The Study of Remote Sensing Image Classification Based on Support Vector Machine

Transcription

1 Sensors & Transducers 03 by IFSA The Study of Remote Sensng Image Classfcaton Based on Support Vector Machne, ZHANG Jan-Hua Key Research Insttute of Yellow Rver Cvlzaton and Sustanable Development & College of Envronment and Plannng, Henan Unversty, Kafeng 47500, Chna College of Economy and Trade, Henan Insttute of Engneerng, Zhengzhou 459, Chna Receved: September 03 /Accepted: 5 October 03 /Publshed: 30 November 03 Abstract: Ths paper proposed a remote sensng mage classfcaton method based on Support Vector Machne (SVM). Because t s a convex optmzaton problem, accordng to the propertes of convex optmzaton we can know that the values of local optmal soluton must be the global optmal soluton, whch s the other classfcaton methods don t have. In the autonomous learnng classfcaton and automatc processng aspects the Support Vector Machne (SVM) shows ts effectveness. In ths paper t used the IKNOS remote sensng mage and selected crops, resdents, water, grass, land transportaton and so on fve bg classes for tranng. The tranng results show that Support Vector Machne (SVM) has hgher recognton rate n classfyng the remote sensng mage. Copyrght 03 IFSA. Keywords: Support Vector Machne (SVM), Remote sensng mage, Classfcaton.. Artcle Sze and Formats Snce the 970s, wth the rapd development of network technology, people pay more and more attenton to computer automatc classfcaton technology of remote sensng mage. Also remote sensng technology can receve spectral nformaton reflected by the ground whch s a knd of passve remote sensng technology. Furthermore, current remote sensng technology s beng developed n the drecton of three multple levels and three hgh levels. Three multple levels are known as multple perspectves, multple sensors and multple platforms, and three hgh refer to the hgh spectral resoluton, hgh spatal resoluton and hgh relatve resoluton. In recent years, facng the massve remote sensng data and abundant resources, how to utlze these resources effectvely s the emergent problem that needs to be addressed n the study []. [Rase the queston]therefore, a large number of applcatons of remote sensng mage classfcaton based on data mnng technology emerge and how we can classfy and manage these remote sensng data effcently and accurately become a hot pont n the research of remote sensng mage n the recent years. At present, many domestc scholars have a lot of researches on remote sensng mage classfcaton management. Among them, there are manly fuzzy sets and rough sets method based on target decomposton, Bayesan classfcaton method, neural network classfcaton method, spectral angle mappng method based on materal characterstcs, I mage statstcal characterstcs, maxmum lkelhood method and mnmum dstance method, etc. These methods n the study of remote sensng mage classfcaton have been wdely used. But when they face the hgh spectrum and spectral data, they stll have many dffcultes. In addton, they also have some defects. For example, the fuzzy set classfcaton method needs to rely on experence or expertse to gve ts degree of membershp so that there s a hgh subjectvty. As a result, the prmary ssue needs to be solved for fuzzy system s accuracy and comprehensblty. What s more, when usng fuzzy 46 Artcle number P_536

2 clusterng technology to cut up the remote sensng mage, the frst thng s to choose varables accordng to the problem defnton and types of data. Whereas rough sets method s sutable for classfyng problems ndcated by data. If the method doesn t be used n a rght way, t may have a great mpact on the classfcaton results. It s hard to meet the hypothess of beng wth ndependent class condton when we use Bayesan classfcaton method n dealng wth large-scale classfcaton. At the same tme, t s dffcult to select the evaluaton functon for the method. Also, the complexty of learnng and tranng n ths method s relatvely large. Neural network classfcaton method n classfcaton s prone to fall nto local mnmum and slow convergence speed phenomena and so on. Apart from the above defects, the tradtonal classfcatons of machne learnng methods have a hgh expectaton of the regularty of data. They are also be used under the