AERIAL IMAGES AND LIDAR DATA FUSION FOR AUTOMATIC FEATURE EXTRACTION USING THE SELF-ORGANIZING MAP (SOM) CLASSIFIER

Transcription

1 In: Brear F, Pierro-Deseilligny M, Vosselman G (Eds) Laser scanning 2009, IAPRS, Vol. XXXVIII, Par 3/W8 Paris, France, Sepember 1-2, 2009 Conens Keyord index Auhor index AERIAL IMAGES AND LIDAR DATA FUSION FOR AUTOMATIC FEATURE EXTRACTION USING THE SELF-ORGANIZING MAP (SOM) CLASSIFIER M. Salah a, *, J.Trinder a, A.Shaker b, M.Hamed b, A.Elsagheer b a School of Surveying and Spaial Informaion Sysems, The Universiy of Ne Souh Wales, SYDNEY NSW 2052, Ausralia - (m.gomah, j.rinder)@uns.edu.au b Dep. of Surveying, Faculy of Engineering Shoubra, Benha Universiy, 108 Shoubra Sree, Cairo, Egyp - ahmshaker@link.ne, prof.mahmoudhamed@ yahoo.com, alielsagheer@yahoo.com ISPRS WG III/2 "Poin Cloud Processing" KEY WORDS: Aerial Images, Lidar, GLCM, Aribues, Building Deecion, Classificaion, Self-Organizing Map. ABSTRACT: This paper presens ork on he developmen of auomaic feaure exracion from mulispecral aerial images and lidar daa based on es daa from o differen sudy areas ih differen characerisics. Firs, e filered he lidar poin clouds o generae a Digial Terrain Model (DTM) using a novel filering echnique based on a linear firs-order equaion hich describes a iled plane surface, and hen he Digial Surface Model () and he Normalised Digial Surface Model () ere generaed. Afer ha a oal of 22 uncorrelaed feaure aribues have been generaed from he aerial images, he lidar inensiy image, and. The aribues include hose derived from he Grey Level Co-occurrence Marix (GLCM), Normalized Difference Vegeaion Indices () and slope. Finally, a SOM as used o deec buildings, rees, roads and grass from he aerial image, lidar daa and he generaed aribues. The resuls sho ha using lidar daa in he SOM improves he accuracy of feaure deecion by 38% compared ih using aerial phoography alone, hile using he generaed aribues as ell improve he deecion resuls by a furher 10%. The resuls also sho ha he folloing aribues conribued mos significanly o deecion of buildings, rees, roads and grass respecively: enropy (from GLCM) derived from ; slope derived from ; homogeneiy (from he GLCM) derived from ; and homogeneiy derived from. 1. INTRODUCTION Research on auomaed feaure exracion from aerial images and lidar daa has been fuelled in recen years by he need for daa acquisiion and updaing for GIS. The high dimensionaliy of aerial and saellie imagery presens a challenge for radiional classificaion mehods based on saisical assumpions. Arificial Neural Neorks (ANNs) on he oher hand may represen a valuable alernaive approach for land cover mapping for such highly dimensional imagery. ANNs require no assumpion regarding he saisical disribuion of he inpu paern classes (Hugo e al., 2007) and hey have o imporan properies: he abiliy o learn from inpu daa; and o generalize and predic unseen paerns based on he daa source, raher han on any paricular a priori model. The Self- Organizing Map is one of he mos commonly used neural neork classifiers. I can be adjused o adap o he probabiliy disribuion of he inpus (Seo and Liu, 2003). In his paper e applied he SOM algorihm for combining mulispecral aerial imagery and lidar daa so ha he individual srenghs of each daa source can compensae for he eakness of he oher. The lo conras, occlusions and shado effecs in he image ere compensaed by he accuraely deeced planes in he lidar daa. Hoever, edges of feaures are no locaed accuraely in lidar poin clouds because of he lidar s sysem discree sampling inerval of 0.5m o 1m, (Li and Wu, 2008). Therefore, e have derived 22 aribues from boh aerial image and lidar daa by a number of algorihms o alleviae his problem. To evaluae he conribuion of he lidar daa and he generaed aribues in he deecion process, hree separae SOM classificaion ess ere carried ou using differen inpu daa o deermine he accuracy of feaure deecion agains a reference map: 1. The aerial image, he lidar daa and he derived aribues, 2. The aerial image and he lidar daa, 3. The aerial image only as inpu daa for he SOM. Finally, he conribuions of he individual aribues o he qualiy of he classificaion resuls ere evaluaed. 