3D model reconstruction from aerial ortho-imagery and LiDAR data

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1 3D model recostructio from aerial ortho-imagery ad LiDAR data ElSobaty Loutfia, Hamed Mahmoud, Ali Amr ad Salah Mahmoud Faculty of Egieerig Shoubra, Beha Uiversity, Cairo, Egypt (Received: Jul 13, 016; i fial form: Feb 15, 017) Abstract: Three dimesioal (3D) city model is a iterestig research topic i the last decade. This is because achievig rapid, automatic ad accurate extractio of a realistic model for the large urba area is still a challege. Cosequetly, icreasig the efficiecy of 3D city modelig is required. The objective of this research is to develop a simple ad efficiet semi-automatic approach to geerate a 3D city model for urba area usig the fusio of LiDAR data ad ortho-rectified imagery. These data sources provide efficiecy for 3D buildig extractio. This approach uses both LiDAR ad imagery data to delieate buildig outlies, based o fuzzy c-meas clusterig (FCM) algorithm. The third dimesio is obtaied automatically from the ormalized digital surface model (DSM) usig spatial aalyst tool. The 3D model is the geerated usig the multi-faceted patch. The accuracy assessmet for both height ad buildig outlies is coducted referrig to the groud truth ad by meas of visual ispectio ad differet quatitative statistics. The results showed that the proposed approach ca successfully detect differet types of buildigs from simple rectagle to circular shape ad LOD (level of detail) is formed by icludig the roof structures i the model. Keywords: 3D city model, LiDAR, Ortho-rectified aerial imagery, Data fusio, FCM, DSM 1. Itroductio The way of represetig earth has chaged with the fast growth of techologies. Two-dimesioal (D) maps have tured from the traditioal paper-based to digital forms ad from plaar D to a three-dimesioal (3D) represetatio of objects. Amog differet cartographic products, 3D city models have show to be valuable for several applicatios such as urba plaig ad maagemet, flood simulatio, lad moitorig, mobile telecommuicatio, 3D visualizatio, solar radiatio potetial assessmet, etc. (Tack et al., 01). With the fast advacemet of spatial ad spectral data acquisitio systems i recet years, umerous approaches for geeratig 3D city model from various types of data such as high-resolutio aerial images, airbore LiDAR data, terrestrial laser scaig, digital surface derives from stereo ad multi-stereo matchig ad heterogeeous data sources have bee preseted (Partovi et al., 013). I this regard, LiDAR ad photogrammetry are receivig major attetio due to their complemetary characteristics ad potetial. Nowadays, the algorithms that have bee used for automatic extractio ad visualizatio of 3D buildig archive various level of progress. However, extractig buildig boudary from LiDAR oly is still challegig task where the horizotal spacig of the sample poits is scattered. Therefore the eed for supplemetary data such as digital maps, high-resolutio satellite imagery ad ortho-imagery is ecessary (Park et al., 011). Differet trials are carried out to geerate 3D city model. Ruiji (004) recostructed 3D buildig models from aerial imagery ad LiDAR data. They used stereo aerial photographs to improve the geometric accuracy of the buildig model. Complex buildigs are recostructed usig the polyhedral buildig model i a data-drive orieted method. The proposed methodology has some limitatios caused by the data used. For example, two idividual buildigs might be detected as oe buildig if they are very close to each other. O the other had, the algorithm may fail i orderig roof polygos i the correct sequece. Aother type of limitatio is from the modelig process itself. I this work, it is assumed that all buildigs have rectagular footprits. Thus, o-rectagular footprits will be forced to have rectagular shapes. Hogjia ad Shiqiag (006) preseted a 3D buildig recostructio approach based o aerial images ad LiDAR data. First, a edge detectig algorithm combig Laplacia edge sharpeig with the threshold segmetatio was developed ad employed to detect the edges ad lies o the images. The, a method usig bidirectio projectio histogram was used to determie the corer poits of buildig ad extract the cotour of the buildig by searchig ad matchig gradually. The four corers of the buildig ca be extracted by combiig the two directios accordig to the directio of the histogram. The heights of the