Automated Extractio of Urba Trees from Mobile LiDAR Poit Clouds Fa W. a, Cheglu W. a*, ad Joatha L. ab a Fujia Key Laboratory of Sesig ad Computig for Smart City ad the School of Iformatio Sciece ad Egieerig, Xiame Uiversity, Xiame, Chia b Seior Member, IEEE, Dept. of Geography ad Evirometal Maagemet, Uiversity of Waterloo, Waterloo, ON N2L 3G1, Caada ABSTRACT This paper presets a automatic algorithm to localize ad extract urba trees from mobile LiDAR poit clouds. First, i order to reduce the umber of poits to be processed, the groud poits are filtered out from the raw poit clouds, ad the u-groud poits are segmeted ito supervoxels. The, a ovel localizatio method is proposed to locate the urba trees accurately. Next, a segmetatio method by localizatio is proposed to achieve objects. Fially, the features of objects are extracted, ad the feature vectors are classified by radom forests traied o maually labeled objects. The proposed method has bee tested o a poit cloud dataset. The results prove that our algorithm efficietly extracts the urba trees. Keywords: Poit clouds, localizatio, segmetatio, classificatio, urba trees 1. INTRODUCTION Urba trees are a importat part of a city. They brig beefits i five aspects: social beefits, architectural beefits, climatic beefits, ecological beefits, ad ecoomic beefits. They improve home ad work eviromet, add beauty for architectural buildig, brig more pleasure to the eviromet, offer biotopes for flora ad faua, ad boost the developmet of ecoomy i a city. 1, 2 Thus, the moitorig of urba trees is very essetial. Traditioal way for moitorig urba trees is depedet o a ispectio method, which cosumes much maual labor work ad time. I recet years, some automated methods based o mobile LiDAR systems have bee proposed ad they are based o the Euclidea clusterig, 3, 4, 5 supervoxels clusterig, 6 ad the graph clusterig, 7. 8 I, 3 raw poit clouds were partitioed alog road directios as sub-regios called road parts. The the segmeted poits were roughly labeled ito groud, o-groud ad off-groud by a surface growig algorithm. 9 Next, a coected compoet aalysis was used to obtai the uique IDs. Fially, prior kowledge was employed to classify them. Rutziger et al. 4 first removed plaar ad large regios with the method i. 9 Next, Rutziger et al. clustered objects by a coected compoets techology. Yu et al. 5 first used Euclidea clusterig method to obtai objects, ad employed the pair wise 3D shape cotext 10 to describe detected feature poits. Next, the template matchig was employed to fid urba trees. Pu et al. used Euclidea clusterig method ad prior kowledge to extract urba trees. I, 6 poit clouds were first partitioed ito multi-scale supervoxels accordig to the attributes of poits ad a spatial distace betwee poits. Next, Yag et al. obtaied the geometric structure iformatio of each supervoxel by Priciple Coectivity Aalysis (PCA). The the predefied rules are applied to merge adjacet supervoxels to become a complete object. Fially, sematic kowledge was employed to classify the objects. Livy et al. 7 first removed the groud poits by projectio desity of raw poit clouds, ad the remaiig poits were clustered by the graph clusterig. Goloviskiy et al. localized objects by a techology based o a graph; the, the mi cut segmetatio 11 was employed to obtai the objects. Fially, the classifiers traied o maually labeled objects were used to classify the objects. Further author iformatio: (Sed correspodece to Cheglu We) Cheglu We: E-mail: clwe@xmu.edu.c 2d ISPRS Iteratioal Coferece o Computer Visio i Remote Sesig (CVRS 2015), edited by Cheg Wag, Rogrog Ji, Cheglu We, Proc. of SPIE Vol. 9901, 99010P 2016 SPIE CCC code: 0277-786X/16/$18 doi: 10.1117/12.2234795 Proc. of SPIE Vol. 9901 99010P-1
However, the processig uit of above most algorithms is a poit. As a result, the time complexity of these algorithms is too high. O the other had, most popular segmetatio method, such as Euclidea clusterig, is ot robust to a occluded eviromet. Thus, the algorithms based o the segmetatio method is limited. To solve the two problems, we develop a efficiet algorithm to extract urba trees. First the processig uit of poit clouds becomes a supervoxel rather tha a poit ad it extremely accelerates process of extractio of urba trees. Secod our proposed segmetatio method is depedet o the localized positios ad robust for the complex eviromet. Therefore, the classificatio of urba trees based o our segmetatio method ca obtai satisfyig results. Our algorithm ca be decomposed ito five steps: (1) Removig poits close to the groud by iterative plae; (2) Segmetig u-groud poits ito supervoxels by the Voxel Cloud Coectivity Segmetatio method; (3) Localizig the urba trees; (4) Segmetig by localizatio (5) Fially, the radom forests traied by maually labeled objects is used to classify objects. 