VOXEL BASED SEGMENTATION OF LARGE AIRBORNE TOPOBATHYMETRIC LIDAR DATA

Transcription

1 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XLII-1/W1, 2017 VOXEL BASED SEGMENTATION OF LARGE AIRBORNE TOPOBATHYMETRIC LIDAR DATA R. Boerner,, L. Hoegner, U. Stlla Photogrammetry and Remote Sensng, Techncal Unversty of Munch Munch, Germany - (rchard.boerner, ludwg.hoegner, stlla)@tum.de Commsson II, WG II/3 KEY WORDS: Pont cloud, large-scale dataset, Segmentaton, Classfcaton, Ground extracton, Voxel structure ABSTRACT: Pont cloud segmentaton and classfcaton s currently a research hghlght. Methods n ths feld create labelled data, where each pont has addtonal class nformaton. Current approaches are to generate a graph on the bass of all ponts n the pont cloud, calculate or learn descrptors and tran a matcher for the descrptor to the correspondng classes. Snce these approaches need to look on each pont n the pont cloud teratvely, they result n long calculaton tmes for large pont clouds. Therefore, large pont clouds need a generalzaton, to save computaton tme. One knd of generalzaton s to cluster the raw ponts nto a 3D grd structure, whch s represented by small volume unts (.e. voxels) used for further processng. Ths paper ntroduces a method to use such a voxel structure to cluster a large pont cloud nto ground and non-ground ponts. The proposed method for ground detecton frst marks ground voxels wth a regon growng approach. In a second step non ground voxels are searched and fltered n the ground segment to reduce effects of over-segmentatons. Ths flter uses the probablty that a voxel mostly consst of last pulses and a dscrete gradent n a local neghbourhood. The result s the ground label as a frst classfcaton result and connected segments of non-ground ponts. The test area of the rver Mangfall n Bavara, Germany, s used for the frst processng. 1. INTRODUCTION The European drectve forces the creaton of change maps of rver areas (European Unon, 2000, 2007). To create these maps, the cheapest and fastest way s to use arborne LDAR wth a green laser. Ths technque measures a set of ponts mappng the water surface, rver ground and the rver waterfront. Such a set of ponts s called topobathymetrc pont cloud. Creatng change maps of rver areas needs a change detecton of topobathymetrc pont clouds whch determnes mssng or new areas between two tmes (e.g. caused by depostons or drfts of the rver ground). To mplement such a change detecton on the bass of topobathymetrc pont clouds, the frst step s a segmentaton and classfcaton. Class labels lke buldng, vegetaton, water, wet ground and dry ground wll be used for the change detecton task by defnng thresholds for a change sgnfcance. For example, the ground under the water surface (. e. wet ground) changes wth a hgher probablty as the dry ground, because of the rver s morphodynamc. Furthermore, the most mportant classes for the analyss of topobathymetrc pont clouds are water and ground. The water class must be known to correct the nfluence of dfferent lght refracton characterstcs of water and ar (Mandlburger et al., 2015). A frst classfcaton s acheved by a segmentaton of the pont cloud nto ground and non-ground ponts. Such ground detecton approaches use a regon growng whch s computatonal expensve n the case of a huge number of ponts n the scanned data. To save computaton tme, t s necessary to generalze the pont cloud whch can be done wth a structure of volume clusters. These so called voxels generalze the pont cloud by summarzng ponts, whch are nsde the same volume part. The proposed segmentaton uses a regon growng approach on the bass of a Correspondng author voxel structure. Ths method s able to process large-scale arborne LDAR data wth the am of creatng segments lke ground and non ground. The focus of the approach s a fast computaton even f the pont cloud data becomes large. The voxel structure, used for ths approach, s generated wth an octree and the tme consumed for the computaton depends only on the sze of the ntal boundng box. Therefore the computaton cost s almost ndependent of the number of ponts n the pont cloud. Ths paper s structured as follows: Secton 2 wll sparsely revew some state of the art approaches of the topc pont cloud segmentaton and classfcaton. Secton 3 wll ntroduces the proposed method and explan how to use t for ground detecton and segmentaton tasks. The used data wll be llustrated and characterzed n secton 4. Afterwards, secton 5 shows frst results of the proposed approach and the used data, whch s ntroduced before. At the end, secton 6 gves a short summary. 