buildig were calculated accordig to the Laser poits withi the buildig boudary. Because of the limitatio of usig the bi-directio histogram ad the method to obtai the height of the roofs, the proposed method seems to be oly suitable for buildigs with rectagular shapes ad flat roofs. It is very hard to apply it for complex buildig recostructio. Lague (007) developed a object-orieted based method for 3D buildig extractio by itegratig LiDAR data ad aerial imagery. The object-orieted buildig model for 3D buildig extractio is developed by itegratig data collectio ad costructio methods, geometry ad topology, sematics ad properties as well Idia Society of Geomatics

2 as data storage ad maagemet of 3D buildigs ito oe model. Arefi et al. (008) proposed a automatic approach for recostructig models i three levels of details. The buildig outlies are detected by classificatio of ogroud objects ad buildig outlies are approximated by hierarchical filterig of miimum boudary rectagles (MBR) ad RANSAC-based straight lie fittig algorithm. Jarvis (008) outlied the itegratio of photogrammetric ad LiDAR data, withi GIS, for the accurate recostructio of a 3D realistic urba model i a semiautomated procedure. Kada ad McKidle (009) developed a approach for automatic recostructio of 3D buildig models from LiDAR data ad existig groud plas by assemblig buildig blocks from a library of parameterized stadard shapes. This approach based o a algorithm to decompose the buildig shape ito sets of oitersectig cells, ad for each cell, the roof top is recostructed by checkig the ormal directio of digital surface model (DSM). Sirmacek et al. (01) extracted 3D block models usig a object-orieted approach based o data fusio from LiDAR ad very high resolutio (VHR) optical imageries. bads Cell size (m) Camera Look Agle alog track across track RGB LMK1000 ±30º ±30º Table : Characteristics of LiDAR datasets Spacig/across track 1.15m Spacig/alog track 1.15m Vertical accuracy 0.10m Horizotal accuracy 0.5m Desity 1 Poit/m Wavelegth 1.047μm Altitude 1100m Swath width 800m Kwak (013) developed a framework for fully-automated buildig model geeratio by itegratig data-drive ad model-drive methods as well as exploitig the advatages of images ad LiDAR datasets. The major limitatio is that it ca model oly the types of buildigs which decompose ito rectagles. The mai goal of this research is to outlie a semiautomatic method for recostructig 3D city model i a third level of details from both LiDAR data ad orthoaerial imagery. The proposed work was accomplished usig a combiatio of the followig software sets: 1) Erdas Imagie 9. for data preprocessig, ad ) a set of programs geerated by the authors i Matlab eviromet for the rest of the work. Figure 1: Ortho-rectified image of the test area. Study area ad data sources The area is a part of the uiversity of New South Wales Campus, Sydey, Australia. It is largely urba area cotaiig residetial buildigs, large campus buildigs, a etwork of mai roads as well as mior road, trees, ad gree areas. The multispectral imagery was captured by film camera o Jue 005 at 1:6000 scale. The film was scaed i red, gree ad blue colour bads with 15 µm pixel size (GSD of 0.096m) ad radiometric resolutio of 16-bit. O the other had, LiDAR data were acquired over the study area o April 005 ad provided i ASCII format (eastig, orthig, heights, itesity ad returs for first ad last pulses). The LiDAR system used was the Optech ALTM 15. Figures 1 ad show the multispectral imagery ad the produced image from the LiDAR poits respectively. The characteristics of aerial image ad LiDAR data are provided i tables 1 ad respectively. Table 1: Characteristics of image datasets Figure : Digital Surface Model (DSM) geerated from the origial LiDAR poitcloud 3. Methodology