2.1 Preprocessig 2.1.1 Groud removal 2. METHOD To reduce the umber of poits to be processed, groud poits should be filtered out from raw poit clouds, the the Radom sample cosesus (RANSAC) algorithm 12 that is modified by us is employed to remove groud poits. For each RANSAC iteratio, the poits ear the fittig plae are classified ito groud poits. The groud height h groud is obtaied i the first RANSAC algorithm. The iteratio cotiues util oe of the poit ear the fittig plae is higher tha the h groud + 1. Figure. 1 shows the result of groud removal. \\\4.merec 4011001. (a) 4,.. (b) Figure 1. results of filterig groud. (a) raw poit clouds. (b) filterig result 2.1.2 Geeratig supervoxels of o-groud poits Whe the groud poits are separated from the raw poit clouds, the u-groud poits are segmeted ito supervoxels through the Voxel Cloud Coectivity Segmetatio method 13 ad the result ca be show i Figure. 2. Proc. of SPIE Vol. 9901 99010P-2
Figure 2. Supervoxel examples of a poit cloud After the u-groud poits are segmeted ito supervoxels, the poits i the supervoxel are projected oto the XY plae. The, the algorithm i 14 is used to obtai the covex hull for the projected poits. After the covex hull is achieved, the directed area of triagular is employed to obtai the area of the polygo of the covex hull. 2.2 Localizatio The raw poit clouds are partitioed ito cells alog the x ad y coordiate, ad the maximum z coordiate of the poits i each cell ca be obtaied. Oly the cells whose maximum z coordiate is i a pre-defied rage (h low, h high ) are retaied. For each poit p(p x, p y, p z ) i the cell c, s(p z ) is acquired by (1). The fuctio s(x) is defied as follows: s(x) = 1 exp( (x htree 2 ) + 1) where h tree is the estimated height of urba trees. Let P (c) calculate the sum of s(p z ) of the poit p i the cell, c. Next the maximum of P (c) ca be obtai ad it is γ. Afterwards the localizatio image Img ca be achieved as followig: P ixel(i, j) = P (c) 255 (2) γ i ad j is the idex of cell c. The a threshold gray value is set to obtai the coected areas. The positio of the maximum gray value of a coected area is take as the positio of a object. 2.3 Segmetatio Before the segmetatio starts, it is ecessary to calculate the highest poit, lowest poit, boudig box, ad baryceter of a supervoxel. After the u-groud poits are segmeted ito supervoxels, the plae coordiate of the gravity ceter, p g (x, y), for each supervoxel is calculated as follows: (1) i=1 x = x p i i=1 y = y p i (3) Proc. of SPIE Vol. 9901 99010P-3
where p i (x pi, y pi, z pi ) belogs to the supervoxel, β; is the umber of poits i the supervoxel, β. There are three steps i our segmetatio method. The order of mergig supervoxels i the three steps decreases accordig to the correspodig pixel value of Img for the detected positios. Step 1. The first step is to obtai tree truks. It is believed that if the plae coordiate of the gravity ceter of a supervoxel is ear a detected positio ad if more poits i the supervoxel is ear a detected positio, this supervoxel would be more likely to belog to the detected positio. Ad this is the thought of the first step. The fuctio dis(a, b) measures the plae distace betwee poit a ad poit b ad is defied as follows: dis(a, b) = (a x b x ) 2 + (a y b y ) 2 (4) Firstly it is eed to obtai the uclassified supervoxel, P patch, whose plae distace betwee the gravity ceter, p g (x, y) of the supervoxel, P patch ad the plae coordiate of a detected positio, p pole is less tha a predefied value, d g. Afterwards, the ratio φ for P patch ca be obtaied. The ratio, φ, is defied as follows: P i = {p dis(p, p pole ) < d i, p P patch } φ = Cout(P i) where d i is less tha d g. Cout(x) measures the umber of elemets i the set, x; is the umber of the poits i P patch. If the value of φ is greater tha λ, the supervoxel, P patch, would temporiably belog to the tree truk object at the detected positio, p pole. If the umber of supervoxels, like P patch, belogig to the object is less tha N ow, these supervoxels.become uclassified supervoxels agai. Whe the pole is separated from the scee, the umber of the poits i the pole ad the height of the tree truk ca be achieved. If the umber of the poits i the pole is less tha a pre-defied value or the height of the pole is less tha a pre-defied value, the pole is ot take as the tree truk. The highest poit of the tree