hypothess of the number of the sample s nfnte. But the data that s classfed by remote sensng mage technology often can t meet the above requrements. It s often dsplayed hgh lattudes, varablty and small sample characterstcs. For these data usng tradtonal machne learnng methods are hard to get deal classfcaton results. But wth the contnuous development of remote sensng technology, the classfcaton of remote sensng technology s also put forward hgher request. Therefore how to acheve effcent and accurate classfcaton of remote sensng mage management becomes the domestc and foregn scholar s concern [-3]. In vew of ths, ths paper comes out wth a research on remote sensng mage based on support vector machne. As t s a convex optmzaton problem, the value of local optmal soluton must be global optmal soluton from the propertes of convex optmzaton whch s other classfcaton methods doesn t have. In addton, support vector machne shows ts effectveness n terms of achevng selflearnng classfcaton and automatc processng n the study of mage classfcaton [4]. Ths paper s dvded mage classfcaton process nto three phases, ncludng preprocessng, mage classfcaton and accuracy evaluaton. () In the preprocessng phase, t s manly to do the geometrc correcton for multspectral mage and de-nose the remote sensng mage wth hgh resoluton. Moreover, t uses the blnear samplng method to select the mages wth the same resoluton. In the mage classfcaton phase, t takes advantage of some spectrum characterstcs of multspectral mages and the best wndow texture feature to generate the tranng mage nformaton. It s necessary to normalze the tranng data n order to avod some ssues n the tranng process, such as dffcultes of kernel functon compute nner product; mage feature s too bg or too small ssue. Before extract mage features for tranng, t stll need to use the grd search method to determne the parameters of the kernel functon so that t can be better to let low dmensonal space mapped to a hgh-dmensonal feature space. At last, the tranng sample wll be mapped to the correspondng feature space by usng kernel functon. Ths s what we use to get the classfcaton result from SVM [5]. (3)The SVM classfcaton result wll be quanttatve evaluate by means of mxed ponts, leakage ponts, recognton rate and other ndexes. Through studed n IKNOS remote sensng mage, crops, resdents, water, grass, land transportaton whch are the fve bg classes are selected for tranng. Tranng results show that usng support vector machne (SVM) to classfy remote sensng mage can obtan hgher recognton rato.. Support Vector Machne SVM (Support vector machne, SVM) s frst proposed by Cornna Cortes and Vapnk n 995. It has ts own advantages n dealng wth small sample, nonlnear and hgh dmensonal pattern recognton. It can be appled to the functon fttng other machne learnng problems. It s bult n the structural rsk mnmum prncple of statstcal learnng theory and the bass of VC dmenson theory. Accordng to the lmted sample nformaton, t can fnd the best compromse between learnng ablty and complexty And t hope to get more wdely promote capacty from ths [6-7]... Lnear SVM The SVM evolved from the optmal classfcaton of lnearly separable case. The optmal classfcaton requres the classfcaton lne not only can classfy two knds n a rght way, but also can make the classfcaton nterval to be the bggest one. The tranng samples that are over the nearest pont n the two classfcatons surface and parallel to the hyper planes H and H of the optmal classfcaton are called support vectors. As shown n Fg.. Fg.. The graphcal llustraton of lnear SVM. In the above fgure, H and H are support vectors. The classfcaton nterval determned by hyper plane B, b and b s bgger than the one determned by B, b and b so that B s the hyper 47