2. RELATED WORK The classfcaton task can be dvded nto two major parts: the segmentaton and the use of these segments for a matchng of class labels. The am of the segmentaton s to create regons of connected ponts. Most state of the art approaches are pont based approaches. That means each pont of the pont cloud needs to be processed. Such pont based segmentaton approaches can be fnd n Rutznger et al. (2008); Shapovalov et al. (2010); Zhang et al. (2016). Rutznger et al. (2008) use a regon growng approach based on the echo wdth of the pulses nsde the laser beam. They have the am to classfy the pont cloud nto vegetaton and non-vegetaton ponts. Therefore, they use the property of vegetaton to create a hgh echo wdth. do: /sprs-archves-xlii-1-w

2 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XLII-1/W1, 2017 Zhang et al. (2016) buld an undrected graph of each non terran pont to ts k-nearest neghbours. To create segments on the bass of ths graph, they determne the local maxma of the ponts heght value and perform a graph cut at ths local maxma. Ths graph cut clusters the whole graph nto smaller parts, whch are normalzed by a predefned threshold for the number of ponts of each segment. Shapovalov et al. (2010) use a machne learnng approach to get the class labels for each pont. A Markov random feld s used to buld a graph on the base of all ponts. The connecton of nodes n the graph s determned by unary and parwse potentals. The unary potental can be seen as the descrptor for a sngle pont and the parwse potental as the descrptor for an edge n the graph. Shapovalov et al. (2010) use for the unary potental spectral and drectonal features, spn mages, angular spn mages and dstrbutons of heghts. Ther parwse potental depends on the angle between normals, the dfference n alttudes and the dfference n postons of two neghboured ponts. The unary potental s computed wth a random forest classfer and the parwse potental wth the Naïve Bayes classfer. The fnal class label for each pont s dentfed wth the Markov random feld nference A smlar approach, adoptng condtonal random felds for pont cloud classfcaton, s propsed by Nemeyer et al. (2014). They obtan the unary and parwse potental wth lnear models traned wth the random forest algorthm. These models use a long lst of attrbutes lke ntensty, echo numbers, heght of a sngle pont above DTM, resduals from a local plane fttng, and others. Zhang et al. (2016) tran SCLDA-based features for ther pont wse descrptors and match these descrptors to a class label wth the AdaBoost classfer. Retberger et al. (2009) show how to segment pont clouds nto tree crown and tree stem segments by usng a graph cut approach. Prevous work for segmentaton uses a modellng of the geometry sampled wth a voxel structure (Xu et al., 2017). Ths paper ntroduces a graph based segmentaton of a local fully connected affnty graph. Instead of a par of voxels the used affnty graph consders all voxels n the local neghourhood. Insde ths graph, voxels are connected n dependence on ther spatal dstance, shape smlarty and surface connectvty correspondng to smoothness and convexty crterons. Ths method shows sutable results for segmentaton of buldng elements by recognzng geometrc prmtves. Furthermore, a supervoxel approach for segmentaton of outdoor scenes s shown n Xu et al. (2016). Ths uses a connecton judgement dependent on egenvalue based geometrc features. Ths features are used to calculate smlartes between two voxels. Ths approach also creates good results for clusterng pont clouds nto geometrc prmtves, but also shows dffcultes n case of vegetaton. Unfortunately, large scale arborne LIDAR data s drawn by few geometrc elements (.e. only buldngs and flat ground parts can be modelled wth geometrc prmtves). Therefore, further development s needed for dealng wth non urban areas. Ajaz et al. (2013) combne voxels to one segment f the voxels are close n poston and ther ntensty- or colour dfferences are lower than a predefned threshold. To match the voxel segments to a class label, they use the surface normal, the heght dfference between geometrcal - and barycentre, geometrcal shape, color, and ntensty. The proposed approach n ths paper s based on ths voxel segmentaton used by Ajaz et al. (2013) but uses another ground detecton approach whch s more sutable for large scale areas and non flat terran. 