2. RELATED WORK There have been many research effors on he applicaion of aerial images and lidar daa for building exracion. Roenseiner e al., (2005) evaluaed a mehod for building deecion by he Dempser-Shafer fusion of lidar daa and mulispecral images. The heurisic model for he probabiliy mass assignmens for he mehod as validaed, and rules for uning he parameers of his model ere discussed. Furher, hey evaluaed he conribuions of he individual cues used in he classificaion process o he qualiy of he classificaion resuls, hich shoed ha he overall correcness of he resuls can be improved by fusing lidar daa ih mulispecral images. Maikainen e al., (2007) used a classificaion ree approach for building deecion. A digial surface model () derived from las pulse laser scanner daa as firs segmened ino classes ground and building or ree. Differen combinaions of 44 * Corresponding auhor. 317

2 In: Brear F, Pierro-Deseilligny M, Vosselman G (Eds) Laser scanning 2009, IAPRS, Vol. XXXVIII, Par 3/W8 Paris, France, Sepember 1-2, 2009 Conens Keyord index Auhor index inpu aribues ere used. The aribues ere derived from he las pulse, firs pulse and a colour aerial orho image. In addiion, shape aribues calculaed for he segmens ere used. Compared ih a building reference map, a mean accuracy of almos % as achieved for exracing buildings. The numbers of sudies ha have uilized ANNs for highly specrally dimensional image analysis are limied. Jen-Hon and Din-Chang (2000) applied he self-organized map classificaion (SOM) mehod for SPOT scene land cover classificaion. Hugo e al. (2007) assessed he poenial of he SOM neural neork o exrac complex land cover informaion from medium resoluion saellie imagery using MERIS Full Resoluion daa. 3. STUDY AREA AND DATA SOURCES To es daa ses of differen characerisics ere used in his sudy. The firs area is a par of he Universiy of Ne Souh Wales campus; Sydney Ausralia, covering approximaely 0m x 0m. I is a largely urban area ha conains residenial buildings, large Campus buildings, a neork of main roads as ell as minor roads, rees and green areas. Lidar daa ere acquired over he sudy area in April 2005, using an Opech ALTM 1225 ih a pulse repeiion frequency (PRF) of 25kHz a a avelengh of 1.047µm. The mulispecral imagery as capured by film camera by AAMHach on June 2005 a 1:00 scale. The film as scanned in hree colour bands (red, green and blue) in TIFF forma, ih 15µm pixel size (GSD of 0.09m) and radiomeric resoluion of 16-bi as shon in Figure 1(lef). The second sudy area is a par of Bahurs ciy; NSW Ausralia, covering approximaely 0m x 0m. I is a largely rural area ha conains small sized residenial buildings, road neorks, rees and green areas. Lidar daa as acquired over he area by a Leica ALS sensor in Augus 2008, operaing ih a PRF of 1kHz a a avelengh of 1.064µm. The mulispecral imagery as capured by a Leica ADS sensor on Ocober Three colour band (red, green and blue) images ere colleced a cm GSD as shon in Figure 1(righ). Figure 1. Orhophoos for (lef), Bahurs (righ). 