SVM Classification of Urban High-Resolution Imagery Using Composite Kernels and Contour Information

Transcription

1 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 SVM Classfcaton of Urban Hgh-Resoluton Iagery Usng Cooste Kernels and Contour Inforaton Assa Bear, Mostafa El yassa, Soufane Idbra, Drss Maass and Azeddne Elhassouny IRF SIC laboratory, Faculty of scences Agadr, Morocco Danelle Ducrot Cesbo laboratory, Toulouse, France Abstract The classfcaton of reote sensng ages has done great forward tang nto account the age s avalablty wth dfferent resolutons, as well as an abundance of very effcent classfcaton algorths. A nuber of wors have shown rosng results by the fuson of satal and sectral nforaton usng Suort Vector Machnes (SVM) whch are a grou of suervsed classfcaton algorths that have been recently used n the reote sensng feld, however the addton of contour nforaton to both sectral and satal nforaton stll less elored. For ths urose we roose a ethodology elotng the roertes of Mercer s ernels to construct a faly of cooste ernels that easly cobne ult-sectral features and Haralc teture features as data source. The cooste ernel that gves the best results wll be used to ntroduce contour nforaton n the classfcaton rocess. The roosed aroach was tested on coon scenes of urban agery. The three dfferent ernels tested allow a sgnfcant roveent of the classfcaton erforances and a fleblty to balance between the satal and sectral nforaton n the classfer. The eerental results ndcate a global accuracy value of 93.5%, the addton of contour nforaton, descrbed by the Fourer descrtors, Hough transfor and Zerne oents, allows ncreasng the obtaned global accuracy by 1.61% whch s very rosng. Keywords SVM; Contour nforaton; Cooste Kernels; Haralc feature; Satellte age; Sectral and satal nforaton; GLCM; Fourer descrtors; Hough transfor ; Zerne oents. I. INTRODUCTION The rch sectral nforaton avalable n reotely sensed ages allows the ossblty to dstngush between sectrally slar aterals [1]. However, suervsed classfcaton of satellte ages (whch assues ror nowledge n the for of class labels for soe sectral sgnatures) s a very challengng tas due to the generally unfavourable rato between the (large) nuber of sectral bands and the (lted) nuber of tranng sales avalable a ror, whch results n the Hughes henoenon. The alcaton of orgnally develoed ethods for the classfcaton of lower densonal data sets (such as ultsectral ages) generally rovdes oor results when aled to satellte ages, artcularly n the case of sall tranng sets []. The classfcaton of such ages s slar to that of other age tyes, t follows the sae rncle, and t s a ethod of analyss of data that as to searate the age nto several classes n order to gather the data n hoogeneous subsets, whch show coon characterstcs. It as to assgn to each el of the age a label whch reresents a thee n the real study area (e.g. vegetaton, water, bult, etc) [3]. Several classfcaton algorths have been develoed snce the frst satellte age was acqured n 197 [4-6]. Aong the ost oular and wdely used s the au lelhood classfer [7]. It s a araetrc aroach that assues the class sgnature n noral dstrbuton. Although ths assuton s generally vald, t s nvald for classes consstng of several subclasses or classes that have dfferent sectral features [8]. To overcoe ths roble, soe non-araetrc classfcaton technques such as artfcal neural networs, decson trees and Suort Vector Machnes (SVM) have been recently ntroduced. SVM s a grou of advanced achne learnng algorths that have seen ncreased use n land cover studes [9, 10]. One of the theoretcal advantages of the SVM over other algorths (decson trees and neural networs) s that t s desgned to search for an otal soluton to a classfcaton roble whereas decson trees and neural networs are desgned to fnd a soluton, whch ay or ay not be otal. Ths theoretcal advantage has been deonstrated n a nuber studes where SVM generally roduced ore accurate results than decson trees and neural networs [7, 11]. SVMs have been used recently to a urban areas at dfferent scales wth dfferent reotely sensed data. Hgh or edu satal resoluton ages (e.g., IKONOS, QUICKBIRD, LANDSAT (TM)/ (ETM+), SPOT) have been wdely eloyed on urban land use classfcaton for ndvdual ctes, buldng etracton, road etracton and other an-ade obects etracton [1, 13]. On the other hand, the consderaton of the satal asect n classfcaton reans very ortant. For ths case, Haralc has descrbed ethods for easurng teture n gray-scale ages, and statstcs for quantfyng those tetures. It s the hyothess of ths research that Haralc s Teture Features and 16 P a g e