3 This study proposes a semi-automatic method for geeratig 3D city model by itegratig sigle aerial imagery ad LiDAR data. This method is composed of five key steps. The five mai procedures are discussed i the followig sectios. Figure 3 summarizes the workflow for the proposed techiques. from both the first ad the last echoes. The DSM was the filtered to geerate a digital terrai model (DTM) as show i figure 4. I this case, the Tilted Surface Method (Salah, 010) was used. I order to compesate for the differece i resolutio betwee image ad LiDAR data, DSM ad DTM grids were iterpolated to 30cm iterval. The, DSM was geerated. Fially, a height threshold of 3m was applied to DSM to elimiatig other objects such as cars as show i Figure 5. Figure 4: Digital Terrai Model (DTM) geerated usig the simple tilted plae filterig method Figure 3: Ortho-rectified image of the test area 3.1. Data preparatio Image ad LiDAR data co-registratio: Image registratio is the method of brigig differet datasets ito a sigle coordiate system. After multiple datasets acquired by differet sesors havig the same coordiate system it still requires some kid of additioal pixel-topixel matchig i order to esure higher reliability i data fusio techiques. This kid of matchig is kow as image co-registratio (Fikri, 01). The orthorectified image (already orthorectified by AAMHatch with a RMSE of 0.41m) is registered to the LiDAR itesity image usig the projective trasformatio i ERDAS 9. eviromet. The Root Mea Square (RMS) error from the modelig process was 0.098m. Followig the projective trasformatio, the image was resampled to 30cm x 30cm pixel size to match the resolutio of the LiDAR data. The biliear iterpolatio was used for resamplig, which results i a better quality image ad requires less processig time Geeratio of the DSM: The DSM represets the absolute heights, of o-groud objects such as buildigs ad trees, above the groud. First, the DSM was geerated Figure 5: DSM of the test area Texture stregth: Texture stregth is based o a statistical aalysis of the gray level gradiets g( r,, which are the first derivative of the gray level fuctio g(r,. The framework of the polymorphic texture stregth based o the Förster operator (Förster ad Gülch, 1987) has bee applied. The gray level gradiet g( r, ca be computed from Equatio 1: gr ( r, 1 g( r 1, g( r 1, g( r,. (1) gc ( r, g( r, c 1) g( r, c 1)

4 From the gray level gradiets g(r, of small widows, 3 x 3 pixels, a measure W for texture stregth is calculated as the average squared orm of the gray level gradiets ormalized by as show i Equatio : ' 1 g r gc g r gc L * g(r, L * ( ) L * ( ) ' ' k () W with L beig a liear low pass filter, Gaussia filter with 0.71, k equals the squared sum of compoets of the covolutio kerel. Thus, k= =0.5. The oise variace is equal to the square of the orm of the gray g(r,. W is high i image widows level grad cotaiig large gray level differeces. For texture stregth calculatio, a widow of 3 x 3 pixels is placed over the top left 3 x 3 block i the image ad the the texture is calculated for all pixel values withi that widow. The texture value is the writte to the cetral pixel of that widow i a ew raster layer. The the widow "moves" over oe pixel ad the process is repeated util all the pixels i the image have served as cetral pixels - except the oes aroud the outside. These edge pixels were filled i with the earest texture calculatio. Fially ad sice most texture calculatios are ot itegers, images were liearly scaled to the full rage for 8-bit data (0-55) as show i figure 6. Figure 6: Texture stregth of the DSM Referece data: I order to evaluate the accuracy of the classificatios, referece data were captured by digitizig buildigs, trees, roads ad groud i the orthophoto as show i figure 7. Durig this process, adjacet buildigs that were joied but obviously separated were digitized as idividual buildigs; otherwise, they were digitized as oe polygo. Roofs were first digitized ad the shifted so that at least oe poit of the polygo coicided with the correspodig poit o the groud. This is to overcome the horizotal layover problem of tall objects such as buildigs. Figure 7: Referece data 3.. Image classificatio 3..1 Fuzzy C-Meas clusterig (FCM): A cluster ca be defied as a group of pixels that are more similar to each other tha to members of other clusters. I most of the clusterig approaches, the distace measure used is the Euclidea distace. Thus, the distace measure is a importat meas by which the research ca ifluece the outcome of clusterig (Velmuruga ad Sathaam, 011). Clusterig ca be divided ito two mai approaches: hard clusterig ad the other oe is fuzzy clusterig (Moertii, 00). I hard clusterig, data is partitioig ito a specified umber of mutually exclusive subsets (Babuska, 001). I hard clusterig method, the boudary betwee clusters is fully defied. However i may real cases, the boudaries betwee clusters caot be clearly defied, where some patters may belog to more tha oe cluster. I such cases, the fuzzy clusterig method provides better results (Moertii, 00). FCM