KPCA, o KFD s employed. hese featue extacton methods seek pojecton vectos based on 1-dmensonal mage vectos. In ou AR system, taget s fstly segmented to elmnate backgound cluttes. hen, enhanced by powe-law tansfomaton to stand out useful nfomaton and stengthen taget ecognton capablty. oeove, featue extacton s based on -dmensonal mage matces, so that the spatal stuctue nfomaton s kept vey well and the covaance matx s estmated moe accuately and effcently. heefoe, two-stage DPCA combnng wth ou poposed pe-pocessng method can obtan the best ecognton pefomance. By compasons of two-stage DPCA and the smla technques, such as (D) PCA (Zhang & Zhou, 005), GDPCA (Kong et al., 005), we can conclude that ou pe-pocessng method s vey effcent and two-stage DPCA s compaable to (D) PCA and GDPCA n pefomance and stoage equements. able 8 gves the esults of two-stage DPCA, and othe appoaches n lteatues unde dffeent azmuth ntevals. Fom t, we obtan that pefomances of ou method s bette than those of lteatues. hs table futhe valdates that two-stage DPCA combnng wth ou pe-pocessng method s the best. Recognton appoaches emplate matchng (Zhao & Pncpe, 001) SV (Byant & Gabe, 1999) PCA+SV (Han et al., 003) KPCA+SV (Han et al., 003) KFD+SV (Han et al., 004) (D) PCA (Zhang & Zhou, 005) combnng ou pepocessng (Defnton Dstance along column) (D) PCA (Zhang & Zhou, 005) combnng ou pepocessng (Defnton Dstance along ow) (D) PCA (Zhang & Zhou, 005) combnng ou pepocessng (Defnton Dstance along ow and column) GDPCA (Kong et al., 005) combnng ou pe-pocessng (Defnton Dstance along column) GDPCA (Kong et al., 005) combnng ou pe-pocessng (Defnton Dstance along ow) GDPCA (Kong et al., 005) combnng ou pe-pocessng (Defnton Dstance along ow and column) wo-stage DPCA (defne dstance along ow) wo-stage DPCA (defne dstance along column) wo-stage DPCA (defne dstance along ow and column) able 8. Pefomances of two-stage DPCA and seveal methods n lteatues unde dffeent azmuth ntevals (%) Fom ths table, we also see that ecognton pefomances of two-stage DPCA acheve the best unde the 180 azmuth ntevals.

21 18 Pncpal Component Analyss Engneeng Applcatons Howeve, wth the azmuth nteval deceasng, ecognton pefomances of two-stage DPCA become wose. hs s because numbe of tanng samples at ntevals becomes smalle; t s not useful fo estmatng the covaance matx exactly, thus esultng n ecognton pefomance loss. Whle n lteatues (Byant & Gabe, 1999; Han et al., 003; Han et al., 004), the classfe of SV s employed, ths s ft fo a small sample classfcaton. Compasons of two-stage DPCA and the (D) PCA and GDPCA methods unde dffeent azmuth ntevals, we thnk that ou pe-pocessng method s vey effcent and two-stage DPCA s compaable to (D) PCA and GDPCA. 6. Conclusons An effcent SAR pe-pocessng method s fst poposed to obtan tagets fom backgound cluttes, and two-stage DPCA s poposed fo SAR mage featue extacton n ths chapte. Compasons wth DPCA and othe appoaches pove that two-stage DPCA combnng wth ou pe-pocessng method not only deceases shaply featue dmensons, but also nceases ecognton ate. oeove, t s obust to the vaaton of taget azmuth, and deceases the pecson equements fo the estmaton of taget azmuth. 7. Refeences Byant,. & Gabe, F. (1999). SV Classfe appled to the SAR publc data set, SPIE, Vol. 371, No. 4, (Apl 1999), pp Gonzalez, R.C. & Woods R. E. (00) Dgtal Image Pocessng, Publshng House of Electoncs Industy, Bejng, Chna. Han, P.; Wu, R. B. & Wang, Z. H. (003). SAR Automatc aget Recognton based on KPCA Cteon, Jounal of Electoncs and Infomaton echnology, Vol. 5, No.10, (Octobe 003), pp Han, P.; Wu, R. B. & Wang, Z. H. (003). SAR aget Featue Extacton and Automatc Recognton Based on KFD Cteon, oden Rada, Vol. 6, No.7, (July 004), pp Kong, H.; Wang, L. & eoh, E. K. (005) Genealzed D Pncpal Component Analyss fo face mage epesentaton and ecognton, Neual Netwoks, Vol. 18, (005), pp usman, S. & Ke, D. (1996). Automatc Recognton of ISAR Shp Images, IEEE ansactons on Aeospace and Electonc Systems, Vol. 3, No. 4, (Octobe 1996), pp Ross,.; Woell, S. & Velten, V. (1998). Standad SAR AR evaluaton expement usng the SAR publc elease data set, SPIE, Vol.3370, No.4, (Apl 1998), pp Yang, J.; Zhang, D. & Fang, A. F. (004). wo-dmensonal PCA: a new appoach to appeaance-based face epesentaton and ecognton, IEEE ansactons on Patten Analyss and achne Intellgence, Vol. 6, No. 1, (Januay 004), pp Zhang, D. Q. & Zhou Z. H. (005). (D) PCA: wo-dectonal two-dmensonal PCA fo effcent face epesentaton and ecognton, Neuocomputng, Vol. 69, (005), pp Zhao, Q. & Pncpe, J. C. (001). Suppot Vecto achne fo SAR automatc taget ecognton, IEEE ansactons on Aeospace and Electonc Systems, Vol. 37, No., (Apl 001), pp

22 Pncpal Component Analyss - Engneeng Applcatons Edted by D. Panya Sanguansat ISBN Had cove, 30 pages Publshe Inech Publshed onlne 07, ach, 01 Publshed n pnt edton ach, 01 hs book s amed at asng awaeness of eseaches, scentsts and engnees on the benefts of Pncpal Component Analyss (PCA) n data analyss. In ths book, the eade wll fnd the applcatons of PCA n felds such as enegy, mult-senso data fuson, mateals scence, gas chomatogaphc analyss, ecology, vdeo and mage pocessng, agcultue, colo coatng, clmate and automatc taget ecognton. How to efeence In ode to coectly efeence ths scholaly wok, feel fee to copy and paste the followng: Lpng Hu, Hongwe Lu and Hongcheng Yn (01). Automatc aget Recognton Based on SAR Images and wo-stage DPCA Featues, Pncpal Component Analyss - Engneeng Applcatons, D. Panya Sanguansat (Ed.), ISBN: , Inech, Avalable fom: Inech Euope Unvesty Campus SeP R Slavka Kautzeka 83/A Rjeka, Coata Phone: +385 (51) Fax: +385 (51) Inech Chna Unt 405, Offce Block, Hotel Equatoal Shangha No.65, Yan An Road (West), Shangha, 00040, Chna Phone: Fax:

23 01 he Autho(s). Lcensee IntechOpen. hs s an open access atcle dstbuted unde the tems of the Ceatve Commons Attbuton 3.0 Lcense, whch pemts unestcted use, dstbuton, and epoducton n any medum, povded the ognal wok s popely cted.