2 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 statstcs as defned for gray-scale ages can be odfed to ncororate sectral nforaton, and that these Sectral Teture Features wll rovde useful nforaton about the age. It s shown that teture features can be used to classfy general classes of aterals, and that Sectral Teture Features n artcular rovde a clearer classfcaton of land cover tyes than urely sectral ethods alone. As well as the contour nforaton s concerned, survey aroaches were develoed for attern recognton. The three ost used ethods are the Fourer descrtors (FD) classcally used to shae recognton and telate atchng; the Hough transfor (HT) whch has becoe a standard tool n couter vson feld. It allows the detecton of lnes, crcles or ellses n a tradtonal way; t can also be etended to the descrton of ore cole obect cases. The thrd ethod s the Zerne Moents (ZM) used to etract nvarant shaes descrtors to soe general lnear transforatons for the ages classfcaton. Ths wor resents the way adoted n our eerents to ncororate contour nforaton nto classfcaton rocess. We have found that the use of ths contour nforaton wth both sectral and satal nforaton allows ncreasng the accuracy obtaned usng only sectral and satal nforaton. The roosed ethod conssts nto cobnng satal, sectral and contour nforaton to obtan a better classfcaton. So we have started wth the etracton of satal nforaton (Haralc teture features) [14], and the contour nforaton (Fourer descrtors, Hough transfor and Zerne oents). Then, we have used these descrtors cobned wth sectral values as entry of the SVM classfer. We have eloted the roertes of Mercer s ernels to construct a faly of cooste ernels that easly cobne satal and sectral nforaton. The three dfferent cooste ernels tested deonstrate enhanced classfcaton accuracy coared to aroaches that tae nto account only the sectral nforaton, and a fleblty to balance between the satal and sectral nforaton n the classfer. An etended verson of the cooste ernel that gves the best results wll be used to ntroduce contour nforaton n the classfcaton rocess. The result obtaned s coared wth the sae cooste ernel usng only sectral and satal nforaton to easure the contrbuton of contour nforaton n the classfcaton s overall accuraces. Ths aer s organzed as follows. In the second secton, we wll dscuss the etracton of sectral, satal and contour nforaton esecally the Grey-Level Co-occurrence Matr (GLCM), Haralc teture features, Hough transfor and Zerne oents used n eerentatons. In secton 3, we wll gve outlnes on the used classfer: Suort Vector Machnes (SVM). Secton 4 wll descrbe the three dfferent cooste ernels used n eerentatons. In secton 5, the eerentatons and results would be resented as well as the nuercal evaluaton. Fnally, conclusons and future research lnes would be rovded n secton 6. II. EXTRACTION OF INFORMATION A. Sectral Inforaton The ost used classfcaton ethods for the reotesensng data consder esecally the sectral denson. Frst attets to analyze urban area used estng ethodologes and technques develoed for land reote sensng, based on sgnal odelng. Each el-vector s regarded as a vector of attrbutes whch wll be drectly eloyed as an entry of the classfer. The tradtonal aroach for classfyng reote-sensng data ay be sued u as: fro the orgnal data set, a feature reducton/selecton ste s erfored accordng to the classes n consderaton, and then classfcaton s carred out usng these etracted features. In our wor, the ste of a feature reducton/selecton can be sed consderng that we have used ultsectral ages such as IKONOS, QUICKBIRD. Accordng to Fauvel [15] ths allows a good classfcaton based on the sectral sgnature of each area. However, ths does not tae n account the satal nforaton reresented by the varous structures n the age. B. Satal Inforaton Inforaton n a reote sensed age can be deduced based on ther tetures. A huan analyst s able to dstngush anade features fro natural features n an age based on the regularty of the data. Straght lnes and regular reettons of features hnt at an-ade obects. Ths satal nforaton s useful n dstngushng the dfferent feld n the reote sensed age. Many aroaches were develoed for teture analyss. Accordng to the rocessng algorths, three aor categores, naely, structural, sectral, and statstcal ethods are coon ways for teture analyss. Many researches have been conducted on the use of Gabor flter bans [16] and co-occurrence atrces [17] for the satal/sectral classfcaton of ultsectral data. Other researches have been conducted wth atheatcal orhology concets. Palason et al. [18] and Fauvel et al. [15] suggest an etracton ethod of orhologcal rofles. These rofles are couted on the frst rncal coonents of hyersectral ages. Plaza [19] uses also atheatcal orhology to etract the endebers of a hyersectral age. Soe other wors [0] cobne sectral classfcaton wth satal segentaton based on watershed ethod. In [1-3], the authors coare dfferent satal features n unsuervsed classfcaton of hyersectral ages; the studes used Gabor flter bans, co-occurrence atrces, Teture sectra and orhologcal rofles. The results obtaned showed that the haralc features etracted fro the cooccurrence atrces gve the best erforance n classfcaton accuraces. The GLCM ethod, roosed by Haralc [4, 5], nvolves two stes to generate satal features P a g e

3 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 Frst, the satal nforaton of a dgtal age s etracted by a co-occurrence atr calculated on a el neghbourhood (el wndow) defned by a ovng wndow of a gven sze. Such a atr contans frequences of any cobnaton of gray levels occurrng between el ars searated by a secfc dstance and angular relatonsh wthn the wndow. The second ste s to coute statstcs fro the gray level cooccurrence atr to descrbe the satal nforaton accordng to the relatve oston of the atr eleents. Even sall, a co-occurrence atr reresents a substantal aount of data that s not easy to handle. Ths s why Haralc uses these atrces to develo a nuber of satal ndces that are easer to nterret. Haralc assued that the teture nforaton s contaned n the co-occurrence atr, and teture features are calculated fro t. A large nuber of tetural features have been roosed startng wth the orgnal fourteen features descrbed by Haralc et al [5], however only soe of these features are n wde use. Wezsa et al [6] used four of Haralc features. Conners and Harlow [7] use fve features. Peng Gong and al. [8] show that these features are uch correlated wth each other. The authors have used the FORTRAN acage TEXTRAN for the satal feature etracton. The analyss was ade on the near-nfrared band ( /µ) wth a quantzaton level of 16. The nterel dstance was et constant to 1, and the four an orentatons were averaged. The wndow szes used were 33, 55, and 77 els. Prelnary tests ade wth larger wndow szes dd not gve satsfactory results. Ten teture features were frst generated on a 55 el wndow. The three less correlated features were then selected to colete the study. The Fg.1. Reresents the Correlaton Matr of the 16 Satal Features. Fg. 1. The Correlaton Matr of the 16 Satal Features. In ths wor, we have chosen the fve features used by Conners and Harlow, whch are soe of the ost coonly used satal easures and the three less correlated (Fg.1.); we have found that these fve suffced to gve good results n classfcaton [9]. These fve features are: hoogenety (E), contrast (C), correlaton (Cor), entroy (H) and local hoogenety (LH), and co-occurrence atrces are calculated for four drectons: 0, 45, 90 and 135 degrees. Let us recall ther defntons consderng a co-occurrence atr M: E 1 0 ( M (, )) C M (, ) Where s the denson of the co-occurrence atr M. 1 Cor ( )( ) M (, ) Where and are the horzontal ean and the varance, and are the vertcal statstcs. H M (, )log( M (, )) 1 ( M (, ) LH ) Each teture easure can create a new band that can be ncororated wth sectral features for classfcaton uroses. C. Contour Inforaton Fourer descrtors are classcal ethods to shae recognton and they have grown nto a general ethod to 18 P a g e