is the most represetative fuzzy clusterig algorithms sice it is suitable for tasks dealig with overlappig clusterig. I FCM, each data poit belogs to a cluster to some degree that is specified by a membership grade (Bora ad Gupta, 014). This techique was itroduced i 1973 ad first reported i 1974 ad subsequetly improved i 1981 (Sugaya ad Shathi, 01). It provides a method of how to group data poits that populate some multidimesioal space ito a specific umber of differet clusters. I Fuzzy clusterig methods, the objects could belog to several clusters simultaeously with differet degrees of membership, betwee 0 ad 1 idicatig their partial membership (Babuska, 001). This gives the flexibility to express that data poits ca belog to more tha oe cluster (Bora ad Gupta, 014). The clusterig algorithm is performed with a iterative optimizatio of miimizig a fuzzy objective fuctio (J m) defied as Equatio (3). c J m = (μ ik ) m d (x k, V i ) (3) i=1 k=1

5 where = umber of pixels c = umber of clusters μ ik= membership value of i th cluster of k th pixel m = fuzziess for each fuzzy membership. x k= vector of k th pixel V i= ceter vector of i th cluster d (x k,v i) = Euclidea distace betwee x kad V i The membership (μ ik) ca be estimated from the distace betwee k th pixel ad ceter of i th cluster as follows: { 0 μ ik 1 for alli, k c μ ik = 1 i=1 for allk 0 < μ ik < for alli k=1 (4) The ceter of cluster (V i) could be calculated by equatios (5) ad the membership value (μ ik ) could be calculated by equatios (6) as follow. V i = μ ik = [ k=1 (μ ik) m k=1 (μ ik ) x k m, 1 i c (5) 1 c ( d(x k,v i m 1 j=1 ) ] d(x k,v j, 1 i c, 1 k (6) J m ca be miimized by iteratio through equatios (5) ad (6). The first step of the iteratio is to iitialize the followig parameters: a fixed c, a fuzziess parameter (m), a threshold of covergece ε, ad a iitial ceter for each cluster, the computig μ ik ad V i usig Equatios (5) ad (6) respectively. The iteratio is stop whe the chage i V i betwee two iteratios is smaller tha ε. At last, each pixel is classified ito a combiatio of memberships of clusters. Several parameters must be specified before usig the FCM algorithm which iclude: the umber of clusters, c, the fuzziess expoet, m, the termiatio tolerace, ε, ad the orm-iducig matrix, A. Moreover, the fuzzy partitio matrix, U, must be iitialized (Babuska, 001). 3.. Subtractive clusterig: Fuzzy C-Meas algorithm requires the aalyst to pre-specify the umber of cluster ceters ad their iitial locatios. The quality of the results depeds strogly o the umber of cluster ceters ad their iitial locatios. Chiu (1994) proposed a effective algorithm, called the subtractive clusterig, for estimatig the umber ad iitial locatio of cluster ceters. By usig this method, the computatio is simply proportioal to the umber of data poits ad idepedet of the dimesio problem as show i equatio 7 (Moertii, 00). For a problem of c clusters ad m data poits, the required umber of calculatios is: N= m + (c 1)m (7) 1 st clustremiderclusters Cosider a group of data poits {x 1,x,...,x }, where, x i is a vector i the feature space. Assume that the feature space is ormalized so that all data are bouded by a uit hypercube. As well, cosider each data poit as a potetial cluster ceter ad defie a measure of the poit to serve as a cluster ceter. The potetial of x i deoted as P i is give i equatio 8. exp ( x i x j j=1 (r a ) ) (8) Where r a is a positive costat defiig a eighbourhood radius deotes the Euclidea distace. A data poit that has may eighbourig data poits will have a higher potetial value ad the poits outside will have little ifluece o its potetial. The first cluster ceter c1 is chose as the poit with the highest potetial. The potetial of c1 is referred to as PotVal (c1). The potetial of each data poit xi is the revised as follows: P i = P i PotVal(c i )exp ( X i c i ( r b ) ) (9) To avoid obtaiig closely spaced cluster ceters, rb is usually set to 1.5 ra. The data poits ear the first cluster ceter will greatly reduce their potetial ad will ulikely be selected as the ext ceter. From equatio 9 the potetial of all data poits will be reduced, after that the poit with the highest potetial is selected as the secod ceter. After the kth cluster ceter c k is determied, the potetial is revised as follows: P i = P i PotVal(c k )exp ( X i c k where c k = the locatio of the k th cluster ceter PotVal (c k) = potetial value. ( r b ) ) (10) The