3 plane of optmal classfcaton. Also the plane determned by support vectors H and H s the optmal hyper plane. We suppose the sample set s ( x, x), ( xl, xl), where x R n, y = {, }, =, l. Accordngly, the hyper plane make the postve and negatve nput n the tranng sample locate on ether sde of the hyper plane. Then there should be parameters ( wb, ) make f w x+ b f( x) = () f w x + b So the optmal hyper plane should make the bggest nterval between two classfcatons. That s: Margn = w () To make t maxmum, n other words, t s to attan the mnmum of ths equaton: w Lw ( ) = Its constrants should meet Eq. (). Fnally, the optmal classfcaton ssue can be expressed to the followng constrant optmal ssues: Through the above equatons, we can attan the α that meet the constrants. And we named the correspondng samples whose α are nonzero as support vectors. So the classfcaton plane s determned by these support vectors. Support vectors and classfcaton plane are shown n Fg.. In many case, tranng data set s lnear nseparable. So Vapnk and others proposed to use generalzed classfcaton to solve ths problem by ntroducng a relaxaton factor. At ths tme, the object functon transforms to: N w k Lw ( ) = + C ξ = (6) The constrants transform to: f w x + b -ξ f( x ) =, (7) f w x + b + ξ where ξ 0 s the relaxaton factor, C s the mstake punsh component and C > 0 s the constant, whch can be used to control the balance between the ntegral pont of the machne and the complexty and the degree of punshment to the samples that dstrbuted n wrong way. Φ ( w) = w = ( w w) y (( w x ) + b), =,..., l (3) w x + b = 0 w x + b = w x + b = + Defne the Lagrange functon: l, (4) = Lwb (,, α) = w α ( y (( x w) + b) ) where α s the Lagrange multpler. And these multplers meet the condton of nonnegatve. Therefore, through the partal dervatve of these three parameters: Lwb (,, α) = 0 b Lwb (,, α) = 0 w l ay = 0 = l w = α y x = l l W( α) = α αα ( ) jyyj x xj =, j= (5) Fg.. Support vectors and classfcaton plane... Non-lnear SVM To solve non-lnear classfcaton problem by usng SVM s to ntroduce a kernel functon that make the non-lnear sample nput vector mapped to a hgh-dmensonal feature space to convert non-lnear to lnear. Then we create an optmal hyper plane n the feature space. The classfcaton plane after changed shows as below: The optmal equaton: T w φ ( x) + b = 0 (8) Q( α) = N N N α α yy ( x ), ( x ) α j j φ φ j = = j = (9) 48

4 N = 0 α 0 =,,, N (0) = α y In Eq. (9), we should attan ts maxmum value and φ( x ), φ ( x ) s ts space nner product. Eq. (0) j s the constrant condtons [4]. If we can construct a kernel functon k( x, x j) that equals to the nner product φ( x ), φ( x ) after changed n the orgnal j space. Now the kernel functons that wdely used n the nonlnear classfcaton problem are manly the followng three ones: ) Sgmod kernel functon k( x, x j) = tanh[ vx ( x) + t ] ) Radal bass functon (RBF) kernel kxx x x σ (, ) = exp( ) 3) Polynomal kernel functon kxx = x x + t (, ) [( ) ] q 3. Optmal Selecton Methods for the Parameters of SVM 3.. Selecton of Kernel Functon Through the analyss, we found that dfferent kernel functons have great nfluence on the performance of SVM. The kernel functon s also one of the parameters can be adjusted n SVM. The selecton of kernel functon for SVM classfer can make a sgnfcant dfference n ts classfcaton ablty and type. Because of the kernel functon, the parameters of the kernel and hgh-dmensonal mappng space have the correspondng relatonshp, when dealng wth the classfcaton problems; we can only choose the approprate kernel functon and kernel parameters and mappng of hgh-dmensonal space to make t possble to get the separator wth good learnng ablty and generalzaton ablty. Ths paper mentoned several common forms of kernel functon n the prevous researches. As RBF kernel has good learnng ablty under large sample, low dmenson and so on crcumstance, t s wdely used accordngly. RBF kernel functon s rewrtten as shown n the above. precson and promote ablty of the SVM [6], we adopt RBF as our kernel functon s as hard as to select ts parameters and ts error factors. When we select the parameters for the tradtonal kernel functon, we generally use the genetc algorthm to acheve t. Although the algorthm showed a lot of advantages n solvng the optmal problems, t also has some own shortcomngs can t be overcome, whch s manly showed n the followng two aspects. On the one hand, n dealng wth dfferent problems, the genetc algorthm has to redesgn mutaton, selecton, crossover operator, etc. On the other hand, the operaton of the genetc algorthm s very complcated. Its effcency s very low n most cases. Consderng the above defcences of the genetc algorthm, by contrast, ths paper ntroduces the grd search method for kernel