4 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 encode varous shae sgnatures. Prevous eerents have used Fourer descrtors to sooth out fne detals of a shae. Also, usng the orton of Fourer descrtors to reconstruct an age that sooths out the shar edges and fne detals found n the orgnal shae. Flterng an age wth Fourer descrtors rovdes a sle technque of contour soothng. Fourer descrton of an edge s also used for telate atchng. Snce all the Fourer descrtors ecet the frst one do not deend on the locaton of the edge wthn the lane, ths rovdes a convenent ethod of classfyng obects usng telate atchng of an obect s contour. A set of Fourer descrtors s couted for a nown obect. Ignorng the frst coonent of the descrtors, the other Fourer descrtors are coared aganst the Fourer descrtors of unnown obects. The nown obect, whose Fourer descrtors are the ost slar to the unnown obect s Fourer descrtors, s the obect the unnown obect s classfed to. They can also be used for calculaton of regon area, locaton of centrod, and coutaton of second-order oents. On the other hand, n the detecton of secfc eleents, There are algorths that, so as to dentfy these basc fors, attet to follow the contours to fnally bnd crtera ore or less cole to trace the desred shae. Another aroach to ths roble s to try to accuulate evdences on a artcular for estence, such as a lne, a crcle or an ellse. It s ths aroach that has been adoted n the Hough transfor. In recent decades, t has becoe a standard tool n couter vson feld. It allows the detecton of lnes, crcles or ellses n a tradtonal way. It can also be etended to descrton of ore cole obects cases. Moreover, the ethods of ages reresentaton by oents are aong the frst to have been aled n attern recognton. The an otvaton s to etract nvarant shaes descrtors to soe general lnear transforatons for the ages classfcaton. Snce the ntal wor of H. Mng-Kuel [30] n 196 on nvarants derved fro the age geoetrc oents, several aroaches have been roosed. Most of these defned oents are eressed as radal oents of the age s crcular haronc functons. The age s Zerne Moents (MZ) were ntroduced by M.R. Teague [31]. He roosed to use cole olynoals of Zerne orthogonal wthn the unt crcle. These ethods are dstngushed by the used radal ernel for, whch s ore or less arorate to the etracton of nvarant descrtors to flat slartes. In the followng we wll ntroduce brefly the Fourer descrtors, the Hough transfor and Zerne oents used n eerent to descrbe the contour nforaton. 1) Fourer Descrtors The Fourer Descrtors (FD) have been frequently used as features for age rocessng, reote sensng, shae recognton and classfcaton. The use of FDs for attern recognton tass started n the early stes by Cosgrff [3] and Frtzsche [33]. A set of orthogonal FDs reresent each attern for the urose of classfcaton. The recognton syste was ndeendent of the character sze and orentaton. Furtherore, FDs were used as features for recognton systes for both handwrtten characters [34] and nuerals [35]. Granlund [34] used a sall nuber of lower-order descrtors for the classfcaton syste. Those descrtors were nsenstve to translaton, rotaton and dlaton. Because of the sall coutatonal ower avalable at that te the syste could not be eaned to gve the sutable nuber of descrtors. The classfcaton syste was aled to a sall nuber of characters. Nevertheless the syste was able to roduce a very good recognton rate of 98%. Zhan and Roses [35] started coutng the FDs by translatng the contour of handwrtten nueral nto a change of angle curve. A large nuber of Fourer coeffcents are roduced. For each coeffcent two nds of FDs are couted, the haronc altude and the hase angle. Those ar of FDs s nvarant under translaton, rotaton and change of sze of the orgnal handwrtten nueral. All the FDs ars fully descrbe the orgnal sgnature. Fourer descrtors were also used to descrbe oen curves n an onlne character recognton syste [36]. The one el thc stroes were taen onlne usng a tablet. Then twenty FDs were couted and used for classfcaton. In reote sensng feld the FDs were aled to the feature of the regons on the data for geoetrcal atchng of the reote sensng ages. It aes ossble to ontor natural and artfcal changes n land cover recsely. The dscreet Fourer functon for a erodc olynoal functon f(t) s, 1 N 1 F() f(t) e ( πt / N), (6) N t0 Where N s the total nuber of onts along the f(t) 1 N 1 F() f(t) {cos(t N) sn(t N)} N t0 (7) wth 0,1,,..., N 1 The Fourer coeffcents are N 1 a f ( t) cos N t1 b N 1 f ( t)sn N t1 t N t As sad before the coonly used FDs are the haronc altude A and the hase angle of the Fourer coeffcents a and b above, A a b b (9) 1 tan a The haronc altude A s a ure shae feature and doesn t contan nforaton about the oston or the orentaton of the nueral but on the other hand the hase angle has those two features. The fed length feature vector would be N (8) 19 P a g e