process proceeds util the stoppig criterio is reached. From the clusterig process, two coclusios ca be draw: 1) a poit with relatively high potetial has more chace to be selected as ceter tha less potetial poit; ) Cluster ceters are selected oly from the data poits eve if the actual cluster ceters are i the dataset or ot. (Che et al., 008). The Stregths of the subtractive clusterig are:1) reduces the time complexity; ad ) results are fixed ad has o radom cluster value. O the other had, accuracy is less ad cautious about choosig the eighbour radius (Leela et al., 014) Post-processig Morphologic operatios: Morphologic operatios have bee applied to separate objects i the image from the backgroud. The basic operatios of biary morphology are: erosio, dilatio, opeig, ad closig. A dilatio operatio elarges a regio, while erosio makes it smaller. A opeig operatio (erosio followed by

6 dilatio) ca get rid of small portios of the regio that jut out from the boudary ito the backgroud regio. A closig operatio (dilatio followed by erosio) ca close up iteral holes i a regio ad elimiate bays alog the boudary (Shapiro ad Stockma, 011). For clarity, the small buildigs were merged ito larger oes or deleted accordig to a 1m distace ad 30m area thresholds. A certai buildig was retaied if it was larger tha 30m ad/or adjacet to aother buildig by a distace less tha 1m. The area threshold represets the expected miimum buildig size, while the distace threshold was set to 1m to fill i ay holes or gaps produced by the classificatio process. Buildig borders were the cleaed by removig regios that were smaller tha 5 pixels i size ad that were coected to the buildig border. Cleaig thresholds less tha 5 pixels may leave the origial buildigs ucleaed, while thresholds larger tha 5 pixels may remove parts of the origial buildigs. The results are the detected buildigs without holes or ay oisy features. Figure 8: Smoothig a lie segmet with the Douglas Peucker algorithm (Douglas ad Peucker, 1973) Vectorizatio ad geeralizatio: I order to extract buildig boudaries, the smoothed biary image is coverted from raster to vector format. After that, the obtaied boudaries eed more processig to overcome the problem of irregularities ad to adjust the rectagularity of the polygos. Oe of the most commo used geeralizatio algorithms is The Ramer Douglas Peucker algorithm (RDP). This algorithm reduces the umber of poits i a curve that is approximated by a series of poits. The first form of the algorithm was suggested i 197 ad the modified by Douglas ad Peucker (1973). This approach automatically marks the first ad last poits to be kept, ad the it fids the furthest poit from the lie segmet betwee the first ad last poits as ed poits. If the vertex is closer tha the tolerace (ε) to the lie segmet the ay poits ot curretly marked to be kept ca be discarded without the simplified curve beig worse tha ε. If the vertex that is furthest away from the lie segmet is greater tha ε from the approximatio the that poit must be kept. The algorithm recursively calls itself with the first poit ad the worst poit ad the with the worst poit ad the last poit (which icludes markig the worst poit beig marked as kept). Whe the recursio is completed a ew output curve ca be geerated cosistig of all (ad oly) those poits that have bee marked as kept (Douglas ad Peucker, 1973) Three dimesioal model costructio For the costructio of the three dimesioal model, Multi-Faceted Patches are used. I this regard, x, y, ad z coordiate of the faces of a give buildig are specified as matrix. MATLAB draws oe face per colum, producig a sigle patch with multiple faces as show i figure 9(The MathWorks, 015). Figure 9: The cocept of multi-faceted patches 4. Aalysis ad results To iitialize FCM algorithm, the parameters are set to the followig values: the total umber of clusters c is iitialized as 13 (as obtaied by the subtractive clusterig), the maximum umber of iteratio as 100, the expoet for μ ik as.0 ad a miimum improvemet ε of 1e -6. The clusterig process termiates whe the maximum umber of iteratios is reached, or whe the objective fuctio improvemet betwee two cosecutive iteratios is less tha the miimum amout of improvemet specified. Figure 10 shows the FCM output. The obtaied overall classificatio accuracy was 87.84%, while the per-class accuracies were 83.51%, 89.06%, 8.83% ad 9.33% for buildigs, trees, roads ad grass respectively.