parameters C and σ optmal selecton when selectng the parameters of SVM. The operaton of ths method s easy to understand. So t has been wdely used n the optmal problem. In ths paper, we chose the grd search method for optmal selecton of the parameters. Ths method conssts of the followng steps. () Frst of all, we have defned the scope of the selected parameters C and σ. Generally, the selecton for C (,, ) and / σ (,,... ) can meet the requrement based on experence. () Do the rough grds search. At frst, we set the step length to, so the two-dmensonal coordnate system can be constructed based on the parameters C and σ. For each set of parameter values on the grd, t s a set of potental soluton and also only represents a set of SVM parameters. (3) Accordng to the K fold cross valdaton method, we can calculate the accuracy for all parameters n the predcton process and we can use the contour to draw them out, so we can get a contour map, and then we can to determne the optmal kernel parameters C andσ. (4) In order to get the search results more accurate, we should also do a fne grd search after the rough grd search. That s to say, we should select an area to search n the panted contour map. Normally we wll choose the hgher accuracy of regonal predcton. Ths means we reduce the step length for a second search. For example, for some gven sample set, we can attan the optmal kernel parameters C and σ after we do the rough search usng as our step length. At ths tme, 0 C =, σ = so we can reduce the range of the grd 8 5 search to C ( ~ ), / σ ( ~ ).Then the search step length becomes 0. at ths tme [7]. 3.. Optmal Selecton of Kernel Functon After we selected the kernel functon, we need to optmze the parameter of kernel functon. As the error penalty factor C and σ are the key factors that affect the performance of the SVM and these parameters have great mpact on classfcaton 4. Experments 4.. The Experment Process In ths paper, the experment process manly ncluded data acquston, feature extracton, data 49

5 processng, parameter selecton and tranng. The specfc operaton s descrbed n the followng. The specfc process frame s shown n Fg Gettng Expermental Data The data used n the paper s from IKNOS remote sensng mages, and t selected the crops, resdents, water area, grassland, traffc land fve categores to tran. In the tranng process, the fve types are labeled A, B, C, D, F and each has 000 samples. It used IKNOs remote sensng mages to do the experment, they cover the scope extensvely. The crops and traffc stes belong to man-made scene, waters and grassland belong to typcal natural scene, at last the resdents represent the most complcated nature objects such scenaro. Because the data s from dfferent angles, the database has strong representatve and challengng. So t can be used to conduct a comprehensve and credble evaluaton. Because n the process of tranng t selected fve knds of sample to do the classfcaton, so t needs to construct fve classfers. Durng the experment process t selected the RBF kernel functon. The classfcaton effect depends on the Kernel parameters and penalty factor C. In the experment t used the grd search method to select the Kernel parameters. The sepecal operaton can be seen n three part []. Extractng mage texture feature Data normalzaton process Image classfcaton based on spectral characterstcs The classfcaton based on sngle wndow texture The mage classfcaton based on the wndow texture The selecton of Kernel parameter The SVM classfcaton The precson evaluaton Fg. 3. SVM-based Remote Sensng Image classfcaton process Mult-wndow Texture Feature Extracton and Data Processng Because the objects on the earth have dfferent spectrum, they correspond to dfferent optmal texture wndow. So ths artcle chooses the texture wndow to flter. In ths artcle, we adopt the method based on dstance to measure the separablty of the wndow. Through a lot of the emprcal results, t showed that those crops, resdents, waters, grassland, and traffc land were selected as 3 3, 5 5,, 3 3, 5 5 the optmal classfcaton wndow. Texture s the concept than can be seen n the analyss of the mage. It refers to the mage pxel gray level or refers to some change n the color. In ths paper t manly studed how to better access to