5 A M, 1, (10) Where M s a fed nteger nuber. The orgnal olynoal could be reconstructed fro ts FDs by usng the followng equaton N 1 ( ) t t f t A o a cos b sn 1 N N wth t 0,1,,... N 1 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 (11) Where, A o s the DC coonent of the functon, and has no effect on the shae descrton. ) Hough Transfor The Hough Transfor (HT) s consdered as a very owerful tool for detectng redefned features (.e. lnes, ellses ) n ages and has been used for ore than three decades n the areas of age rocessng, attern recognton and couter vson. Its an advantages are ts nsenstvty to nose and ts caablty to etract lnes even n areas wth el absence (el gas) [37-39]. The Hough technque s artcularly useful for coutng a global descrton of a feature(s) (where the nuber of soluton classes need not to be nown a ror), gven (ossbly nosy) local easureents. The otvatng dea behnd the Hough technque for lne detecton s that each nut easureent (e.g. coordnate ont) ndcates ts contrbuton to a globally consstent soluton (e.g. the hyscal lne whch gave rse to that age ont). As a sle eale, consder the coon roble of fttng a set of lne segents to a set of dscrete age onts (e.g. el locatons outut fro an edge detector). Fg.. shows soe ossble solutons to ths roble. Here the lac of a ror nowledge about the nuber of desred lne segents (and the abguty about what consttutes a lne segent) render ths roble under-constraned. (a) (b) (c) Fg.. (a) Coordnate onts, when (b) and (c) Possble straght lne fttngs. We can analytcally descrbe a lne segent n a nuber of fors. However, a convenent equaton for descrbng a set of lnes uses araetrc or noral noton as follow: ρ = cos θ + y sn θ Where ρ s the length of a noral fro the orgn to ths lne and θ s the orentaton of ρ wth resect to the X-as. (Fg.3.) For any ont (,y) on ths lne, ρ and θ are constant. Fg. 3. Paraetrc descrton of a straght lne (ρ,θ ) In an age analyss contet, the coordnates of the ont(s) of edge segents (.e. (,y ) ) n the age are nown and therefore serve as constants n the araetrc lne equaton, whle ρ and θ are the unnown varables we see. We lot the ossble (ρ,θ ) values defned by each (,y ) onts n Cartesan age sace a to curves (.e. snusods) n the olar Hough araeter sace. Ths ont-to-curve transforaton s the Hough transforaton for straght lnes. When vewed n Hough araeter sace, onts whch are collnear n the cartesan age sace becoe readly aarent as they yeld curves whch ntersect at a coon (ρ,θ ) ont. The transfor s leented by quantzng the Hough araeter sace nto fnte ntervals or accuulator cells. As the algorth runs, each (,y ) s transfored nto a dscretzed (ρ,θ ) curve and the accuulator cells whch le along ths curve are ncreented. Resultng eas n the accuulator array reresent strong evdence that a corresondng straght lne ests n the age. 3) Zerne Moents The etracton of features fro an age by the ethod of oents s one of the technques coonly used. It obvously gves the aount of nforaton whch s encoded n the age [40]. A oent s an overall descrton of the dstrbuton of els wthn an age. Each te a gven order gves dfferent nforaton of other tes on the age [41, 4]. The central oents of order, q are gven by the followng eressons: q y q y y I, y Wth: q q X Y I y, (13) 0 0 X Y 1 0 and y Where I (, y) s the gray level of the el, y. The central oents are gven as followng [39, 40]: q y y I, y (14) q y The noralzed central oents are gven by P a g e

6 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 q q wth 1 (15) q 0 0 Hu oents are defned as a set of oent nvarants [43], but are not orthogonal. The ost nterestng oents are orthogonal that can be obtaned through the Zerne olynoals. The Zerne oents do not change the orentaton, the scale and the translaton. They rean robust to nose and to nor varatons of the fors [44]. There s no redundant nforaton because ther bases are orthogonal. An age s best descrbed by a sall set of Zerne oents than any other tye of oents such as geoetrc oents, Legendre, rotatonal or cole oents [45]. The Zerne oents are buld usng a set of cole olynoals whch for a colete orthogonal set on the unt ds. For an age f, the Zerne oents are defned as follows [45]: n Z n 1 f (, y) V * n (, ) y (16) Where and n defne the order of the oent. Knowng that V n R e, (17) n Where R ( n ) s the radal olynoal Zerne. The latter can be descrbed by: n / s ( n s)! (18) ns Rn( ) ( 1) r s0s n n s! s! s! Where n and are ntegers (ther values are even ntegers). These oents can be used as a tool for coarng two classes by calculatng the dstance denoted by d between the vectors of Zerne oents of each class. If we are nterested n coarng one class to ultle classes, the ost slar age corresonds to that whch s characterzed by a sallest dstance d. III. SVM CLASSIFICATION In ths secton we wll brefly descrbe the general atheatcal forulaton of SVMs ntroduced by Van [46, 47]. Startng fro the lnearly searable case n whch the otal hyerlanes are ntroduced. Then, the classfcaton roble s odfed to handle non-lnearly searable data. At the end of ths secton, a bref descrton of ultclass strateges would be gven. A. Lnear SVM For a two-class roble n a n-densonal sace R n, we assue that l tranng sales R n, are avalable wth ther corresondng labels y = ±1, S = {(, y) [1, l]}. The SVM ethod conssts of fndng the hyerlane that azes the argn,.e., the dstance to the closest tranng data onts for both classes [48]. Notng wr n as the noral vector of the hyerlane and b R as the bas, the hyerlane H s defned as: Where w, b 0, H w, s the nner roduct between w and. If H then f ( ) w, b s the dstance of to H. The sgn of f corresonds to decson functon y = sgn (f()). Fnally, the otal hyerlane has to aze the argn: w. Ths s equvalent to nze w and leads to the followng quadratc otzaton roble: w n subect to y ( w, b) 1 1, l For non-lnearly searable data, the otal araeters w, ) are found by solvng: ( b w l n C 1 subect to y ( w, b) 1, 0 1, l Where the constant C control the aount of enalty and are slac varables whch are ntroduced to deal wth sclassfed sales (Fg.4.). Ths otzaton tas can be solved through ts Lagrangan dual roble: Fnally: l l 1 a y y, 1, 1 subect to0 C 1, l l 1 y 0 w l 1 y The soluton vector s a lnear cobnaton of soe sales of the tranng set, whose s non-zero, called Suort Vectors. The hyerlane decson functon can thus be wrtten as: y Where u s an unseen sale. u l sgn y u, b P a g e