7 Figure 10: Classified image usig fuzzy c-meas clusterig I order to extract buildigs from the classified image, the classified image was compared with the DSM ad texture stregth images. Digital values of the classified image are coverted to 1 (backgroud) if it correspods to 0 i the DSM ad/or higher value (over 0.5) i the texture stregth image. Otherwise, the pixel value is kept as it is. The result is a buildig image with oisy features as show i figure 11. Figure 1: The fial detected buildigs The smoothed image is the coverted from raster to vector format to extract buildig boudaries. The Ramer Douglas Peucker algorithm (RDP) was used to overcome the problems of irregularities ad adjust the rectagularity of the polygos. The tolerace was iitially specified equal to the pixel size of the data. If the output still cotais too much detail, the the tolerace ca be doubled ad so o. Similarly, if the output lies do ot have eough detail, the tolerace ca be halved. Figure 13 shows the extracted buildigs before ad after the simplificatio ad adjustmet of the rectagularity. Figure 11: Classified image usig fuzzy c-meas clusterig Morphologic operatios were the applied to merge small buildigs ito larger oes ad fill i holes accordig to the specified 1m distace ad 30m area thresholds. Buildig borders were the cleaed accordig to the specified 5 pixels threshold. The result was a image that represets the detected buildigs with a cosiderable lower degree of oisy features as show i Figure 1. Figure 13: Buildigs map before ad after adjustig the rectagularity I order to costruct 3D model, two mai items must exist: buildig outlies, ad height iformatio. I the proposed

8 methodology ad to determie the heights of the buildigs, a set of radom sample poits (costraied by buildig footprits) are geerated at the corers of each buildig as show i figure 14. The result is a feature class cotaiig groups of poits. Elevatio iformatio extracted from elevatio surface ca be added to each poit as a attribute. The MATLAB code is the used to costruct the 3D model as show i figure 15. Figure 16: Distributio of the GPS cotrol poits Table 3: The accuracy estimate of the buildig vectorizatio process Figure 14: Geeratio of a set of radom sample poits (costraied by buildig footprits) Δ E Poit (GPS map) (GPS map) ΔE + ΔN Mea RMS 0.51 Δ N 5. Coclusio ad future work This paper discusses the fusio of LiDAR ad aerial orthorectified imagery data for the costructio of 3D city models. I the proposed approach data classificatio was carried out with a overall accuracy of almost 83.51%. The horizotal accuracy of buildig outlies reached 0.51 m, while vertical accuracy raged betwee 15-0 cm. As a future step ad i order to maximize the beefits of the proposed method, the authors aim at icreasig the degree of automatio ad level of details. Due to the limitatio of LiDAR data o had, curret work has bee doe with oly oe data set. I the future it is plaed to process more ad larger test areas i order to cofirm the results foud so far. Ackowledgemets Figure 15: buildigs extracted from the study area I order to evaluate the plaimetric accuracy of the resulted vector map, three GCPs were determied by field surveys. The GCPs were selected to be evely distributed throughout the study area as show i Figure 16, ad a compariso was carried out betwee GPS observatios ad the extracted buildig data coordiates with a RMSE of 0.51m as show i Table 3. The vertical accuracy of the costructed 3D buildig model is withi 15-0 cm which matches with a vertical accuracy of LiDAR. The Authors wish to ackowledge AAMHatch for the provisio of the UNSW dataset. Refereces Arefi, H., J. Egels, M. Hah ad H. Mayer (008). Levels of detail i 3D buildig recostructio from LIDAR data. Iteratioal Archives of the Photogrammetry, Remote Sesig ad Spatial Iformatio Scieces. 37, Babuska, R. (001). Fuzzy ad eural cotrol - DISC course lecture otes. Faculty of Iformatio Techology ad Systems, Delft Uiversity of Techology, Delft, the Netherlads.