3. METHOD The proposed method for pont cloud segmentaton depends on a voxelsaton of the pont cloud. These volume clusters (.e. voxels) are needed to generalze the nput data and therefore reduce processng tme. The generalsaton clusters the whole volume of the pont cloud nto smaller parts, whch nclude several ponts. Therefore, the algorthm only needs to process the volume parts, whose number s lower than the number of ponts. The am of the proposed method s to segment the data nto ground and non-ground areas. For non-flat terrans some condtons are defned and flters are used to create the ground segment. These condtons and flters are explaned n secton 3.1. The used segmentaton approach for the non-ground voxels wll be ntroduced n secton Ground Extracton For pont cloud segmentaton the ground can be used as a separator between the segments (Ajaz et al., 2013). Especally n the case of arborne data, the ground needs to be extracted to get non connected segments. One method for ground extracton s to use a condton lke the ground s flat. Wth ths condton, heght hstograms are analysed to detect a threshold and the ponts under ths threshold are marked as ground ponts (Hebel and Stlla, 2009). Ths leads to problems n hlly terrans, where the ground can be hgher than some buldngs. An alternatve soluton s to use a regon growng algorthm wth the heght dfference as a smlarty crteron (Hebel and Stlla, 2012). Such a pont wse regon growng s of low effcency f the pont cloud data grows. For the proposed ground extracton, both methods are combned and adopted to the voxel space. A schematc summary of the ground detecton s shown n Fgure 1 whch wll be explaned n the followng. An Octree s used to create the voxel structure. Furthermore, a spatal ID s used to defne neghbourng voxels and to search n an ordered lst for a voxel of gven coordnates. The man attrbute of ground voxels s, that they are the lowest ones n a local neghbourhood. Therefore, the man dea for a fast voxel based ground detecton s to search for the voxel to each x-y- coordnate wth the lowest z value. By the use of the octree, ths means that wthn an teraton over all x and y coordnates the ground voxel s found as the frst exstng voxel nsde the teraton from z = 0 to z = 2 l. A voxel can be seen as a bloated pont or as a pont group. Therefore, markng only the lowest voxels s lke creatng samplng ponts n a contnuous ground functon. Fgure 2a llustrates ths nterpretaton as a samplng functon. All voxels have the same sze and nclude dfferent szed areas dependng on the voxel samplng. By checkng only the lowest voxels, the contnuous ground functon s transformed to a step functon n the voxel space. Ths could lead to holes n the ground layer (Fgure 2a). To avod these holes, addtonal voxels are added to the ground f they fulfl the followng three condtons: frst: they are occuped; second: there s at least one neghbour wth hgher or equal z-value marked as ground (see Fgure 2b); For the thrd condton, the boundng box over the ponts nsde a voxel (sub boundng box) s used. If the sub boundng boxes of do: /sprs-archves-xlii-1-w

3 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XLII-1/W1, 2017 (a) (b) (c) Fgure 2. The three steps of the ground detecton. (a) frst step: The lowest exstng voxels to each x-y-coordnate (blue ones) are marked as ground voxels. Ths can be nterpreted as creatng samplng ponts of a contnuous ground functon (black graph). The blue arrows show the search drecton along the z axs and dotted voxels are non exstng voxels. (b) second step: addng voxels where at least one neghbour s marked as ground and have hgher or equal z-values. (c) thrd step: Look f there are sub boundng boxes (black) of the ground voxels whch don t touch each other (green arrows). In ths case, the next voxles along z are added to the ground (marked n green). Fgure 1. Schematc workflow of the ground detecton. Recangles show processng steps and ellpses the output of the processng step. The gradnt calculaton s dvded nto ts sub processng steps whch are shown n the blue box. two neghbourng ground voxels wth same heght do not touch each over (ther dstance s smaller