Support Vector Machine for Urban Land-use Classification using Lidar Point Clouds and Aerial Imagery

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1 Support Vector Machne for Urban Land-use Classfcaton usng Ldar Pont Clouds and Aeral Imagery Hayan Guan* a, Jonathan L a, Mchael A. Chapman b, Lang Zhong c, Que Ren a a Dept. Geography and Envronmental Management, Unversty of Waterloo, 200 Unversty Ave. West, Waterloo, ON, Canada N2l 3G1 b Dept. Cvl Engneerng, Ryerson Unversty, 350 Vctora Street, oronto, ON, Canada M5B 2K3 c School of Remote Sensng and Informaton Engneerng, Wuhan Unversty, 129 Luoyu Road, Wuhan, Hube, Chna ABSRAC Support Vector Machne (SVM), as a powerful statstcal learnng method,, has been found that ts performance on landuse -classfcaton outperform conventonal classfers usng multple features extracted from ldar data and magery. herefore, n ths paper, we use SVM for urban land-use classfcaton. Frst, we extract features from ldar data, ncludng mult-return, heght texture, ntensty; other spectral features can be obtaned from magery, such as red, blue and green bands. Fnally, SVM s used to automatcally classfy buldngs, trees, roads and ground from aeral mages and ldar pont clouds. o meet the objectves, the classfed data are compared aganst reference data that were generated manually and the overall accuracy s calculated. We evaluated the performance of SVMs by comparng wth classfcaton results usng only ldar data, whch shows that the land use classfcaton accuracy was mproved consderably by fusng ldar data wth multspectral mages. Meanwhle, comparatve experments show that the SVM s better than Maxmum Lkelhood Classfer n urban land-use classfcaton. Keywords: SVM, Land use, Classfcaton, Features, Ldar, Imagery 1. INRODUCION Land-use classfcaton has always been an actve research topc n remote sensng communty. oday, most arborne lght detecton and rangng (ldar) systems can collect pont cloud data and mage data smultaneously. Hgher land-use classfcaton accuracy of complex urban areas becomes achevable when both types of data are used. An arborne ldar system can drectly collect a dgtal surface model (DSM) of an urban area. Unlke a dgtal terran model (DM), the DSM s a geometrc descrpton of both terran surface and objects located on and above ths surface lke buldngs and trees. Ldar-derved dense DSMs have been shown to be useful n buldng detecton, whch s a classfcaton task that separates buldngs from other objects such as natural and man-made surfaces (lawn, roads) and vegetaton (trees). radtonal aeral magery can provde an abundant amount of structure, ntensty, colors, and texture nformaton. However, t s dffcult to recognze objects from aeral magery due to mage nterpretaton complexty. hus, the complementary nformatonal content of ldar pont clouds and aeral magery contrbute to urban object classfcaton. he development of ldar system, especally ncorporated wth hgh-resoluton camera component, and lmtatons of ldar data urged researchers to fuse magery nto ldar data for land-use classfcaton (Haala et al., 1998; Zeng et al., 2002; Rottenstener et al., 2003; Collns et al., 2004; Hu and ao, 2005; Walter, 2005; Rottenstener et al., 2005; Brattberg and olt, 2008; Chehata et al., 2009; Awrangjeb et al., 2010). Besdes multspectral magery, (Haala and Brenner, 1999; Bartels and We, 2006) used color nfrared (CIR) magery to perform a pxel-based land-use classfcaton. * h6guan@uwaterloo.ca, phone

2 Haala and Walter (1999) ntegrated the heght nformaton an addtonal channel, together wth the spectral channels nto a pxel-based classfcaton scheme. Charanya et al. (2004) descrbed a supervsed classfcaton technque that classfy ldar data nto four classes-road, meadow, buldng and tree-by combnng heght texture, mult-return nformaton and spectral feature of aeral mages. Brennan and Webster (2006) presented a rule-based object-orented classfcaton approach to classfyng surfaces derved from DSM, ntensty, multple returns, and normalzed heght (ede et al. 2008). Germane and Hung (2010) proposed two-step classfcaton methodology to delneate mpervous surface n an urban area usng ldar data to refne a base classfcaton result of multspectral magery based on ISODAA. her experment showed that the use of ldar data can mprove the overall accuracy by 3%. herefore, the mplementaton of ldar sgnfcantly