7 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 Wth the use of ernels, t s ossble to wor lctly n F whle all the coutatons are done n the nut sace. The classcal ernels used n reote sensng are the olynoal ernel and the Gaussan radal bass functon: (, ) ( ) 1 oly (, ) e[ ] gauss Fg. 4. Classfcaton of a non-lnearly searable case by SVMs. There s one non searable feature vector n each class. B. Non-Lnear SVM Usng the Kernel Method, we can generalze SVMs to nonlnear decson functons. By ths technque, the classfcaton caablty s roved. The dea s as followng. Va a nonlnear ang, data are aed onto a hgher densonal sace F (Fg.5.): : R n F ( ) The SVM algorth can now be sly consdered wth the followng tranng sales: ( S) ( ), y / [1, l]. It leads to a new verson of the hyerlane decson functon where the scalar roduct s now: ), ( ). Hoefully, for ( soe ernels functon, the etra coutatonal cost s reduced to: ), ( ) (, ) ( The ernel functon should fulfll Mercers condtons. C. Multclass SVMs SVMs are desgned to solve bnary robles where the class labels can only tae two values: ±1. For a reote sensng alcaton, several classes are usually of nterest. Varous aroaches have been roosed to address ths roble [49]. They usually cobne a set of bnary classfers. Two an aroaches were orgnally roosed for a -classes roble. One versus the Rest: bnary classfers are aled on each class aganst the others. Each sale s assgned to the class wth the au outut. Parwse Classfcaton: ( 1) bnary classfers are aled on each ar of classes. Each sale s assgned to the class gettng the hghest nuber of votes. A vote for a gven class s defned as a classfer assgnng the attern to that class. IV. COMPOSITE KERNELS In the followng secton, we wll be dealng wth three dfferent ernel aroaches that not only allow onng sectral and tetural nforaton for ultsectral age classfcaton, but also ntroducng the contour nforaton by usng an etended ernel verson [50, 51]. A. The Staced Features Aroach The ost coonly adoted aroach n ultsectral age classfcaton s to elot the sectral content of a el ( ). However, erforance can be roved by ncludng both sectral and satal nforaton n the classfer. Ths s usually done by eans of the staced aroach, n whch feature vectors are bult fro the concatenaton of sectral and satal features. Note that f the chosen ang s a transforaton of the concatenaton { -sect, -sa }, then the corresondng staced ernel atr s: Sect, Sa (, ) ( ), ( ) Fg. 5. Mang the Inut Sace nto a Hgh Densonal Feature Sace wth a ernel functon Whch does not nclude elct cross relatons between -sa and -sect. Includng the contour nforaton s also ossble by eans of the staced aroach; the feature vectors wll be bult fro the concatenaton of sectral, satal and contour features: { -sect, -sa, -cont }. The corresondng staced ernel atr Sect, SaCont, reans the sae n (9) P a g e

8 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 B. The Drect Suaton Kernel A sle cooste ernel cobnng sectral and tetural nforaton naturally coes fro the concatenaton of nonlnear transforatons of -sa and -sect. Let us assue two nonlnear transforatons 1. and. nto Hlbert saces H 1 and H, resectvely. Then, the followng transforaton can be constructed: ( ) 1 sect, sa And the corresondng dot roduct can be easly couted as follows: (, ) 1sect, sa, 1 sect, sa,, sect sect ( ), ( ) sect sa sa sa In the sae way, we can elot the Mercer s roertes to generalze ths forulaton n order to have a suaton of ultle ernels:, ), ( 1 So to use sectral, satal and contour nforaton we tae the case of =3, then we wll have: (, ) sect sect, sect sa sa, sa, Cont Cont Cont C. The Weghted Suaton Kernel By elotng roertes of Mercer s ernels, a cooste ernel that balances the satal and sectral content n (8) can also be created, as follows: (, ), (1 ), sect sect sect sa sa sa Where μ s a ostve real-valued free araeter (0 < μ < 1), whch s tuned n the tranng rocess and consttutes a tradeoff between the satal and sectral nforaton to classfy a gven el. Ths cooste ernel allows us to ntroduce a ror nowledge n the classfer by desgnng secfc μ rofles er class, and also allows us to etract soe nforaton fro the best tuned μ araeter. A generalzaton of the weghted suaton to ultle ernels s ossble by usng Lnear cobnaton ethods, and we can lnearly araeterze the cobnaton functon: (, ), 1 Where μ denotes the ernel weghts. Dfferent versons of ths aroach dffer n the way they ut restrctons on μ: the lnear su (. e., ), the conc su (. e., ), or the conve su (.., e and 1). As can be seen, the conc su s a secal case of the lnear su and the conve su s a secal case of the conc su. The conc and conve sus have two advantages over the lnear su n ters of nterretablty. Frst, when we have ostve ernel weghts, we can etract the relatve ortance of the cobned ernels by loong at the. Second, when we restrct the ernel weghts to be nonnegatve, ths corresonds to scalng the feature saces and usng the concatenaton of the as the cobned feature reresentaton: ( ) 1 11( ) ( ).. ( ) And the dot roduct n the cobned feature sace gves the cobned ernel: ( ) 1 1( ) ( ) ( ). ( ), ( )... ( ) ( ) 1, The cobnaton araeters can also be restrcted usng etra constrants, such as the l -nor on the ernel weghts or trace restrcton on the cobned ernel atr, n addton to ther doan defntons. For eale, the l 1 -nor rootes sarsty on the ernel level, whch can be nterreted as feature selecton when the ernels use dfferent feature subsets. So to use sectral, satal and contour nforaton we tae the case of =3, then we wll have: (, ) wth sa 1sect sect, sect,, sa and sa Cont Cont Cont D. The Coutatonal Colety The coutatonal colety of a ultle ernel learnng (MKL) algorth anly deends on ts tranng ethod (.e., whether t s one-ste or two-ste) and the coutatonal colety of ts base learner P a g e