than a threshold), the next voxels along z are added, as well (see Fgure 2c). The proposed approach can be regarded as a voxel based regon growng where step one (Fgure 2a) detects seed voxels. Afterwards, the regon growng s performed between these seeds by lookng at the z coordnates of the voxel and determnng voxels to be added to the ground segment (Fgures 2b and 2c). Roofs and trees create scan shadows nsde the ground layer whch enables the algorthm to select roof or tree voxels as seeds. Therefore, the ground s over segmented by ncludng roof and tree voxels. Voxels contanng tree ponts can be selected from ths over segmented ground by checkng the pulse type of all ponts (p ) n each voxel. Through the number of returns and the return number, t s possble to determne pulse classes, ncludng frst, last and mddle pulses (Rutznger et al., 2008). Ths s adopted to the pont set nsde a sngle voxel. The probablty of a voxel to be a voxel consstng of last pulses (to be a ground voxel) s calculated by: where rn p prob last = (1) nor p prob last = probablty of last pulse wth prop last [0, 1] nor p = number of returns of the beam to pont = return number of pont rn p Last pulses are the ones, where the return number s equal to the number of returns (Rutznger et al., 2008). Therefore, ths probablty gets a value of 1.0 for a voxel ncludng only last pulses. The probablty s smaller than 1.0 f there are other pulse classes nsde the voxel. Ths behavour s used to defne a threshold whch determnes voxels ncludng mostly last pulses. Voxels whch do not nclude mostly last pulses are marked as non-ground voxels. Voxels whch nclude roof ponts fulfl ths condton, too. They need another flter method. Ths method depends on the assump- do: /sprs-archves-xlii-1-w

4 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XLII-1/W1, 2017 ton that roofs fly over the ground of ther local area. The attrbute fly symbolzes that there are non exstng voxels between the roof and the ground. To search for these roof voxels, a gradent s calculated based on the dscrete samplng of the voxels. Ths gradent s defned by the bggest dfference n heght from the current voxel to the closets neghbourng voxels. The closest neghbourng voxels are searched n the lst of ground voxels to gven x- y-coordnates. A horzontal threshold s used to gnore neghbours nsde ths lst, whch are to far away from the current voxel. If the ground s flat n the current neghbourhood, the gradent s close to zero, because there s no dfference n the heght coordnates. In case that the current voxel s a roof voxel, the local gradent detects a sgnfcant dfference of the z-coordnates (Fgure 4). The gradent calculaton s based on the center ponts of the sub boundng boxes of neghbourng voxels. These sub boundng boxes are further used to detect facades, whch need to be gnored for the gradent calculaton. Facades of buldngs are detected by lookng at the center ponts of the neghbourng voxels sub boundng boxes. If the x-y-coordnates of a center pont of a vertcal neghbour s nsde the horzontal footprnt of the current sub boundng box, ths neghbour s marked as facade and therefore not consdered n the gradent calculaton. Ths facade detecton s shown n Fgure 3. Fgure 4 shows ths gradent flter n profle on an example of a roof. Fgure 4. Profle of the gradent calculaton, green lne: ground, red lne: buldng. Blue boxes shows the voxel from the octree and black boxes the sub boundng boxes. The horzontal dstance defnes usable neghbours and the vertcal dstance defnes the gradent value. these pulse classes can be calculated wth: n prob sngle = (2) nor p n prob frst = (3) rn p prob last = (4) nor p rn p Fgure 3. Handlng of facade elemets, green lne: ground, red lne: buldng, green pont: center of the sub boundng box. In case of a neghbour n the vertcal drecton whch ncludes a facade, the sub boundng box s center s horzontal nsde the current sub boundng box. Such facade ncludng neghbours are excluded of the lst of neghbours. 