enhances the classfcaton of optcal magery both n terms of accuracy as well as automaton. Rottenstener et al. (2007) demonstrated that the classfcaton accuracy of a small resdental area can be mproved by 20% when fusng arborne ldar pont cloud wth multspectral magery. Huang et al. (2008) showed that the performance of urban classfcaton by ntegratng ldar data and magery are better than other classfcaton methods only usng sngle data source. he Support Vector Machne (SVM), based on statstcal learnng theory, has been found that ts performance on landuse classfcaton outperform tradtonal or conventonal classfer (Yang, 2011), and has become a frst choce algorthm for many remote sense users. Beng a non-parametrc classfer SVM s partcularly sutable for classfyng remotely sensed data of hgh dmensonalty and from multple sources (Lodha, 2006; Waske and Benedktsson, 2007; Malpca, 2010). Classfcaton procedures based on the SVM have been appled to multspectral, hyperspectral data, synthetc aperture radar (SAR) data, and ldar data. herefore, n the project, the SVM classfer s suted to classfyng objects by usng multple features extracted from ldar data and magery. hs paper s organzed as follows. In Secton 2, we descrbe the basc prncples of SVM for classfcaton, the ldar data and calbrated magery used n the paper, features selected from the ldar data and magery, respectvely. Secton 3 then dscusses the SVM classfcaton results usng only ldar data comparng wth ntegraton of ldar data and mage data, and compares results of SVM by Maxmum Lkelhood Classfer (MLC). Fnally Secton 4 concludes the proposed method. 2.1 Prncples of SVM 2. MEHODOLOGY SVM, ntroduced n 1992 (Boser et al, 1992), have recently been used n numerous applcatons n the feld of remote sensng. Mountraks (2011) ndcated that SVMs are partcularly appealng n the remote sensng feld due to ther ablty to generalze well even wth lmted tranng samples, a common lmtaton for remote sensng applcatons. SVMs are typcally a supervsed classfer, whch requres tranng samples. Lterature shows that SVMs are not relatvely senstve to tranng sample sze and have been mproved to successfully perform wth lmted quantty and qualty of tranng samples. Dalponte et al. (2008) pont out that SVMs outperformed Gaussan maxmum lkelhood classfcaton and k- NN technque (Angelo et al, 2010), and that the ncorporaton of ldar varables generally mproved the classfcaton performance. In ths secton we wll brefly descrbe the basc SVMs concepts for classfcaton problems. he man advantage of SVMs s gven by the fact that t can fnd an optmal hyper-plane learnt from the spatal dstrbuton of tranng data n the feature space. SVM ams to dscrmnate two classes by fttng an optmal separatng hyper-plane to the tranng data wthn a mult-dmensonal feature space Z, by usng only the closest tranng samples (Melgan, 2004 )hus, the approach only consders samples close to the class boundary and works well wth small tranng sets, even when hgh dmensonal data sets are classfed.

3 Z1 ClassA w x b 1 w x b 0 w x b 1 Hyper-plane H1 H H2 m 2/ w ClassB Z2 Fg. 1 an optmum separatng hyper-plane Fgure 1 demonstrates the basc concepts of the SVM classfcaton, n whch m s the dstance between H1 and H2, and H s the optmum separatng hyper-plane whch s defned as: wx b 0 (1) where x s a pont on the hyper-plane, w s an n-dmensonal vector perpendcular to the hyper-plane, and b s the dstance of the closet pont on the hyper-plane to the orgn. It can be shown that: Equatons (2) and (3) can be combned nto: wx b 1, classa (2) wx b 1, classb (3) 1 y wx b 0 (4) he SVM attempts to fnd a hyper-plane, Equaton (1), wth mnmum wwthat s subject to constrant (4). he classfcaton processng to fnd the optmum hyper-plane s equvalent to solvng quadratc programmng problems: mn 1 w w C 2 y w ( x ) b 1 St. 0, 1,2,, l where C s the penalty parameter whch controls the edge balance of the error usng the technque of Lanrange multplers, the optmzaton problem becomes: l 1 (5) 1 mn y y K( x y ) 2 l l l j j j 1 j1 1 l y 0 St. 1 0 C, 1,2,, l (6) where K( x y ) ( x ), ( y ) j j s kernel functon, the functons used to project the data from nput space nto feature space. he kernel functon mplctly defnes the structure of the hgh dmensonal feature space where a maxmal margn hyper-plane wll be found. A feature space would cause the system to overft the data f t ncludes too many features, and conversely the system mght not be capable of separatng the data f the kernels are too poor. Four kernel functons are avalable namely: Gaussan radal bass functon (RBF), see Equaton (7), lnear, polynomal and sgmod. We chose the Gaussan RBF kernel for our SVM classfers, snce RBF kernels have yelded extremely hgh accuracy rates for the

