MULTISPECTRAL IMAGE CLASSIFICATION USING BACK-PROPAGATION NEURAL NETWORK IN PCA DOMAIN S. Chtwong, S. Wtthayapradt, S. Intajag, and F. Cheevasuvt Faculty of Engneerng, Kng Mongkut s Insttute of Technology Ladkrabang, Bangkok, 0520, Thaland Phone: (662)-3264203-4 ABSTRACT Recently, n classfcaton of multspectral remote resensng mage by usng back-propagaton neural network (BPNN), all bands of mage must be used for tranng and classng. Dsadvantage of the mentoned method not only requres more tme for tranng and classng but also more complexty. In ths paper, to decrease the mentoned dsadvantage, prncpal component analyss (PCA) s appled to reduce dmensonalty of multspectral remote sensng mage as preprocessng. The frst three prncpal components whch contan nformaton more than that of orgnal mages of 95 percents are then used for tranng and classng. Landsat 7 satellte TM mage n vsble bands of 6 s mplemented to test results. We compare results of the classfed multspectral remote sensng mage as the proposed method wth those of one as maxmum lkelhood classfer wth prncpal component analyss (MLC-PCA) n term of accuracy percentage. Our results show that classfcaton usng the threelayer back-propagaton neural network wth prncpal component analyss (BPNN-PCA) s better than MLC-PCA and also t s lower complexty certanly.. INTRODUCTION Havng a munber of researchers has been reported to apply and verfy BPNN for classng the multspectral remote sensng mage [3], [4], [5], [6], [7] and [8]. Ther reports have been used nput data n spatal doman to nput layer of BPNN. The more the number of nput data bands, the more the number of nodes n nput layer are. As mentoned reasons, then the processng of tranng and classng of classfer not only consumes more tme but also ncreases complexty. One way to reduce tme for tranng and classng together wth complexty s that dmensonalty of the number of data bands n spatal doman must be reduced by applyng PCA algorthm [] and [2] whch s transformng mage from spatal doman nto PCA doman. Reducng dmensonalty s only selectng the frst three prncpal component mages as nput of BPNN-PCA. To man the hghest clusterng property of BPNN-PCA, we select nput codng scheme as normalzaton codng [3] to ncrease accuracy percentage whch s used to evaluate results comparng MLC-PCA. Ths paper s organzed as follows. In Secton 2 PCA s descrbed; n Secton 3 MLC-PCA [5] and BPNN- PCA for tranng and classng are descrbed. The expermental results and concluson are gven n Secton 4 and 5, respectvely. 2. PRINCIPAL COMPONENT ANALYSIS Prncpal component analyss (PCA) [], [2] s an establshed statstcal method for reducng the dmensonalty of data. It s lnear transformaton to transform the orgnal data onto the new data called that prncpal component. Each component contans a dfferent varance of data and t s also uncorrelated. Normally, the frst component contans the most varance. One contans the hghest nformaton content correspondng wth the hghest contrast. The frst three prncpal components are employed as nput of BPNN-PCA. Total varance of them s more than 95 %. By the mentoned method, ones contan more nformaton detal than that of the three bands of orgnal mage. The procedure of PCA method carres out as the followng steps: ) Calculate the mean vector m of a pxel vector x, =,2,..., N as N N = m= x. 2) Calculate the covarance matrx defned generally by = E{( m)( m ) T } x x x s a pxel matrx, E s the where = {, 2,..., N} expectaton operator and superscrpt T denotes the transpose. The covarance matrx s gven by N T = ( x m)( x m ). N = 3) Calculate the egenvalues λ of covarance matrx by solvng the characterstc equaton λi = 0.