3.2 Segmentaton The result of the ground detecton are two segments: the ground segment and the non-ground segment. The next step s to cluster the non-ground segment nto smaller parts. The result of ths segmentaton should be sutable for a further classfcaton task. Therefore, voxels belongng to one segment should have same attrbutes. For such segmentaton, a regon growng approach dependng on ntensty dfferences as proposed by Ajaz et al. (2013). Addtonally the pulse class of the voxel s used. Wth n = number of ponts, prob sngle = probablty of sngle pulse, prob frst = probablty of frst pulse and prob last = probablty of last pulse, These probabltes are wthn the range of [0,1]. If the voxel mostly consst of ponts wth the searched pulse class, the probablty s close to 1. A segmentaton based on the pulse class can be created by puttng neghbourng voxels together whch have the same pulse class. A sngle voxel s matched to the pulse class where the probablty s above a threshold. The same threshold s used for all probabltes and f all of these probabltes are below ths threshold, the voxel consst of mddle pulses. 4. DATA The proposed method s tested on a dataset of the Mangfall area n Bavra, Germany (Fgure 5). Ths area was scanned wth a VQ 820G scanner and reaches from the Tegernsee to the hghway A8. Approxmately 200 Mo ponts were scanned wthn three flght strps of approxmately 500 m wdth and 17 km length. A rotatng mult facet mrror s used as the scan mechansm whch ensures an ncdence angle of 20 wth respect to the flght drecton and an accuracy of the ncdence angle up to 1. The beam dvergence s approxmately 1 mrad whch creates a 50 cm footprnt by a flght heght of 500 m (Stenbacher et al., 2012). The scanner recognses a maxmum of 4 pulses nsde the same laser beam. Furthermore, the scanner uses a wavelength of about 532 nm. Therefore, the proposed data are topobathymetrc data. Insde a sngle flght strp a pont resoluton of 4 dm on the ground was obtaned and the laser beam penetrates the water up to a depth of 5 m. do: /sprs-archves-xlii-1-w

5 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XLII-1/W1, 2017 n the returnng sgnal, the GPS tme of the returnng sgnal, the scan angle rank of the laser beam and two flags whch ndcates the flght strp edges and the scan drecton. 5. RESULTS Fgure 5. The scanned regon (Photos from Google maps). In the Background: whole Germany and parts of Europe, n the zoomed part (red rectangle): the scanned Mangfall area (marked n green) from the Tegernsee n the south to the hghway n the north. Frst algorthmc tests are performed on the Tegernsee part of the proposed data (secton 4). Ths part s shown n Fgure 6 and conssts of more than 4 mo ponts. The area has a length of 1.4 km and a wdth of 400 m. A ground truth (GT) was manually labelled and used for the evaluaton. Fgure 7 shows the raw data and the ground ponts nsde the ground truth. (a) (b) (a) Fgure 7. Manually labelled Ground Truth. (a): complete pont cloud, (b): ground layer. For the evaluaton of the runtme, dfferent voxel szes are used. Varyng voxel szes result n dfferent maxmum levels of the used octree (Table 1). To reduce the processng tme, the whole data set s dvded nto sx smaller parts of equal horzontal dmensons of 500 m by 400 m. These parts are processed n parallel. The resultng accuraces are shown n Fgure 8. The runtme evaluaton uses the followng parameters: Heght threshold whch determnes the voxels to delete n the gradent chan (Tz ): 6 m (b) Fgure 6. Regon of the Tegernsee. (a): Google Maps Screenshot, (b): Scanned data wth the ponts ntensty as grey value Besde the ntensty (Fgure 6) other recognsed pont attrbutes are the return number of a sngle pulse, the number of returns Horzontal threshold, whch determnes the neghbours to use(th ): 0.5 vdag, where vdag s the length of the voxel s horzontal dagonal. Th s further addressed as the factor n front of vdag. Number of teratons for the gradent chan: 30 m / vdag. Ths choce results n a length of 40 m for the gradent chan. Threshold whch determnes small gradents: 0.5 m; do: /sprs-archves-xlii-1-w