4 most challengng hgh-dmensonal mage classfcatons, such as those nvolvng hyper-spectral magery or a combnaton of hyper-spectral magery and ldar data (Melgan, 2004). 1 2 ( xyj ) 2 K( x, y ) e (7) SVM by tself s a bnary. LULC applcatons usually needs to dvde the data set nto more than two classes. In order to solve for the bnary classfcaton problem that exsts wth the SVM and to handle the mult-class problems n remotely sensed data, two popular approaches are commonly used. One-Aganst-One s the method that calculates each possble par of classes of a bnary classfer. Each classfer s traned on a subset of tranng examples of the two nvolved classes. All N (N-1)/2 bnary classfcatons are combned to estmate the fnal output. When appled to a data set, each classfcaton gves one vote to the wnnng class and the pont s labelled wth the class havng most votes. hs approach s sutable for problem wth large amount of data. One-Aganst-All nvolves tranng a set of bnary classfers to ndvdually separate each class from the rest. Anthony et al. (2007) have reported that the resultng classfcaton accuracy from One-Aganst-All method s not sgnfcantly dfferent from One-Aganst-One approach and the choce of technque adopted s based on personal preference and the nature of the used data. In the paper, we use the One-Aganst- All technque snce the One-Aganst-One technque results n a larger number of bnary SVMs and need ntensve computatons, but also the One-Aganst-All method, for an N-class problem, constructs N SVM models, whch s traned to tell the samples of one class from samples of all remanng classes. 2.2 Data Descrpton Fgure 2 shows the study dataset, whch was collected over 1,000 m above ground level by Optech ALM 3100 system. he data sets covered a resdental area of 981 m 819 m n the Cty of oronto, Ontaro, Canada.he ldar dataset conssts of the frst- and last- returns of the laser beam. he true color mage data used were taken by an onboard 4k 4k dgtal camera smultaneously. Fgure 2 (a) shows a raster DSMs, contanng a total of 803,439 ponts, whch were nterpolated wth the frst and the last pulse return by the b-lnear nterpolaton method. he wdth and heght of the grd equals to the ground sample dstance (GSD) of the aeral mage (0.5 m). he elevaton of the study area ranges from m to m. Besdes buldngs, several clusters of trees located along the street; (b) demonstrates the ntensty mage of ldar data wth the same resoluton as the DSM; (c) shows a true color aeral mage that was re-sampled to 0.5 m ground pxel. he majorty of buldngs appeared n the color mage are wth gable roofs or hp roofs. j 2.3 Feature Selecton (a) (b) (c) Fg. 2 Data sets: (a) DSM of ldar data; (b) ntensty mage of ldar data; (c) aeral orthophoto We use the SVM algorthm to classfy the data set nto four classes (trees, buldngs, grass and roads). Snce the SVM requres a feature vector for each ldar pont to be classfed, there are sx features selected from ldar data and aeral magery for urban land-use classfcaton, ncludng mult-return nformaton, heght texture, ldar ntensty and three mage bands (RED, BLUE and GREEN). Feature selecton s very crucal as meanngful features facltate classfcaton accuracy of the data set. Feature 1: Ldar Mult-return Informaton (LHr): Heght nformaton between frst- and last- returns usually dfferentates tree features from ldar data. One of ldar system s characterstcs s the capablty of laser beam to