4. METHODOLOGY non-ground poins. Abo Akel e al., (2004) used a robus mehod ih orhogonal polynomials and road neork for filering of lidar daa in urban areas. The basic assumpion of he approach adoped in his paper is ha he heigh of a ground poin is loer han he heighs of neighbouring non-ground poins and he errain can be described using a simple iled plane ihin small areas. The mehod sared by dividing he daa ino small m x m square paches. In principle, he pach should be larger han he larges building ihin he es area in such a ay ha no objec ihin he sudy area can oally cover he pach. Oherise, poins falling over buildings ill be classified as on-errain poins. Then, he algorihm consruced a marix, A (m, n), here m and n are he number of paches in boh X and Y direcions respecively, see figure 2(lef). Then, he loer lef and he upper righ coordinaes for each pach ere deermined and sored. Daa from boh he firs and he las pulse echoes ere used in order o obain denser errain daa and hence a more accurae filering process. For each pach e fied iled plane surfaces o he errain poins using equaion (1): here Z = a + b * x + c (1) X, Y and Z = coordinaes of lidar poin clouds. The process of plane surface consrucion sared ih he deecion of o poins, one on each pach border, in he Y direcion, hich represen he minimum elevaions on hese borders. The o poins ere hen shifed in X direcions by a reasonable value, for example 0m, hile Z values remained consan, see figure 2(middle). The reason behind he shifing process is o creae a ne se of o poins o consruc a comparison plane, see figure 2(righ), hich includes he four deeced poins (o old and o ne) and represens he general slope of he pach. The main assumpion here as ha he surface varies sloly from region o region over he pach of ineres. The four poins ere hen used o deermine he bes esimaes of he coefficiens of he plane by a leas squares soluion. Based on he compued coefficien values of a, b and c, equaion (1) as applied for each individual poin i ih coordinaes X i, Y i in he lidar poin clouds o find he Z value of is corresponding poin on he plane. From a comparison of he elevaion of each daa poin ih is corresponding elevaion on he generaed plane surface, all poins belo, on or above his plane ihin he hreshold (=15cm), ere classified as onerrain poins. Threshold as equal o he lidar sysem accuracy. Figure 2 demonsraes he seps of he filering process, hile figure 3 shos a par of he resuls for daa. Feaure exracion of he sudy area as implemened in several sages as follo: 4.1 Filering of lidar poin clouds Filering is he process of separaing on-errain poins (DTM) from poins falling ono naural and human made objecs. Axelsson (2000) developed an adapive Triangulaed Irregular Neork (TIN) mehod o find ground poins based on seleced seed ground measuremens. Whiman e al., (2003) used an elevaion hreshold and an expanding search indo o remove Figure 2. Dividing he area ino small square paches (lef), deecing and shifing he loes o poins of he pach (middle) and consrucing he iled plane and removing he non-ground feaures (righ). 318

3 In: Brear F, Pierro-Deseilligny M, Vosselman G (Eds) Laser scanning 2009, IAPRS, Vol. XXXVIII, Par 3/W8 Paris, France, Sepember 1-2, 2009 Conens Keyord index Auhor index Figure 3. Poins filered as on-errain poins in green colour (lef) compared o he aerial image (righ). Finally, he filered lidar poins ere convered ino an image DTM, he as generaed from he original Lidar poin clouds (firs and las pulses) and he as generaed by subracing he DTM from he, see figures 4. These are grey scale images here ones range from dark for lo elevaions o brigh for high elevaions. Figure 4. (lef), DTM (middle) and he (righ). In order o analyze he produced filering errors, a sample of ell disribued filered poins has been seleced, overlaid on he orhophoo and classified visually as ground and nonground. Compared o hose resuls, our algorihm has achieved commission errors, classifying non-ground poins as ground poins, and omission errors, classifying ground poins as nonground poins, of abou 3.1% and 5.2% for case sudy and 5.9% and 9.4% respecively for Bahurs case sudy. Compared ih oher mehods, his echnique is simple and requires no ork uning parameers excep for he pach size. Also, fiing a simple iled plane ino a small square area effecively removes mos of he non-ground poins especially hose on lo vegeaion. 