6 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XLII-1/W1, 2017 maxmum Voxel sze vx = 20 m vy = 20 m vz = 2m vx = 10 m vy = 10 m vz = 1m vx = 5 m vy = 5 m vz = 1m vx = 3 m vy = 3 m vz = 1m maxmum octree level (sngle thread) total processng tme s s s s Table 1. Computaton tme, evaluated on an Intel Core HQ CPU Fgure 11 shows a comparson of the nfluence of two major parameters Tz and Th of the used flter. The used maxmum voxel szes are 10 m x 10 m x 1 m and 3 m x 3 m x 1 m. The functon graphs of these two maxmum voxel szes look smlar whch means, that the nfluence of Tz and Th behaves the same. The stronger generalsaton by larger voxels and the resultng elmnaton of more ponts of the ground layer s shown by the offset and bgger dfferences between the two senstvty graphs. In general t can be sad, that a lower Tz wth hgher Th results n a hgher senstvty of the non-ground segment. Ths confguraton results n a stronger flter and therefore n more detected outlers. The lower senstvty of the ground layer can be explaned by a stronger flter, too. Snce steep slopes and roofs are very smlar n the scanner data (both have bg heght dfferences n a small area), a stronger flter wll delete the ponts of steep slopes as well as roof ponts. Therefore, the senstvtes are correlated n a negatve manner. If the senstvty of the non-ground layer gets bgger, the senstvty of the ground layer gets lower. The nfluence of Th depends on the value of Tz. If Tz s selected to be lower, Th changes the senstvtes wth a stronger nfluence. A vsual result of the proposed method s shown n Fgure 9. Ths fgure shows the area of the northern brdge across the rver (see Fgure 7) wth a vew drecton from north to south. The used maxmum voxel dmenson s 10 m x 10 m x 1 m and the used flter parameters are: Tz = 6 m, Th = 0.5. Fgure 8. Senstvty, dependent on the runtme. Dfferent Voxel szes are symbolzed by dfferent run tmes. Fgure 11 shows that a maxmum voxel sze of 10 m x 10 m x 1 m creates the best senstvtes. If the voxel sze s selected as a smaller or larger one, the senstvty becomes lower. Ths has dfferent reasons: In the case of larger voxels, the pont cloud s more generalzed. Therefore, the selecton of a voxel to be a non-ground voxel wll excse more ponts of the ground layer. Ths s shown by a strong fall of the functon for the detected ground ponts. In contrast to larger voxels, smaller voxels preserve more detals n the pont cloud generalsaton. In the case of smaller voxels, the scan shadows of buldngs nsde the ground layer are preserved as well and more seed voxels are selected on roofs. Due to more detals, t s possble that the gradent gets wth small steps to the ground and therefore preserve these wrongly detected ground voxels. In other words, the wrongly detected ground s not detected non-ground, whch explans the decrease of the non-ground senstvty. Especally the z-dmenson becomes very small wth hgher octree levels, because the ntal boundng box has a smaller z-dmenson than x- or y- dmenson. Voxels wth very small z-dmensons create a sparse heght samplng of the ground layer and therefore create unoccuped voxels between two ground voxels. The regon growng s aborted at these unoccuped voxels and the samplng of the ground layer s less complete, too. Snce fgure 11 shows the senstvtes of both classes, the accuraces can be estmated as well. The absolute number of ground ponts n the GT-data s: and the absolute number of non-ground ponts: By usng ths values to calculate the accuraces of the two classes, t turns out, that the accuracy of the one class s close to the senstvty of the other class. Therefore, the followng evaluaton of the flter method uses only the senstvty values. Fgure 9. Results of the ground detecton. Top: raw data, mddle: result of our approach, bottom: ground truth Ths area was selected, because t shows some remanng roof ponts n the ground layer. The area ncludes steep slopes below vegetaton as well as water and rver bed ponts. Both confguratons can be handled wth the proposed method as t can be seen n the fgure. Fgure 10 shows vsually results of the segmentaton of the nonground segment. Due to mssng ground truth data, t s not possble to do a numercal evaluaton lke for the ground detecton. However, the results show a dvson between the classes vegetaton, water, and buldng whch s a sutable result for a future classfcaton task. do: /sprs-archves-xlii-1-w