9 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 One-ste ethods usng fed rules and heurstcs generally do not send uch te to fnd the cobnaton functon araeters, and the overall colety s deterned by the colety of the base learner to a large etent. One-ste ethods that use otzaton aroaches to learn cobnaton araeters have hgh coutatonal colety, due to the fact that they are generally odeled as a se-defnte rograng (SDP) roble, a quadratcally constraned quadratc rograng (QCQP) roble, or a second-order cone rograng (SOCP) roble. These robles are uch harder to solve than a quadratc rograng (QP) roble used n the case of the canoncal SVM. Two-ste ethods udate the cobnaton functon araeters and the base learner araeters n an alternatng anner. The cobnaton functon araeters are generally udated by solvng an otzaton roble or usng a closedfor udate rule. Udatng the base learner araeters usually requres tranng a ernel-based learner usng the cobned ernel. For eale, they can be odeled as a se-nfnte lnear rograng (SILP) roble, whch uses a generc lnear rograng (LP) solver and a canoncal SVM solver n the nner loo. Note that solvng the nzaton roble n all nds of cooste ernels requres the sae nuber of constrants as n the conventonal SVM algorth, and thus no addtonal coutatonal efforts are nduced n the resented aroaches. V. EXPERIMANTAL RESULTS In ths secton, we are gong to evaluate the roosed aroach by usng two hgh resoluton satellte ages wth dfferent resolutons reresentng the scene of urban areas. A. Data The frst age used n classfcaton s a subset of hgh resoluton QUICKBIRD satellte age, wth a hgh satal resoluton of.4 er el. It reresents urban scene areas. We dsose of four sectral bands: blue, green, red and near nfrared. We can see n Fg.7. (a) a reresentaton of ths subset. The second age s a subset of hgh resoluton IKONOS satellte age. It has also four sectral bands: red, blue, green and near nfrared, wth a hgh satal resoluton of 4.1 er el. Ths subset of the age s reresented n Fg.8. (a). We wll have two fles contanng the etracted features for each age, TranFle.dat and TestFle.dat resectvely for learnng and for classfcaton, and dvded on s classes as descrbed n the followng Table I. B. Coarng Cooste Kernels Our eerents are dvded on two stages (Fg.6. and Fg.9.). The frst one concerns the studes of cooste ernels roosed n secton 4 usng only sectral and satal nforaton. In the second stage we wll use an etended verson of the cooste ernel that gave the best erforance n the frst stage, to ntroduce contour nforaton n addton to sectral and satal nforaton. Class N TABLE I. Class nae DIFFERENTS CLASSES Tran sales Iage 1 Iage 1 Ashalt Green area Tree Sol Buldng Shadow Total So as we can see n Fg.6., that reresents the frst eerence, we have develoed a two ste classfcaton rocess: the frst one s the etracton of the satal and sectral features, so we coute Grey Level Co-occurrence Matr (GLCM) to etract Haralc teture features that we have added to sectral nforaton. The second ste s the SVM classfcaton; a suervsed ernel learnng algorth wdely used. We have selected SVMlght wth cooste ernels, whch s an leentaton of Suort Vector Machnes (SVMs) n C language [5]. Sectral nforaton Set of sectral values of each el Multsectral age SVM classfcaton Cooste ernel Classfcaton Ma Satal nforaton Haralc teture features Fg. 6. A reresentatve llustraton of the frst stage of the roosed worflow P a g e

10 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 To on satal and sectral nforaton, we have used three dfferent ernel aroaches as resented n secton 4; naed the staced features aroach n (9), the drect suaton ernel n (31) and the weghted suaton ernel n (34). In the case of the weghted suaton ernel, μ was vared wth a ste of 0.1 n the range [0, 1]. For slcty and for llustratve uroses, μ was the sae for all classes n our eerents. The enalzaton factor n the SVM was tuned n the range C = { }. We have used the Gaussan RBF ernel (8) (wth σ = { }) for the two ernels. uses a sectral sect nforaton whle uses Haralc features. sa The classfcaton a resented on (b) n Fg.7. and Fg.8., s obtaned when the classfcaton s erfored usng the staced features aroach (9). When the classfcaton s erfored usng the drect suaton ernel (31), we obtan the corresondng classfcaton a whch s resented on (c) n Fg.7. and Fg.8.. A vsual analyss of classfcaton as shows those areas ore hoogeneous for the as obtaned usng the drect suaton ernel than those obtaned by usng the staced features aroach. The fuson of the sectral and the satal features usng the weghted suaton ernel gve us the classfcaton a resented on (d) n Fg.7. and Fg.8.. We can see that the classes are ore connected and also we have got less sclassfed els n the result coared to the other aroaches. Table II lsts the accuracy estates and aa coeffcent of the classfcaton results, all odels are coared nuercally (overall accuracy, aa coeffcent). Table III and Table IV resents resectvely the confuson atr results for SVM classfcaton usng the weghted suaton ernel (34) based on sectral and satal nforaton, for both ages used n eerents. TABLE II. Methods SVM usng only sectral nforaton The staced features aroach The drect suaton ernel The weghted suaton ernel OVERALL ACCURACY (%) AND KAPPA COEFFICIENT OF CLASSIFIED IMAGES Overall accuracy Iage 1 Iage Kaa coeffcent Overall accuracy Kaa coeffcent 87.56% % % % % % % % 0.9 TABLE III. CONFUSION MATRIX RESULTS (%) FOR SVM CLASSIFICATION USING THE WEIGHTED SUMMATION KERNEL FOR IMAGE 1. GLOBAL ACCURACY = 94.48% Class nae Ashalt Green area Tree Sol Buldng Shadow Ashalt 93,66 1,41 1,91 1,01 1,63 0,38 Green area 1,13 94,99 0,00 1,08 1,54 1,6 Tree 0,8 1,07 9,8,50 0,8,51 Sol 4,84 0,95 0,00 93,87 0,34 0,00 Buldng 0,01 1,16,69 0,47 95,67 0,00 Shadow 0,08 0,4,58 1,07 0,00 95,85 (a) (b) (c) (d) Fg. 7. (a) Orgnal age 1, (b) Classfcaton Ma obtaned usng the staced features aroach, (c) Classfcaton Ma obtaned usng the drect suaton ernel, (d) Classfcaton Ma obtaned usng the weghted suaton ernel P a g e