4.2 Generaion of aribues Feaures or aribues commonly used for feaure exracion from aerial images and lidar daa include heigh exure (Maas and Vosselman, 1999) or surface roughness (Brunn and Weidner, 1998) of he lidar daa, reflecance informaion from aerial images (Vögle and Seinle, 2000) or lidar daa (Hug, 1997), he difference beeen firs and las pulses of he lidar daa (Alharhy and Behel, 2002). The aribues calculaed for predefined segmens or single pixels are presened as inpu daa for a classificaion mehod. Before generaing he aribues, he aerial phoographs (already orhorecified by AAMHach) ere regisered o he lidar inensiy image using a projecive ransformaion. The Roo Mean Square (RMS) errors from he modelling process ere 0.01m and 0.01m in X and Y respecively and he oal RMS error as 0.02m, indicaing an accurae regisraion beeen image and lidar daa and demonsraing ha mos of he geomeric disorions had already been removed by he orhorecificaion process. Folloing he ransformaion, he image as resampled o 30cm x 30cm and cm x cm cell size in case of and Bahurs respecively o mach he resoluion of he lidar daa. A bilinear inerpolaion as used for resampling, hich resuls in a beer qualiy image han neares neighbour resampling and requires less processing han cubic convoluion. In our es, a se of 78 possible aribues ere seleced as shon in Table 1. Because of he ay he exure equaions derived from he GLCM (Haralick, 1979) are consruced, many of hem are srongly correlaed ih one anoher. Clausi (2002) analysed he correlaions among he exure measures o deermine he bes subse of measures and shoed ha Conras, Correlaion and Enropy used ogeher ouperformed any one of hem alone. If only one can be used, he recommended choosing from amongs Conras, Dissimilariy or Homogeneiy. Based on hese experimens, only 22 of he 78 possible aribues ere uncorrelaed and hence available for he classificaion process as shon in he shaded cells of Table 1. The aribues include hose derived from he GLCM, Normalized Difference Vegeaion Indices (), sandard deviaion of elevaions, slope and he polymorphic exure srengh based on he Försner operaor (Försner and Gülch, 1987). Aribues Aribue R G B I N Mean Specral S. Deviaion Srengh Conras Dissimilariy Homogeneiy A.S.M GLCM Enropy Mean Variance Correlaion Heigh SD Slope Table 1. The full se of he aribues; aribues available for he classificaion are shon by shading. 4.3 Land cover classificaion The SOM (Kohonen, 1999) as used for classifying he images. Figure 5 illusraes he basic archiecure of an SOM. The inpu layer represens he inpu feaure vecor and hus has neurons for each measuremen dimension. In our sudy, e applied a separae neuron for each band. Therefore, he SOM has 29 inpu neurons hich are: 22 generaed aribues, 3 image bands (R, G and B), inensiy image, DTM, and. For he oupu layer of an SOM, e used a 15 x 15 array of neurons as an oupu for he SOM. This number as seleced because, as recommended by Hugo e al. (2007), small neorks resul in some unrepresened classes in he final labelled neork, hile large neorks lead o an improvemen in he overall classificaion accuracy. Each oupu layer neuron is conneced o all neurons in he inpu layer by synapic eighs. Inpu layer Oupu layer Synapic eighs Inpu feaure vecor Figure 5. Example of SOM ih a 4 neurons inpu layer and an equally spaced 5x5 neurons oupu layer. 319