ID:489-5 where I s the ( N N) -sze dentty matrx. 4) Sort the egenvalues havng the general form as followng λ λ 0 0 L 0 0 λ 0 0 L 2 = 0 0 λ3 L 0 M 0 0 0 L λ N where λ > λ2 > K > λn. The number of egenvalues s the same as the number of nput data bands. The frst egenvalue λ contans the most varance the hghest contrast. The other egenvalues are usually much smaller. 5) Generate each prncpal component of PCA mages by projectng each pxel of orgnal mage onto the egenvectors denoted by N Y = y = a x = a x + a x + L + a x. j j 2 2 N N j= where a j s egenvectors by that =, 2,..., N. That s, the new brghtness value of each pxel n the PCA mages s gven by a weghted sum of the correspondng pxels n each of the spectral bands. 3. CLASSIFICATION METHODS Image classfcaton s automatcally procedures that to categorze all pxels n an mage nto land cover classes. In ths paper proposes two methods of supervsed classfcaton, maxmum lkelhood and neural network classfer, to perform classfy mult-spectral mages. 3. Maxmum Lkelhood Classfer [5] Maxmum lkelhood classfer (MLC) s a parametrc classfer that reles on the second-order statstcs of a Gaussan probablty densty functon model for each class. The basc dscrmnant functon for each class s g ( ) = p( ω ) p( ω ) t exp ( U ) ( U ) p( ω ) 2 = n /2 (2 π ) /2 Where n s the number of bands for ths case of 3, s the nput vector, U s the mean vector of class for ths case of 6, and s s the covarance matrx of class, that x µ x µ 2 2, U M M x µ n n σ σ L σ 2 n σ σ L σ 2 22 2 n = M M O M σ σ L σ n n 2 nn = = The values n the mean vector, U, and the covarance matrx are estmated from the tranng data. P µ =,, 2,..., j x j = n jl P l = and P σ = ( x µ )( x µ ), jk jl j kl k P l = j =, 2,..., n; k =, 2,..., n where P s the number of tranng patterns n class. Then dscrmnant functon can be reduced by takng natural log and dscardng the constant π term to T = g ( ) ln ( U ) ( U ) 3.2 Neural Network Classfer One of neural network archtecture whch s sutable to apply for classng multspectral remote sensng mage s a three-layer back-propagaton network. The frst layer s nput layer whch conssts of nodes of 3 so as to correspond wth nput data, the frst three prncpal component, and the output layer conssts of nodes of 6 so as to correspond wth a desred classes of 6 water, sol, mountan, forest, urban and buldng. As mentoned, the sngle hdden layer s then selected and here conssts of nodes of 4. Actvaton functon s sgmod one defned as f ( NET) = e NET where NET s the sum of weghted nputs to the processng node. In ths paper, nput codng scheme s normalzaton codng selected [3] because of mantanng the hghest cluster-
ID:489-5 ng property. The coded normalzed value whch s fed to nput layer of network for range of 0 to can be obtaned as follow : nor = f fmax fmn fmn where f mn and f max are the mnmum and maxmum values of each of prncpal component. 4. EPERIMENTAL RESULTS The data used to test all of results n ths paper s the satellte magery acqured by the Landsat 7 n Enhanced Thematc Mapper+ system n Kanjanaburee provnce, Thaland. Also we use partcularly vsble bands, 2, 3, 4, 5 and 7. The sze of mage s 8296 887 wth 8 bt resoluton whch s 256 gray levels. From the data, we selected a sub-regon of 52 56 pxels. The selected area s shown n Fg.. All of the orgnal mages as mentoned n spatal doman s transformed nto PCA doman by usng PCA algorthm so as to reduce dmensonalty. The resulted mages consst of the frst three component mages shown n Fg. 2. The frst component mage (PC) s the largest egenvalue whch mean that t contans the most nformaton content. The other component mages (PC2 and PC3) are lower. We use the frst three component mages as nput data by whch each component mage s normalzed and fed to nput layer of neural network. Before usng MLC and BPNN to classfy, tranng procedure s frst performed. All of the frst three prncpal component mage s used for tranng and test. Fg. 2 only shows the PC selected pxel areas whch conssts of classes of 6 water, sol, mountan, forest, urban and buldng. The number of selected pxels of each class s ndcated n Table for tranng and test of both MLC and BPNN. For MLC, the statstcal numercal values of the frst three prncpal component data n each class mnmum values (mn), maxmum values (max), mean values (mean) and standard devaton (std) s ndcated n Table 2. Also Table 3 ndcates covarance matrx of each class. The numercal results to test the accuracy of both MLC and BPNN are ndcated n table 4 and 5, respectvely. From such results, see that BPNN-PCA shows hgher accuracy enough when comparng wth MLC-PCA and Table 6 ndcates comparng accuracy percentage as classfer for tranng and test by BPNN-PCA and MLC-PCA. Whch see that BPNN- PCA s better than MLC-PCA n term of accuracy percentage. To vsually show results, the satellte mage of 52 52 pxels s mplemented to classfy as the mentoned classes. Fg 