5 penetrate the trees canopy through a small openng. he number of returns counts on the object wthn the travel path of the laser pulse. Many commercal ldar systems can measure multple returns. Feature 2: Ldar heght texture (LHt): A range mage, dfferent from tradtonal optc mages, s based on the raw ldar pont clouds and created by nterpolaton. Every pxel n range mage represents a certan heght value. he brghter a pxel s, the hgher ts heght s. In other words, the value of a pxel s proportonal to ts heght value. (Mass, 1999) ponts out heght texture defned by local heght varatons s a sgnfcant feature of objects to be recognzed. By applyng mage processng algorthms the gradent magntude mage of the range mage can be calculated, contanng nformaton about heght varatons, whch s useful n dfferentaton of man-made objects and natural objects. Feature 3: Ldar ntensty (L): he ntensty s related to the reflectve propertes of the targets as well as the lght used, and dfferent materal has dfferent reflectance. Smlar to low-resoluton aeral mages, ldar ntensty nformaton can be used to extract planmetrc features and serve as ancllary nput for ldar data processng. Features 4 to 6: hree mage bands (R, B and G) from the aeral mage: he spectral nformaton of aeral mage correspond to the response of all objects on terran surface to vsble lght. hus, the resultng of feature vector for each ldar pont s gven by Fv LHr LHt L R G B values were normalzed n the range of [-1, 1].. All feature In ths study, about 17,039 canddate ponts were randomly selected as tranng data sets, and about 15% ponts, or the total of 105,298 were used for valdaton. he errascan model of errasold R was used to manually edt the tranng data sets. 3.1 Experments and Results 3. RESULS AND DISCUSSION wo parameters should be specfed whle usng the RBF kernels: C and the kernel functon γ. he problem s that there s no rule for the selecton of the kernel s parameters and t s not known beforehand whch C and γ are the best for the current problem (Ln and Ln, 2003). Both parameters C and γ depend on the data range and dstrbuton and they dffer from one classfcaton problem to another (Van der Lnden et al., 2009). In the paper, we select these parameters emprcally by tryng a fnte number of values and keepng those that provde the least test error. he results of optmzaton for C and γ are 300, and 0.4, respectvely. Fg. 3(a) shows the results of SVM usng ldar data and aeral mage data. Four classes-buldngs, grass, tress and roads are separated from each other very well. here are some classfcaton error occurred around some buldng boundares, whch are msclassfed as trees because of the selected mult-return feature. Although the laser beam can penetrate the trees canopy to the ground, the heght nformaton between frst- and last-return s unrelable to dstngush the tree from the ldar pont clouds. hs s because the laser beam httng on the edge of buldng also generates two returns. Secondly, f the densty of trees s hgh, the small-footprnt ldar cannot penetrate the tree s canopy. (a) (b) (c)

6 MLC SVMs Classfcaton Fg. 3 Classfcaton sults obtaned by (a) SVM usng ldar data and aeral mage, (b) SVM usng ldar data, and (c) MLC usng ldar data and aeral mage. 3.2 Quanttatve Assessment o evaluate the overall performance of our classfcaton method, we utlzed software errasold to produce the reference data for comparng wth the classfed results on a pxel-by-pxel bass. One of the most common methods of expressng classfcaton accuracy s the preparaton of a classfcaton error matrx (confuson matrx). An error matrx s an effectve way to assess accuracy n that t compares the relatonshp between known reference data and the correspondng results of the classfcaton (Congalton, 1991). It s a square matrx E of N N elements, where N s number of classes. he element E s the number of ponts known to belong to class and classfed as belongng to j class j. hus, the elements on the leadng dagonal E correspond to correctly classfed ponts, whereas the offdagonal elements correspond to erroneous classfcatons (.e., the commsson and omsson errors). From the confuson matrx, overall accuracy (OA) (Story and Congalton, 1986) can be calculated. able 1. Results of SVM classfcaton Reference data Class Buldngs Roads ree Grass Buldngs Roads ree Grass OA= ( )/ =84.23% able 1 and Fgure 3(a) show the classfcaton obtaned usng the SVM algorthm. hese results show that the proposed method produced 84.23% n overall classfcaton accuracy. Fgure 3(b) shows the SVM classfcaton results usng ldar data alone. Intensty nformaton of ldar data s utlzed to separate grass from roads. However, there are severe salt and pepper phenomena, and some low buldngs between hgh ones are msclassfed as roads because of a lack of spectral nformaton. And part of roads also msclassfed as class grass due to ther ntensty nformaton smlar to characterstcs reflected from grass. Fnally, the overall accuracy s 81.78%. herefore, ntegraton of ldar pont clouds and aeral mages can mprove the accuracy of classfcaton. 