4 In: Brear F, Pierro-Deseilligny M, Vosselman G (Eds) Laser scanning 2009, IAPRS, Vol. XXXVIII, Par 3/W8 Paris, France, Sepember 1-2, 2009 Conens Keyord index Auhor index During he raining period, each neuron ih a posiive aciviy ihin he neighbourhood of he inning neuron paricipaes in he learning process. A inning processing elemen is deermined for each inpu vecor based on he similariy beeen he inpu vecor and he eigh vecor (Jen-Hon and Din-Chang, 2000). Le X= (x 1, x 2, x 3, x n ) be a vecor of reflecances for a single pixel inpu o he SOM. We ook he previously menioned 29 values (22 generaed aribues, 3 image bands, R, G and B, inensiy image, DTM, and ) as he vecor of reflecances of each pixel. Iniially, synapic eighs beeen he oupu and inpu neurons ere randomly assigned (0-1). The eigh vecor, W, corresponding o oupu layer neuron j can be rien as in equaion (2): T = [ j1 j2... jp ] j = 1, 2, 3,..., N (2) The disances beeen a eigh vecor and an inpu feaure vecor ere hen calculaed, and he neuron in he oupu layer ih he minimum disance o he inpu feaure vecor (knon as he inner) as hen deermined as in equaion (3): Where n inner = argmin = (x ) 2 (3) i 1 i x i = he inpu o neuron i a ieraion = he synapic eigh from inpu neuron i o oupu neuron j a ieraion. The eigh of he inner and is neighbours ihin a radius γ ere hen alered (hile hose ouside ere lef unalered) according o a learning rae α as shon in equaions (4, 5): + 1 (x = + α i ), d inner γ j (4) + 1 =, d inner γ j (5) Inpu Neurons Ou pu Iniial Min. Max. neurons γ α α (15*15) Table 2. SOM parameers used for he es. Figure 6. Resuls of he SOM classificaion for (lef) and Bahurs (righ). Red: buildings, green: rees, black: roads and grey: grass. 5. RESULTS AND ANALYSIS 5.1 Evaluaion of he classificaion resuls To evaluae he conribuion of lidar daa and aribues o he classificaion accuracy, he SOM as performed hree imes. By using he aerial image alone on he daa, many buildings ere classified as roads because hey have he same specral reflecance and he classificaion accuracy as 49% (Figure 7b). The use of he lidar daa along ih he aerial image increased he classificaion accuracy o abou 87% due o is abiliy o deec planes accuraely bu sill some errors occurred due o he poor horizonal accuracy of edge deecion in he lidar daa (Li and Wu, 2008) (Figure 7c). The use of he aerial imagery, lidar daa and exraced aribues improved he classificaion accuracy again o abou 98% since he aribues compensaed for he eakness of lidar for edge deecion (Figure 7d). The classificaion accuracies for he Bahurs case sudy for he hree differen cases ere 52%, 85% and 94% respecively. here α = he learning rae a ieraion d = he disance beeen he inner and inner j oher neurons in he oupu layer. α as calculaed from equaion (6): 1 α min α = α max (6) α max The parameer values for our es ere seleced according o he suggesions proposed by Vesano e al. (2000) o improve he classificaion accuracy ihou giving any compuer memory consrains, see able 2. Figure 6 shos he classificaion resuls. Figure 7. For daa (a) The mulispecral aerial image, (b) The SOM classified image using aerial image only, (c) The SOM classified image using aerial image and lidar daa, (d) The SOM classified image using aerial image, lidar daa and aribues. 5.2 Conribuions of he individual aribues Furhermore, e evaluaed he conribuions of he individual aribues o he qualiy of he classificaion resuls. The red, green and blue bands of he aerial image ere considered as he primary daa source and ere available in each es. Figure 8 shos ha: inensiy image and enropy derived from performed bes for building deecion ih 73% and 83% average classificaion accuracies respecively; homogeneiy, srengh and slope derived from performed bes for ree deecion ih 82%, 82% and 86% average classificaion accuracies respecively;, enropy and homogeneiy 320

5 In: Brear F, Pierro-Deseilligny M, Vosselman G (Eds) Laser scanning 2009, IAPRS, Vol. XXXVIII, Par 3/W8 Paris, France, Sepember 1-2, 2009 Conens Keyord index Auhor index derived from performed bes for road deecion ih 94%, 95% and 97% average classificaion accuracies respecively; inensiy image and enropy and homogeneiy derived from performed bes for grass deecion ih 68%, 72% and 77% average classificaion accuracies respecively Buildings enropy_r enropy_r homogeneiy_r srengh_r enropy_g homogeneiy_g srengh_g Trees Roads Grass homogeneiy_r srengh_r enropy_g homogeneiy_g srengh_g Bahurs enropy_b homogeneiy_b