11 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 TABLE IV. CONFUSION MATRIX RESULTS (%) FOR SVM CLASSIFICATION USING THE WEIGHTED SUMMATION KERNEL FOR IMAGE. GLOBAL ACCURACY = 9.55% Class nae Ashalt Green area Tree Sol Buldng Shadow Ashalt 89,36,04 1,9 1,50 3,3 1,86 Green area 5,13 9,1 0,00 1,03 1,54 0,09 Tree 1,18 1,5 93,15 1,9 0,03,0 Sol 1,75 1,13 0,64 93,04 3,44 0,00 Buldng 1,96,78,7 0,87 91,67 0,00 Shadow 0,6 0,3 1,57 1,64 0,00 95,85 (a) (b) (c) (d) Fg. 8. (a) Orgnal age, (b) Classfcaton Ma obtaned usng the staced features aroach, (c) Classfcaton Ma obtaned usng the drect suaton ernel, (d) Classfcaton Ma obtaned usng the weghted suaton ernel. Multsectral age Relable contour a Sectral nforaton Set of sectral values of each el Satal nforaton Haralc teture features Fourer Descrtors Hough transfor Contour features Zerne Moents SVM classfcaton Cooste ernel Classfcaton Ma Fg. 9. A reresentatve llustraton of the second stage of the roosed worflow C. Introducng Contour Inforaton In the second stage (reresented by Fg.9.) we have started, le the frst stage, wth the etracton of the sectral and satal features, so we have couted Grey Level Cooccurrence Matr (GLCM) to etract Haralc teture features that we have added to sectral nforaton. But, before the P a g e

12 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 SVM classfcaton, we have an addtonal ste that conssts on buldng a relable contour a fro whch we have etracted contour descrtors secally Hough transfor and Zerne oents, whle Fourer descrtors are etracted drectly fro the orgnal age. 1) Edge Detector Choce Generally the edge detectors can be groued nto three aor categores: the frst one s the Early vson edge detectors (Gradent oerators, e.g. the detectors of Sobel and Krsch). The second category s Otal detectors (e.g. the Canny algorth, etc.). The thrd category s the Oerators usng araetrc fttng odels (e.g. the detectors of Haralc, Nalwa-Bnford, Nayar, Meer and Georgescu, etc) [53]. The edge detecton rocess s greatly eased f, nstead the orgnal ages, «edge enhanced» ones are used. Ths nevtably leads to the use of soe edge detectors fro the second category. In the resent wor, we have chosen to use Canny edge detector. John Canny has treated edge detecton as sgnal rocessng roble and aed to desgn the «otal» edge detector. He forally has secfed an obectve functon to be otzed and used ths to desgn the oerator. The obectve functon was desgned to acheve the followng otzaton constrans [54]: Maze the sgnal to nose rato n order to rovde good detecton. Acheve good localzaton to accurately ar edges. Mnze the nuber of resonses to a sngle edge (non-edges are not ared). ) Buldng a Relable Contour Ma The Canny ethod fnds edges by loong for local aa of the gradent of the age. The gradent s calculated usng the dervatve of a Gaussan flter. The ethod uses two thresholds, to detect strong and wea edges, and ncludes the wea edges n the outut only f they are connected to strong edges. Ths ethod s therefore less lely than the others to be fooled by nose, and ore lely to detect true wea edges. For slcty and for llustratve uroses, we have used edge functon n Matlab to etract contour a wth the Canny ethod, and we have secfed a scalar for thresh, ths scalar value s used for the hgh threshold and 0.4*thresh s used for the low threshold. Ths scalar was vared wth a ste of 0.1 n the range [0, 1]. The Fg.10. Reresents two values of threshold used for the frst age. For the choce of thresholds of the age contours that gves us a relable contour a whch wll be used later n the classfcaton rocess, we have adoted two easures roosed by Wedeann [55], whch are used for the evaluaton of etracton ethods roads fro satellte ages, these two easures are defned as follows: Coleteness = length of the reference contour n accordance wth the etracted contour / length of the reference contour Eactness = length of the etracted contour n accordance wth the reference contour / length of the etracted contour. Hgh threshold=0. Fg. 10. eele of contour a for age 1 Hgh threshold=0.8 The rncle s to coare the contours of each threshold wth the reference contours whch are the contours of the SVM classfcaton usng the sectral and satal nforaton (Fg.11.). SVM Contour a fro the classfcaton a Fg. 11. Selectng relable contour a Multsectral age Calculatng: Coleteness and Eactness Relable contour a Contour etracton usng Canny edge detector Contour a corresondng to threshold The coarson s ade through the calculaton of these easures. The constrant s that the selected threshold a s the one n whch the etracted contours are the closest to the classfcaton reference contours. The assessent ethod leented n our study has a tolerance of a wdth of three els along the edges. The Fg.1. Reresents a threshold evaluaton for both ages. The choce of thresholds of the age contours that gves us a relable contour a that we have taen the one wth a good both Coleteness and Eactness, so we have chosen threshold 0.3 for age 1 and 0.4 for age as we can see n Fg P a g e