4 and 5 show the classfed mages by MLC- PCA and BPNN-PCA, respectvely. whch not nclude tme for calculatng PCA for tranng and classng everthough the accuracy of BPNN s more slght than that of BPNN-PCA but tme used for BPNN- PCA tranng s reduced of half. We compare the results of BPNN-PCA wth MLC-PCA seeng that our method s better n term of accuracy percentage of both tranng and test but not takng nto account of tme of MLC-PCA whch s less than BPNN-PCA. REFERENCES [] G.F. Byane, P.F. Crapper and K.K. Mayo, Montorng Land-Cover Change by Prncpal Component Analyss of Multtemporal Landsat Data, Remote Sensng of Envronment, Vol.0, No.3, pp. 75-84, 980. [2] S.K. Jenson and Frederck A. Waltz, Prncpal component analyss a canoncal analyss n remote sensng, Proc. Am. Soc. of Phoogrammetry, Fall Church, pp. 337-348, 979. [3] C.C. Chong, and J.C. JIA, Classfcaton of multspectral mages usng BP-neural network classfer nput codng assessments Proc. IEEE TENCON 94, vol.2, pp. 867-87, 994. [4] H. Bschof, W. Schneder, and A. J. Pnz, Multspectral classfcaton of landsat-mages usng neural networks IEEE Transcatons on Geoscence and Remote Sensng, vol.30, pp. 482-490, May 992. [5] J.A. Benedktsson, P.H. Swan and O.K. Ersoy, Neural network approaches versus statstcal methods n classfcaton of multsource remote sensng data, IEEE Trans. on Geoscence and Remote Sensng, Vol.28, No.4, pp. 540-552, July 990. [6] J.D. Paola and R.A. Schowengerdt, A Detal Comparson of Backpropagaton Neural Network and Maxmum- Lkelhood Classfers for Urban Land Use Classfcaton, IEEE Trans. on Geoscence and Remote Sensng, Vol.33, No.4, pp. 98-996, July 995. [7] P. D. Heermann and N. Khazene, Classfcaton of Multspectral Remote Sensng Data Usng a Back- Propagaton Neural Network, IEEE Trans. on Geoscence and Remote Sensng, Vol.30, No., pp. 8-88, Jan. 992. [8] W. Zhou Verfcaton of the Nonparametrc Characterstcs of Backpropagaton Neural Networks for Image Classfcaton, IEEE Trans. on Geoscence and Remote Sensng, Vol.37, No.2, pp. 77-779, March 999. 5. CONCLUSION In ths paper, we propose the one way to mplement BPNN- PCA nstead of BPNN so as to reduce complexty and tme
ID:489-5 Fg. The RGB mage from Band 4, 5 and 7. Prncpal component Prncpal component 2 Prncpal component 3 Fg. 2 The frst three prncpal component mages, PC, 2 and 3. tranng Test Classes Water Sol Forest Mountan Urban Buldng Fg. 3 The selected pxel areas of the PC mage used for tranng and test. Table The number of pxels n each class used for tranng and test. Number of Classes pxels Water Sol Forest Mountan Urban Buldng Total Tranng 360,99 964 576 696 439 4,234 Test 99,324,339,583 870 77 6,824 Total,35 2,523 2,303 2,59,566,56,058
ID:489-5 Table 2 The statstcal numercal values n each class of PC, 2 and 3 used for trangng and test. Components Classes Mn. Max. Mean Std. Water 5 3 0.43 4.7 Sol 52 0 7.62 7.79 # Forest 82 29 07.80 7.48 Mountan 72 08 90.48 4.96 Urban 2 27 42.6 26.92 Buldng 67 44 86.93 7.35 Water 87 99 96.05.76 Sol 02 43 26.37 6.78 #2 Forest 68 5 89.6 7.6 Mountan 85 47 6.0 0.72 Urban 50 255 86.86 8.26 Buldng 42 98 69.57 0.5 Water 40 60 49.73 3.9 Sol 3 39 29.40 2.84 #3 Forest 8 35 26.86 2.50 Mountan 2 54 3.84 3.6 Urban 4 66 3.66 9.5 Buldng 30 86 68.74 6.45 Table 3 Covarance matrx n each class of PC,2 and 3. Water Sol Forest # 2 3 # 2 3 # 2 3 22.508-0.5937 -.476 60.6636-26.87-9.729 56.8972-44.0337-3.3597 2-0.5937 3.03 0.9835 2-26.87 46.986-0.4575 2-44.0337 5.2235 -.388 3 -.476 0.9835 0.682 3-9.729-0.4575 8.0428 3-3.3597 -.388 6.2380 Mountan Urban Buldng # 2 3 # 2 3 # 2 3 24.6325-4.2949 3.003 724.7625 338.653 96.796 53.9653 4.323-3.4774 2-4.2949 6.087-6.3538 2 338.653 333.2662 3.4497 2 4.323 02.9669-23.2038 3 3.003-6.3538 3.0562 3 96.796 3.4497 90.3692 3-3.4774-23.2038 29.7052 # denote order of prncpal components Table 4 Results of classfcaton n the selected testng area by MLC-PCA. Classes Results Water Sol Forest Mountan Urban Buldng Total Water 99 0 0 0 0 0 99 Sol 0,305 2 0 0,308 Forest 0 0,93 5 0 0,208 Mountan 0 2 27,237 0 2,368 Urban 0 7 7 9 87 0 924 Buldng 0 0 30 0 75,026 Total 99,324,339,583 87 77 6,825 Accuracy (%) 00 98.56 89.0 78.4 00 99.72 92.48
ID:489-5 Table 5 Results of classfcaton n the selected testng area by BPNN-PCA. Classes Results Water Sol Forest Mountan Urban Buldng Total Water 99 2 0 0 0 0 993 Sol 0,322 6 7 0 0,335 Forest 0 0,305 40 0,346 Mountan 0 0 24,490 2 35,55 Urban 0 0 4 869 0 874 Buldng 0 0 0 45 0 68 726 Total 99,324,339,583 87 77 6,825 Accuracy (%) 00.00 99.85 97.46 94.3 99.77 94.98 97.55 Table 6 Comparng accuracy percentage for classfcaton by BPNN-PCA and MLC-PCA. Classfer Component No. Used Area Accuracy Maxmum lkelhood, 2 and 3 Tranng 95.9 % Test 92.48 % Neural network, 2 and 3 Tranng 96.62 % Test 97.55 % Water Sol Forest Mountan Urban Buldng Fg. 4 The classfed mage by MLC-PCA. Water Sol Forest Mountan Urban Buldng Fg. 5 The classfed mage by BPNN-PCA.