srengh_b inensiy homogeneiy_ inensiy srengh_ inensiy Bahurs enropy_r homogeneiy_r srengh_r enropy_g homogeneiy_g srengh_g enropy_b homogeneiy_b srengh_b inensiy homogeneiy_ inensiy Bahurs enropy_r homogeneiy_r srengh_r enropy_g homogeneiy_g srengh_g enropy_b homogeneiy_b srengh_b inensiy homogeneiy_ inensiy Bahurs enropy_b homogeneiy_b srengh_b inensiy homogeneiy_ inensiy homogeneiy _dsm srengh _dsm enropy _ndsm homogeneiy _ndsm srengh _ndsm enropy _ndsm srengh_ inensiy homogeneiy _dsm srengh _dsm srengh_ inensiy homogeneiy _dsm srengh _dsm enropy _ndsm srengh_ inensiy homogeneiy _dsm srengh _dsm homogeneiy _ndsm srengh _ndsm homogeneiy _ndsm srengh _ndsm enropy _ndsm homogeneiy _ndsm srengh _ndsm Figure 8. Conribuions of he individual aribues o he qualiy of he classificaion resuls. 5.3 Evaluaion of building resuls Buildings as he mos imporan feaures of he urban landscape ere evaluaed individually. Firs, building regions ere reained if hey ere larger han he expeced minimum building area (30 and m 2 for and Bahurs respecively) and/or ere adjacen o a larger homogeneous region by a disance less han 1m. Finally, building borders ere cleaned by removing srucures ha ere smaller han 8 pixels and ha ere conneced o he image border. In order o evaluae he classificaion accuracy, buildings ere manually digiized in he mulispecral images o serve as he reference daa. Adjacen buildings ha ere joined bu obviously separaed ere digiized as individual buildings. Oherise, hey ere merged as one polygon. In comparison ih he reference daa 96% of all buildings ere deeced ih ell defined edges and also ihou holes. Also o give a good insigh o he behaviour of he building deecion process, compleeness and correcness of deecion resuls, described in (Roenseiner e al., 2005), ere compued and figure 9 shos hese values for deeced buildings. For he case sudy, buildings around m 2 ere deeced ih compleeness and correcness around 88% and 84% respecively and improving for increasing building size. For Bahurs case sudy, buildings around 30m 2 ere deeced ih boh compleeness and correcness around 73% and % respecively and improving for increasing building size. For boh cases, all buildings larger han m 2 ere deeced ih boh compleeness and correcness over %. Similar accuracies have been repored in Roenseiner e al. (2005). They evaluaed a mehod for building deecion by he Dempser-Shafer fusion of airborne laser scanner (ALS) daa and mulispecral images using differen daa ses. By Dempser-Shafer fusion, 95% of all buildings larger han m 2 ere correcly deeced. Percen Percen Bahurs Compleeness Correcness Area (m 2 ) Area (m 2 ) 230 Compleeness Correcness Figure 9. Compleeness and correcness agains building areas: for (op) and Bahurs (boom)

6 In: Brear F, Pierro-Deseilligny M, Vosselman G (Eds) Laser scanning 2009, IAPRS, Vol. XXXVIII, Par 3/W8 Paris, France, Sepember 1-2, 2009 Conens Keyord index Auhor index 6. CONCLUSION A mehod for feaure exracion based on Self-Organizing Map fusion of lidar, mulispecral aerial images and 22 auxiliary aribues as presened. The aribues ha ere generaed from he lidar daa and mulispecral images include: exure srengh, Grey Level Co-occurrence Marix (GLCM) homogeneiy and enropy, Normalized Difference Vegeaion Indices () and slope. The approach significanly improves he accuracy of feaure deecion over approaches hen only images and/or lidar daa are used. The resuls sho ha using lidar daa in he SOM improves he accuracy by 38% compared ih using aerial phoography alone, hile using he generaed aribues as ell improve he resul by a furher 10%. An invesigaion ino he conribuions of he individual aribues shoed ha: enropy derived from performed he bes for building deecion; slope derived from performed he bes for ree deecion; homogeneiy derived from performed he bes for road deecion; and homogeneiy derived from performed he bes for grass