7 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XLII-1/W1, 2017 REFERENCES Fgure 10. Result of the segmentaton of the non-ground segment. The maxmum Voxel dmenson s x,y < 20m, z < 2m. The segments n the pont clouds are colour coded: whte: ground, others: randomly dfferent color. 6. SUMMARY Ths paper ntroduces a geometrc segmentaton approach for arborne mult-response LIDAR data. Snce such arborne data can become very large (a large amount of ponts s scanned), a voxel structure s used to generalze the data and therefore reduce processng tme. Ths voxel structure s used for a ground segmentaton. The proposed ground detecton s able to detect the ground of a hlly terran as well as the ground below vegetaton and water surfaces. By usng the voxel generalzaton, the ground detecton s almost ndependent of the number of ponts nsde the pont cloud. The expermented results revealed that the performance and processng tme depend on the used voxel sze. Some over and under segmentatons of the ground stll exst. Under segmentatons are characterzed by small non-ground areas surrounded by ground ponts. Over segmentatons are characterzed by ground areas surrounded by roof ponts. Ths phenomenon wll be used to detect and correct the over and under segmentatons n the future by checkng the class labels of the local neghbourhood. The segmentaton results of non-ground areas (Fgure 10) are created by a segmentaton dependent on the voxel pulse classes: sngle, frst, mddle or last pulse. Ths segmentaton shows a cut of vegetaton areas nto tree crown top ponts and other tree ponts. A combnaton of pulse class and ntensty dfference should be consdered for the segmentaton n future work. Furthermore, the segmentaton of non-ground areas wll be used for a classfcaton task, whch matches each segment to the class labels: buldng, vegetaton and water. ACKNOWLEDGEMENTS Ths research s funded by Bayersche Forschungsstrftung, project Schrtthaltende 3D-Rekonstrukton und -Analyse (AZ ), sub project Änderungsdetekton n Punktwolken. The dataset s provded by our project partner StenbacherConsult. Ajaz, A. K., Checchn, P. and Trassoudane, L., Segmentaton based classfcaton of 3D urban pont clouds: A supervoxel based approach wth evaluaton. Remote Sensng 5(4), pp European Unon, Drectve 2000/60/EC of the European Parlament and of the Councl of 23 October 2000 establshng a framework for Communty acton n the feld of water polcy. Nov. 2016). European Unon, Drectve 2007/60/EC of the European Parlament and of the Councl of 23 October 2007 on the assessment and management of flood rsks. Nov. 2016). Hebel, M. and Stlla, U., Automatsche Koregstrerung von ALS-Daten aus mehreren Schräganschten städtscher Quartere. Photogrammetre Fernerkundung Geonformaton 3, pp Hebel, M. and Stlla, U., Smultaneous calbraton of ALS systems and algnment of multvew LDAR scans of urban areas. IEEE Transactons on Geoscence and Remote Sensng 5(6), pp Mandlburger, G., Hauer, C., Weser, M. and Pfefer, N., Topo-bathymetrc LDAR for montorng rver morphodynamcs and nstream habtats - a case study at the pelach rver. Remote Sensng 7(5), pp Nemeyer, J., Rottenstener, F. and Soergel, U., Contextual classfcaton of ldar data and buldng object detecton n urban areas. ISPRS journal of photogrammetry and remote sensng 87, pp Retberger, J., Schnörr, J., Krzystek, P. and Stlla, U., D segmentaton of sngle trees explotng full waveform ldar data. ISPRS Journal of Photogrammetry and Remote Sensng 64(6), pp Rutznger, M., Höfle, B., Hollaus, B. and Pfefer, N., Object-based pont cloud analyss of full-waveform arborne laser scannng data for urban vegetaton classfcaton. Sensors 8(8), pp Shapovalov, R., Velzhev, A. and Barnova, O., Nonassocatve markov networks for 3D pont cloud classfcaton. The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Stenbacher, F., Pfenngbauer, M., Aufleger, M. and Ullrch, A., Hgh resoluton arborne shallow water mappng. In: Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Proceedngs of the XXII ISPRS Congress, Vol. 39, p. B1. Xu, Y., Tuttas, S. and Stlla, U., Segmentaton of 3d outdoor scenes usng herarchcal clusterng structure and perceptual groupng laws. In: th IAPR Workshop on Pattern Recognton n Remote Sensng (PRRS), pp Xu, Y., Tuttas, S., Hoegner, L. and Stlla, U., Geometrc prmtve extracton from pont clouds of constructon stes usng vgs. IEEE Geoscence and Remote Sensng Letters 14(3), pp Zhang, Z., Zhang, L., Tong, X., Mathopoulos, P. T., Guo, B., Huang, X., Wang, Z. and Wang, Y., A multlevel pontcluster-based dscrmnatve feature for ALS pont cloud classfcaton. IEEE Transactons on Geoscence and Remote Sensng 54(6), pp do: /sprs-archves-xlii-1-w

8 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume XLII-1/W1, 2017 maxmum Voxel dmenson: 3 m x 3 m x 1 m maxmum Voxel dmenson: 10 m x 10 m x 1 m Fgure 11. Influence of the parameters Th and Tz to the ground detecton. The two columns show dfferent maxmum Voxel dmensons (see header) do: /sprs-archves-xlii-1-w