truk is P summit. Step 2. The secod step is to filter out the vegetatio ear the tree truk. First the supervoxels whose plae ceter is i the pre-defied plae rage at each positio of the detected tree truk are foud. Next, the supervoxels whose highest poit is lower tha a pre-defied value are removed. Step 3. The third step is to achieve the complete tree. This step is motivated by the breadth-first search algorithm. The supervoxels ca expad towards up ad dow. The patch with the peak poit, P peak (x peak, y peak, z peak ), is take as the seed patch, ad added ito the queue. Next, the top elemet of the queue is take as the growig patch, ad the top elemet is removed from the queue. Sequetially, the eighborig patches whose baryceter is achieved i the r grow meter rage at the growig patch ceter. If the maximum i distaces from the eight plae projectio corers of the boudig box of the eighbor patch to the p pole is less tha r safe, the the eighborig patch is added to the queue. After all the eighborig patches have bee judged, the first elemet of the queue becomes the ew growig patch. This process cotiues util the queue is empty. 2.4 Feature Extractio After the segmetatio process termiates, the features of the object are calculated. There are two kids of features: pole features ad whole object features. Tree truk features. Whe the first step of segmetatio eds, the followig ie features that describe the pole are computed: (1) the height; (2) the average height; (3) the stadard deviatio i height; (4) the average area of the covex hull of supervoxel s plae projectio poits; (5) the stadard deviatio i the area of the covex hull of supervoxel s plae projectio poits; (6) the area of the covex hull of the whole object s plae projectio poits; (7) the estimated volume; (8) the umber of pole poits; ad (9) the umber of the supervoxel whose area of the covex hull of the supervoxel s plae projectio poits is greater tha the stadard area of the covex hull of supervoxel s plae projectio poits of a urba tree, s base. Whole tree features. After the secod step of segmetatio is completed, the followig te features that describe the whole object are computed: (1) the height; (2) the average height; (3) the stadard deviatio i height; (4) the pixel value i the correspodig locatio image positio; (5) the projectio covex hull area; (6) the estimated volume; (7) the height differece of the baryceter ad geometry ceter; (8) the umber of poits; (9) the umber of eighborig patches i oe meter rage at the peak positio of the first segmeted pole. (5) Proc. of SPIE Vol. 9901 99010P-4
2.5 Classificatio I the fial stage, radom forests traied o maually labeled objects are employed to classify the objects. 3. RESULTS AND DISCUSSION The poit clouds were acquired by a RIEGL VMX-450 system. They were acquired o the Rig Road South i Xiame, Chia. This is a typical urba scee ad may trees are beside street light poles, which causes that the extractio of trees is difficult. First, the groud poits were removed from the raw poit clouds by multiple RANSAC methods. Next, the o-groud poits are segmeted ito supervoxels. Subsequetly, a ovel localizatio method was proposed to localize urba trees with h low = 2.0m, h high = 8.0m, ad h tree = 8.0m. Next, a segmetatio by localizatio was proposed to obtai the urba trees with d g = 1.5m, d i = 0.5m, λ = 0.5, r grow = 1.1m, ad r safe = 2.4m. The localizatio ad extractio results are show i Figure. 3. Fially, the radom forests traied o maually labeled objects were used to classify objects. The results of extractig urba trees are show i Figure. 4 ad the groud true, detectio result, ad quatitative evaluatio is preseted i Table. 1. Figure 3. localizatio ad segmetatio results Figure 4. extractio of urba trees Table 1. Quatitative Evaluatio Result usig radom forests Groud Truth Detectio Result Quatitative Evaluatio Urba Trees Urba Trees False Positive Completeess Correctess Quality F 1 -measure 311 268 33 86.2% 89.0% 77.9% 87.6% 4. CONCLUSION I this paper, we have proposed a ovel ad efficiet method to extract urba trees from mobile LiDAR poit clouds. Eve though our algorithm was tested i a complex eviromet, the segmetatio result is still good. The segmetatio is the key step for the classificatio of urba trees, for the extracted features largely rely o the segmetatio result. O the other had, the proposed features are suitable ad they ca be employed to classify trees ad o-tree objects. Therefore, our algorithm about extractio of urba trees is very promisig. Proc. of SPIE Vol. 9901 99010P-5
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