9 Bora, D. ad A. Gupta (014). A comparative study betwee fuzzy clusterig algorithm ad hard clusterig algorithm. Iteratioal Joural of Computer Treds ad Techology (IJCTT) Vol 10, No., Che, J., Z. Qi, Z. ad J. Jia (008). A weighted mea subtractive clusterig algorithm. Iformatio Techology Joural 7(), Chiu, S. (1994). Fuzzy model idetificatio based o cluster estimatio. Rockwell Sciece Ceter Thousad Oaks, Califoria, Douglas, D. ad T. Peucker (1973). Algorithms for the reductio of the umber of poits required to represet a digitized lie or its caricature. The Caadia Cartographer 10(), Fikri, A. (01). A methodology for processig raw LiDAR data to support urba flood modelig framework. Ph.D. Dissertatio, Istitute for Water Educatio, Delft Uiversity of Techology. Förster, W. ad E. Gülch (1987). A fast operator for detectio ad precise locatio of distict poits, corers ad cetres of circular features. I Proceedigs of the ISPRS Itercommissio Workshop o Fast Processig of Photogrammetric Data, 4 Jue 1987, Iterlake, Switzerlad, Hogjia, Y. ad Z. Shiqiag (006). 3D buildig recostructio from aerial CCD image ad sparse laser sample data. Optics ad Lasers i Egieerig 44(6): Jarvis, A. (008). Itegratio of photogrammetric ad LiDAR data for accurate recostructio ad visualizatio of urba eviromets. MSc. Dissertatio, Departmet of Geomatics Egieerig, Uiversity of Calgary. Kada, M. ad L. McKiley (009). 3D buildig recostructio from LiDAR based o a cell decompositio approach. Iteratioal Archives of the Photogrammetry, Remote Sesig ad Spatial Iformatio Scieces, 38, Kwak, E. (013) Automatic 3D buildig model geeratio by itegratig LiDAR ad aerial images usig a hybrid approach. Ph.D. Dissertatio, Departmet of Geometrics Egieerig, Uiversity of Calgary. Lague, W. (007) Object-orieted model based 3D buildig extractio usig airbore laser scaig poits ad aerial imagery. M.Sc. Dissertatio, Istitute of Geo- Iformatio Sciece ad Earth Observatio, ITC. Leela, V., K. Sakthi priya ad R. Maikada (014). Comparative study of clusterig techiques i Iris data sets. World Applied Sciece Joural, 9, 4-9. Moertii, V.S. (00). Itroductio to five data clusterig algorithms. Itegral, Vol 7, No, Park, H., M. Salah ad S. Lim (011). Accuracy of 3Dmodels derived from aerial laser scaig ad aerial ortho-imagery. Surveyig Review, 43, 30, Partovi, T., H. Arafi, T. Kraub ad P. Reiartz (013). Automatic model selectio of 3D recostructio of buildigs from satellite imagery. Iteratioal Archives of Photogrammetry, Remote Sesig ad Spatial Iformatio Scieces. Volume XL-1/W. Tehra, Ira. Ruiji, Ma. (004). Buildig model recostructio from LiDAR data ad aerial photographs. PhD thesis, The Ohio State Uiversity. Salah, M. (010). Towards automatic feature extractio from high resolutio digital imagery ad LiDAR data for GIS applicatios. Ph.D. Dissertatio, Departmet of Surveyig Egieerig, Uiversity of Beha. Shapiro, L. ad G. Stockma (011). Computer visio. Pretice Hall, New Jersey. Sirmacek, B., H. Taubeboeck ad P. Reiartz (01). A ovel 3D city modellig approach for satellite stereo data usig 3D active shape models o DSMS. Iteratioal Archives of the Photogrammetry, Remote Sesig ad Spatial Iformatio Scieces, Volume XXXIX-B3, 01- XXIIi ISPRS Cogress, Melboure, Australia. Sugaya, R. ad R. Shathi (01). Fuzzy C- meas algorithm- A review. Iteratioal Joural of Scietific ad Research Publicatios, (11), November 01 Editio, ISSN Tack, F., G. Buyuksalih ad R. Goosses (01). 3D buildig recostructio based o give groud pla iformatio ad surface models extracted from spacebore imagery. ISPRS Joural of Photogrammetry ad Remote Sesig, vol (67), The MathWorks (015). 3-D visualizatio. The MathWorks, Ic. e.pdf Velmuruga, T. ad T. Sathaam (011). A comparative aalysis betwee k-medoids ad fuzzy C-meas clusterig algorithms for statistically distributed data poits. Joural of Theoretical ad Applied Iformatio Techology, Vol 7 No 1,