the mage texture features n order to do the future analyss, understandng and classfcaton. In the dgtal mage texture feature extracton t manly has three ways: the texture features extracton based on sgnal process; the texture features extracton based on structuralzaton; the texture features extracton based on statstcs. When carryng on the texture feature extracton the sx components of texture feature correspond to the sx propertes from psychology Angle. They are: neat degrees, drecton, roughness, contrast, and lne drecton [7]. In ths paper t s manly based on gray level cooccurrence matrx texture feature to fnsh the feature extracton. In gray level co-occurrence matrx t studed the two pxels combnaton about the gray confguraton. It s one of the most representatve calculaton methods of thesecond order statstcs texture feature. To the decded dstance d and drecton θ. Among them n the drecton θ of straght lne, for example, such as 0,45,90,35, etc. In pxel, and then the probablty can be normalzed to p, whch can be expressed as the value of gray level co-occurrence matrx (, j ).We put L as the grayscale, so t can be expressed as the gray level co-occurrence matrx, whch actually s the jont hstogram of the two pxels. We use the gray level co-occurrence matrx can deduce a seres of characterstcs statstcs. In ths artcle t used the Haralck [8-9] and the gray level co-occurrence matrx to calculate the statstcs. 50

6 In order to avod the kernel functon s nner product computaton dffcultes appeared n the process of tranng, mage feature s too bg or too small, the normalzed processng to the tranng data. Before the mage feature extractng for tranng, but also the grd search method s used to determne the parameters of the kernel functon so that we can better to low dmensonal space s mapped to hghdmensonal feature space. The last wll be the tranng sample to the correspondng feature space by kernel functon mappng, n order to get the SVM classfcaton results The Selecton for the Kernel Parameters Because the kernel functon, the Kernel parameters and the hgh dmensonal mappng space they exst correspondng relaton, n the classfcaton problem we only choose the proper kernel functon, the Kernel parameters and hgh dmensonal mappng space, we could get the separator that has good learnng ablty and generalzaton ablty. Because the error penalty factor C and σ are the key factors that affect the performance of the SVM [5]. These parameters wll have great nfluence on the classfcaton accuracy and generalzaton ablty of SVM. Ths artcle adopted the grd search method to select the Kernel parameter C and σ. Ths operaton of the method s smple and easy to understand, so t has been wdely used n the optmzaton problem. The selecton method of the parameter can be seen n 3. above The Experment Result and Analyss The data used n the paper s from IKNOS remote sensng mages, and t selected the crops, resdents, water area, grassland, traffc land fve categores to tran. Because the objects on the earth have dfferent spectrum, they correspond to dfferent optmal texture wndow. Therefore, ths artcle chose the texture wndow to flter. In ths artcle, we adopt the method based on dstance to measure the separablty of the wndow. Through a lot of the emprcal results, t showed that those crops, resdents, waters, grassland, and traffc land were selected as 3 3, 5 5,, 3 3, 5 5 the optmal classfcaton wndow. Ths artcle used the multwndow texture to classfy the remote sensng mage nformaton. Ths paper dd the mult-wndow texture feature extracton for the data collected, the extracton process, seen n 4.3. Then t put the remote sensng mage characterstcs based on mult-wndow texture gotten n 4.3 and parameters decded n 4.4 as nput for tranng. Ths paper dd the experment by adoptng the methods that are based on spectral characterstcs, based on the sngle wndow texture feature and texture feature extracton method based on wndow experment, and each experment repeated 0 rounds, and the average recognton accuracy of each round, as the fnal results. Then we compared the predct results gotten from the methods that the Kernel parameter was gotten from the experence method and the grd search method. In each round experment, from the data sample of each type we randomly