3.3 Comparson wth Maxmum Lkelhood Classfer In ths secton, we compare results of proposed method by MLC, the classfcaton results shown n the Fgure 3(c). he vsual nspecton shows that MLC s not as robust as SVM because there, besdes salt and pepper nose, are unexpectedly msclassfed ponts comparng wth the classfcaton results obtaned usng the SVM method. More buldng boundary ponts are mstakenly labelled as tree ponts; buldngs are confused wth roads. able 2 llustrates the error matrx of MLC. he overall accuracy of classfcaton s 77.41%. herefore, the comparson of the SVM method wth the MLC method demonstrates that use of the SVM method can mprove overall accuracy by about 7 % n the landuse classfcaton process. able 2. Results of MLC classfcaton Reference data Class Buldngs Roads ree Grass

7 Buldngs Roads ree Grass OA=77.41% 4. CONCLUSIONS In ths paper we have presented a classfcaton method based on SVM usng aeral magery along wth ldar pont clouds, whch classfed the dataset nto four classes: buldngs, trees or hgh vegetaton, grass and roads. he SVM classfcaton method could solve sparse samplng, non-lnear, hgh-dmensonal data, and global optmum problems. In our proposed methodology, we have used One-Aganst-All SVMs wth the RBF kernel because t can analyss hgher-dmensonal data and requres that only two parameters, C and γ. he fuson of mult-source data could obtan more accurate nformaton than a sngle data source s used. Spectral nformaton of aeral magery ntegrated wth ldar pont clouds results n a sgnfcantly hgher accuracy than usng ldar range data alone. Compared wth the MLC method, our results demonstrate that the SVM method can acheve hgher overall accuracy n urban land-use classfcaton. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] Angelo, J. J., B. W. Duncan, and J, F. Weshampel, Usng ldar-derved vegetaton profles to predct tme snce fre n an oak scrub landscape n east-central Florda, Remote Sensng, 2010, 2, ; Anthony, G., H. Gregg, and M. shldz, Image Classfcaton usng SVMs: One-aganst-One Vs One-aganst- All, In: Proc. 28th Asan Conference on Remote Sensng Kuala Lumpur, Malaysa,12-16 November Awrangjeb, M., M. Ravanbakhsh and C.S. Fraser, Automatc detecton of resdental buldngs usng ldar data and multspectral magery, ISPRS Journal of Photogrammetry and Remote Sensng, 65(5), (2010). Bartels, M., and H. We, Maxmum lkelhood classfcaton of ldar data ncorporatng multple co-regstered bands, Proceedngs of 4th Internatonal Workshop on Pattern Recognton n Remote Sensng/18th Internatonal Conference on Pattern Recognton, 20 August, Hong Kong, Chna, pp.17-20, (2006). Boser, B. E., I. M. Guyon, and V. N. Vapnk, A tranng algorthm for optmal margn classfers, Proceedngs of the 5th Annual ACM Workshop on Computatonal Learnng heory, (1992), pp ,(1992). Brattberg, O. and G. olt, erran classfcaton usng arborne ldar data and aeral magery, Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, 37 (B3b): , (2008). Brennan, R. and. L. Webster, Object-orented land cover classfcaton of ldar derved surfaces, Canadan Journal of Remote Sensng, 32 (2): , (2006). Charanya, A.P., R. Manduch, M. Roberto and S.K. Lodha, Supervsed parametrc classfcaton of aeral ldar data, Proceedngs of the IEEE Conference on Computer Vson and Pattern Recognton Workshop, 27 June - 2 July, Baltmore, MD. Vol. 3, pp , (2004). Chehata, N., L. Guo and M. Clement, Contrbuton of arborne full-waveform ldar and mage data for urban scene classfcaton, Proceedngs of the 16th IEEE Internatonal Conference on Image Processng, 7-11 November, Caro, Egypt, pp , (2009). Collns, C. A., R. C. Parker, and D.L. Evans, Usng multspectral magery and mult-return ldar to estmate trees and stand attrbutes n a southern bottomland Hardwood forest, Proceedngs of 2004 ASPRS Annual Conference, May 23-28, Denver, Colorado, unpagnated CD-ROM,(2004). Congalton, R.G., A revew of assessng the accuracy of classfcatons of remotely sensed data, Remote Sensng of Envronment, 37(1): 35-46,(1991). Dalponte, M., L. Bruzzone, and D. Ganelle, Fuson of hyperspectral and LIDAR remote sensng data for classfcaton of complex forest areas, IEEE ransactons on Geoscence and Remote Sensng, 46 (5), ,(2008).

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