13 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 A vsual analyss of classfcaton as shows that t s less nosy and the classfcaton erforances are ncreased globally as well as alost all the classes. It atches well wth an urban land cover a n ters of soothness of the classes; and t also reresents ore connected classes. Table V lsts the accuracy estates and aa coeffcent of the classfcaton results, we can fnd dfferent cobnaton of descrtors used to characterze the contour nforaton all odels are coared nuercally (overall accuracy, aa coeffcent). Table VI and Table VII resent resectvely the confuson atr results for SVM classfcaton usng the etended weghted suaton ernel (38) based on sectral, satal and contour nforaton for both ages used n eerents. TABLE V. OVERALL ACCURACY (%) AND KAPPA COEFFICIENT OF CLASSIFIED IMAGES USING THE EXTENDED WEIGHTED SUMMATION KERNEL Fg. 1. threshold evalaton for the two ages 3) Results To cobne sectral, satal and contour nforaton, we have used the etended weghted suaton ernel n (38) that gave the best erforance at the frst stage of our eerents. Where the are vared n the range [0, 1] to satsfy the 3 condton 1. For slcty and for llustratve uroses, 1 all were the sae for all classes n our eerents. The enalzaton factor n the SVM was tuned n the range C = { }. In ths wor, we have couted the artcaton of contour nforaton n functon of sectral and satal nforaton: ( ) and we have vared 1 and wth a ste of n the range [0, 1] to satsfy the condton 1. 1 We have used the Gaussan RBF ernel (8) (wth σ = { }) for all ernels. uses a sectral nforaton, sect uses Haralc sa features whle uses Fourer descrtors, Hough transfor cont and Zerne oents. The age (c) n Fg.13. and Fg.14. reresent the relable contour a used to coute contour descrtors (Hough transfor and Zerne oents); whle (d) n Fg.13. and Fg.14. reresent the classfcaton a resultng by ntroducng contour (Fourer descrtors, Hough transfor and Zerne oents) nforaton wth both sectral and satal nforaton. Used Descrtors Sectral + haralc features Sectral + haralc features + FD Sectral + haralc features + ZM Sectral + haralc features + HT Sectral + haralc features + FD +HT Sectral + haralc features + FD + ZM Sectral + haralc features + HT + ZM Sectral + haralc features + FD + HT + ZM Overall accuracy Iage 1 Iage Kaa coeffcent Overall accuracy Kaa coeffcent 94.48% % % % % % % % % % % % % % % % 0.93 The cooste ernels offer ecellent erforance for the classfcaton of ultsectral satellte ages by sultaneously elotng both the satal and sectral nforaton. The weghted suaton ernel allows a sgnfcant roveent of the classfcaton erforances when coared wth the two other aroaches. So the etended weghted suaton ernel has been selected to ntroduce contour nforaton. The eerental results ndcate a global accuracy value of 93.5%, the addton of contour nforaton, descrbed by the Fourer descrtors, Hough transfor and Zerne oents, allows ncreasng the obtaned global accuracy by 1.61% (usng all descrtors) whch s very rosng. Although the Hough transfor don't gve a rearable ncreasng of the overall accuracy, t reserves the edges n the obtaned classfcaton a P a g e

14 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 TABLE VI. CONFUSION MATRIX RESULTS (%) FOR SVM CLASSIFICATION USING THE EXTENCED WEIGHTED SUMMATION KERNEL WITH ALL DESCRIPTORS FOR IMAGE 1. GLOBAL ACCURACY = % Class nae Ashalt Green area Tree Sol Buldng Shadow Ashalt 96,5 0,34 1,9 0,00 0,6 0,60 Green area 1,03 96,78 0,00 0,03 0,87 1,9 Tree 0,18 1,36 95,4 0,38 0,00,66 Sol 0,00 0,34 0,13 96,94,49 0,10 Buldng 1,94 1,16 0,81 0,08 96,01 0,00 Shadow 0,33 0,0 1,7,57 0,01 95,35 TABLE VII. CONFUSION MATRIX RESULTS (%) FOR SVM CLASSIFICATION USING THE EXTENDED WEIGHTED SUMMATION KERNEL WITH ALL DESCRIPTORS FOR IMAGE. GLOBAL ACCURACY = 94.08% Class nae Ashalt Green area Tree Sol Buldng Shadow Ashalt 93,3 1,00 3,1 0,00 0,64 1,9 Green area 1,04 95,18 0,00 1,08 1,44 1,6 Tree 0,8 1,08 93,91 1,40 0,8,51 Sol 3,33 1,6 0,00 93,07,34 0,00 Buldng 1,41 1,06 0,41,36 94,76 0,00 Shadow 0,71 0,4,47,09 0,00 94,31 (a) (b) (c) (d) Fg. 13. (a) Orgnal age 1, (b) Classfcaton Ma obtaned usng the weghted suaton ernel, (c) A relable contour a and (d) Classfcaton Ma obtaned usng the etended weghted suaton ernel (a) (b) (c) (d) Fg. 14. (a) Orgnal age, (b) Classfcaton Ma obtaned usng the weghted suaton ernel, (c) the relable contour a and (d) Classfcaton Ma obtaned usng the etended weghted suaton ernel P a g e

15 (IJACSA) Internatonal Journal of Advanced Couter Scence and Alcatons, Vol. 4, No.7, 013 VI. CONCLUSION AND FUTURE RESEARCH LINES Addressng the classfcaton of hgh resoluton satellte ages fro urban areas, we have resented three dfferent ernel aroaches tang sultaneously the sectral and the satal nforaton nto account (the sectral values and the Haralc features). The weghted suaton ernel allows a sgnfcant roveent of the classfcaton erforances when coared wth the two other aroaches. So an etended verson of ths ernel has been selected to ntroduce contour nforaton (Fourer descrtors, Hough transfor and Zerne oents). Ths aroach ehbts fleblty to balance between the sectral, satal and contour nforaton as well as coutatonal effcency. The roosed ethod s coutatonally eensve n coarson wth a sngle ernel-based aroach. In order to address ths ssue, we are lannng on elorng the act of reducng the orgnal data set densonalty before alyng the roosed aroach. 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