selected the two-thrds of the mages as the tranng set, and the remanng mages as the test. Ths paper put the average relatve error, the recognton rate, the mean square error (MSE) and the average tranng tme as the evaluaton standards. The average tranng tme s about 0 rounds experments. The expermental results are shown n Table, Table and Table 3 [7]. Table. The mage classfcaton results of dfferent feature extracton methods. Evaluaton standard MAPE MSE IR(%) Extracton method Spectrum Sngle wndow texture Mult-wndow texture In Table the experment was based on the spectrum, sngle wndow texture and mult-wndow texture feature extracton method. Through the comparson of the three feature extracton methods, we can fnd the most effectve feature extracton n the remote sensng mage classfcaton. The expermental method s shown n secton 4.3, respectvely usng spectrum, sngle wndow texture and mult-wndow texture feature extracton method. Expermental parameters are penalty factor C and σ. The expermental used the mean relatve error, the correct recognton rate and the mean square error (mse) as evaluaton standards. We can see from the results n the table: () based on mult-wndow texture feature extracton methods for remote sensng mage classfcaton, ts average relatve error and mean square error (MSE) are concentrated, the average relatve error between ~ 436.5, the mean square error (MES) concentraton between 3.4 ~ 3.8. () based on spectrum and sngle wndow texture feature extracton method, the average relatve error and the mean square error are large, the average relatve error are and respectvely, and the mean square error are 3.87 and respectvely. They were sgnfcantly hgher than those under the multwndow texture feature. (3) The dentfcaton accuracy of the method proposed n ths paper s 5

7 above 95%, almost all were sgnfcantly hgher than those based on spectrum and sngle wndow texture feature extracton method. The reasons for the result are: () As the objects on the earth have dfferent spectra, dfferent optmal texture wndow characterstc, t selected the mult-wndow texture feature extracton methods, whch s more n lne wth the characterstcs of remote sensng mages. () Before the experment t used the grd search method to determne the Kernel parameters so that we can successfully map the low dmensonal space to hghdmensonal feature space. Ths s helpful to mprove the recognton accuracy. (3) The combnaton of the mult-wndow texture feature extracton and support vector machne (SVM) realzed the comprehensve utlzaton of remote sensng mage spectral nformaton and spatal nformaton, so t can make the classfcaton result have the space coherence, so that the average relatve error and mean square error (MSE) showed more concentrated. Table. The comparson of classfcaton performance. Parameter determnng method Determne the Kernel parameter by experence Evaluaton standard MAPE MSE Tranng tme (S) Determne the Kernel parameter by grd search In Table t shows the predctve results gotten from the methods that the Kernel parameter was gotten from the experence method and the grd search method, from whch we can test the effectveness of the grd search method to determne the Kernel parameter. The experment method s to use the experence method and the grd search method to determne the Kernel parameter. In each round experment, we randomly selected the twothrds of the mages as the tranng set, and the remanng mages as the test. The experment parameters are error penalty factor C, σ and wndow texture. In the experment t used the mean relatve error and the mean square error as the evaluaton standards. From the results we can see the average relatve error and the mean square error are and respectvely by usng the grd method, but under the fxed parameters they are 4765 and The grd search method showed great advantage both n the mean relatve error and the mean square error [9].The reasons for the results are: () the grd search method selects the Kernel parameter of SVM accordng to the dstrbuton nformaton of the nput data, whch makes the support vector machne have better adaptablty. () Usng the grd search method to determne the Kernel parameters, t better can map the low dmensonal space to hgh-dmensonal feature space. Ths s helpful to mprove