deecion. In he fuure, e inend o consruc a hybrid classifier based on muliple classifiers operaing simulaneously o achieve a more effecive and robus decision making process. REFERENCES Abo Akel, N., Zilbersein, O. and Doysher, Y., A Robus Mehod Used ih Orhogonal Polynomials and Road Neork for Auomaic Terrain Surface Exracion from LIDAR Daa in Urban Areas. Inernaional Archives of Phoogrammery, Remoe Sensing and Spaial Informaion Science, Vol. 35, ISPRS Alharhy, A. and Behel, J., Heurisic Filering and 3d Feaure Exracion from LIDAR Daa. ISPRS he Inernaional Achieves of he Phoogrammery, Remoe Sensing and Spaial Informaion Sciences Vol. XXXIV. Axelsson, P., DEM Generaion from Laser Scanner Daa Using Adapive TIN Models. Inernaional Archives of Phoogrammery and Remoe Sensing, XXXIII, Par B3: Brunn, A. and Weidner, U., Hierarchical Bayesian Nes for Building Exracion Using Dense Digial Surface Models. ISPRS Journal of Phoogrammery & Remoe Sensing, 53(5), pp Clausi, D. A., An Analysis of Co-Occurrence Texure Saisics as a Funcion of Grey-Level Quanizaion. Canadian Journal of Remoe Sensing, vol. 28 no. 1 pp Försner, W. and Gülch, E., A Fas Operaor for Deecion and Precise Locaion of Disinc Poins, Corners and Cenres of Circular Feaures. In ISPRS Inercommission Workshop, pages , Inerlaken. Haralick, R.M., Saisical and srucural approaches o exure. Proceedings of he IEEE, 67, pp Hug, C., Exracing Arificial Surface Objecs from Airborne Laser Scanner Daa. In: Gruen, A., Balsavias, E. P., Henricsson, O. (Eds.), Auomaic Exracion of Man-Made Objecs from Aerial and Space Images (II), Birkhäuser Verlag, Basel, pp Hugo, C., Capao, L., Fernando, B. and Mario, C., Meris Based Land Cover Classificaion ih Self-Organizing Maps: preliminary resuls, EARSeL SIG Remoe Sensing of Land Use & Land Cover. Jen-Hon, L. and Din-Chang, T., Self-Organizing Feaure Map for muli-specral spo land cover classificaion. GIS developmen.ne, AARS, ACRS Kohonen, T., 19. The Self-Organizing Map. Proceedings of he IEEE, 78: LI Y. and WU H., Adapive Building Edge Deecion by Combining LiDAR Daa and Aeria Images. The Inernaional Archives of he Phoogrammery, Remoe Sensing and Spaial Informaion Sciences. Vol. XXXVII. Par B1. Being 2008 Maas, H. and Vosselman, G., To Algorihms for Exracing Building Model from Ra Laser Alimery Daa. ISPRS Journal of phoogrammery and remoe sensing, 54 (2-3): , Maikainen, L., Kaarinen, H. and Hyyppä, J., Classificaion Tree Based Building Deecion from Laser Scanner and Aerial Image Daa. ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007, Espoo, Sepember 12-14, 2007, Finland. Roenseiner, F., Summer, G., Trinder J., Clode S. and Kubik, K., Evaluaion of a Mehod for Fusing Lidar Daa and Muli-Specral Images for Building Deecion. In: Silla U, Roenseiner F, Hinz S (Eds) CMRT05. IAPRS, Vol. XXXVI, Par 3/W24 - Vienna, Ausria, Augus 29-30, Seo, K. and Liu, W., Comparing ARTMAP Neural Neork ih he Maximum-Likelihood Classifier for Deecing Urban Change. Phoogrammeric Engineering & Remoe Sensing, 69: Vesano, J., Himberg, J., Alhoniemi E. and Parhankangas J., SOM Toolbox for Malab 5. Technical Repor A57, Helsinki Universiy of Technology, Neural Neorks Research Cenre, Espoo, Finland. Vögle, T., Seinle, E., D Modelling of Buildings Using Laser Scanning and Specral Informaion. In: Inernaional Archives of Phoogrammery and Remoe Sensing, Amserdam, he Neherlands, Vol. XXXIII, Par B3, pp Whiman, D., K. Zhang, S.P. Leaherman, and W. Roberson, Airborne Laser Topographic Mapping: Applicaion o Hurricane Sorm Surge Hazards. Earh Sciences in he Ciies (G. Heiken, R. Fakundiny, and J. Suer, ediors), American Geophysical Union, Washingon DC, pp ACKNOWLEDGEMENTS The auhors ish o hank AAMHach for he Lidar daa and aerial imagery and he Deparmen of Lands, NSW, Ausralia for Bahurs daa ses. Also, M. Salah s ime in Ausralia funded by he Egypian Governmen is graefully acknoledged. 322