the recognton accuracy. Table 3. The comparson of the tranng tme. Sngle wndow Mult-wndow Tranng tme Spectrum texture texture Average tme (s) In table 3, t showed the tranng tme under the spectrum, sngle wndow texture and mult-wndow texture feature extracton methods, from whch can test the effcency of the method proposed n ths paper. Expermental method stll used support vector machne (SVM) classfcaton method, adoptng the grd search method to determne the Kernel parameters. In the table t showed the tranng tme of the frst fve tmes and the average tranng tme of the 0 rounds experments. The expermental parameters are error penalty factor C,σ and wndow texture. The experments used the tranng tme of each round and the average tranng tme as evaluaton crtera. From the expermental results we can see that mult-wndow texture feature extracton show great advantage n the optmal tme, whch s less than that n the sngle wndow texture extracton method. But the optmzaton tme of the sngle wndow texture feature extracton method s sgnfcantly lower than the spectrum feature extracton method. From the optmzaton tme of the three methods, we can say the optmzaton tme of the mult-wndow texture feature extracton method sgnfcantly lower than the other two methods. The results of the above reasons are: () for the multwndow texture feature extracton methods for feature extracton, t s more n lne wth the characterstcs of remote sensng mages. () the mult-wndow texture feature extracton s manly based on the texture feature of gray level cooccurrence matrx to complete work. In gray level cooccurrence matrx t studed the two pxels combnaton about the gray confguraton. Through the matchng of gray t can mprove the optmzaton effcency and accuracy. 5. Dscusson Ths paper presented a support vector machne (SVM) mult-wndow texture remote sensng mage classfcaton method. It selected IKNOS remote sensng mages, and choose crops, resdents, water area, grassland, traffc land fve categores to tran. Then t gave the mult-wndow texture feature of the extracted tranng samples. It used the grd search 5

8 method to determne the parameters of the kernel functon so that t can be better to let low dmensonal space mapped to a hgh-dmensonal feature space. At last, the tranng sample wll be mapped to the correspondng feature space by usng kernel functon. Ths s what we use to get the classfcaton result from SVM. The expermental results showed that: based on mult-wndow texture feature extracton methods for remote sensng mage classfcaton, ts average relatve error and mean square error (MSE) are concentrated, and ts recognton rate s sgnfcantly hgher than those based on spectrum and sngle wndow texture feature extracton method. The SVM showed great advantage n classfyng management, but the SVM faces great dffcultes n constructng multple classfer. So the constructon of the SVM classfer s stll the focus of the next research. Reference []. Png Ja, Hatao L, Hu Ln, The research of Multsource remote sensng mage classfcaton based on the SVM, Scence of Surveyng and Mappng, Vol. 33, No. 4, 008, pp. -3. []. Feng Lu, Lmn Zhang, Rufeng Zhang, The research of Remote sensng mage based on rough sets and support vector machne (SVM) classfcaton algorthm, Journal of Electronc Desgn Engneerng, Vol. 0, No., 0, pp [3]. Chengwang Xe, Comparatve study of dfferent types of support vector machne (SVM) algorthm, Small Mcrocomputer System, Vol., No., 008. [4]. Zhle Gong, Dexan Zhang, An text categorzaton algorthm of an mproved support vector machne, Computer Smulaton, Vol. 7, No. 6, 009, pp [5]. Vapnk Vladmr N., The Nature of Statstcal Learnng Theory, Sprnger-Verlag, New York, Inc, 995. [6]. Gn Gao, Image classfcaton based on SVM, Master thess, Northwestern Unversty, 00. [7]. Zhan Fafa, L Wezhong, Lu Luye, Technologes of extractng land utlzaton nformaton based on SVM method wth mult-wndow texture, Journal of Remote Sensng, Vol. 6, No., 0, pp [8]. Haralck R M., Statstcal and Structrual Approaches to Texture, Proceedngs of IEEE, 957. [9]. Bo Yn, Jngbo Xa, Fu Ka, Network traffc predcton research based on IPSO chaos support vector machne (SVM), Computer Applcaton Research, Vol. 9, No., 0, pp Copyrght, Internatonal Frequency Sensor Assocaton (IFSA). All rghts reserved. ( 53