CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: AUTOMATIC EXTRACTION OF URBAN OBJECTS FROM MULTI-SOURCE AERIAL DATA Adriano Mancini, Emanuele Frontoni and Primo Zingaretti

Document type:: Monograph

Structure type:: Chapter

18 CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation The best orientation for a road is chosen by minimizing the num ber of extracted segments (as shown in Fig.9); a road can be de fined as the minimum set of segments with a length greater than T2 and same angular value. The set of segments which forms a road is created applying a clustering algorithm; the DBSCAN (Ester et al., 1996) is adopted to group the set of extracted seg ments. A segment belongs to a cluster if and only if the distance between the initial point of segment and the nearest neighbour is under a threshold; if this geometric criteria is satisfied the lengths of clusterized segments are also checked. If the length are compa rable (in terms of distance from the mean value of the cluster) the set of cluster is labelled as road and the centerline is calculated. In Fig. 10 a series of tests on Mannheim data-set for different orientations is shown. Tests put in evidence that the algorithm, owing to the clustering, does not consider incoherent segments (Fig. 10c). a) b) C) Figure 10: Road extraction for three different angles; segments are the thick red lines (bottom), while raw ones are shown in top The extracted geo-referenced and vectorial road graph with the proposed technique is shown in Fig.l 1; some roads are not cor rectly identified due to presence of high density canopies. Figure 11 : Road graph for Mannheim data-set 5 CONCLUSIONS AND FUTURE WORK In this paper, we presented a complete methodology to solve the problem of automatic extraction of urban objects from multi source aerial data. The procedure, which consists of sequential steps, takes advantage of classified data with a powerful machine learning algorithm as AdaBoost with CART as weak learner. The capability of distinguishing among four classes in an urban area as Mannheim increases the set of possible applications; two test cases were presented: building and road extraction. In the case of building extraction, the fusion of spectral data with LiDAR data using AdaBoost overtakes the limits of a simple nDSM thresh olding especially when canopies have a high density. The pro posed road extraction method allows to reduce the effect of oc- clusions;roads, extracted with the “line growing” approach en hanced with clustering, well match with a photo-interpretation process. As future works, more tests on more complex data with curved lines will be performed; moreover different weak learners based on RBF Neural Networks will be tested. ACKNOWLEDGMENT We would like to thank C. Nardinocchi and K. Khoshelham at Delft University of Technology for their helpful support. REFERENCES Bacher, U. and Mayer, H., 2005. Automatic road extraction from multispectral high resolution satellite images. Proceedings of CMRT05. Breiman, L., Friedman, J., Olshen, R. and Stone, C., 1984. Clas sification and Regression Trees. Wadsworth and Brooks, Mon terey, CA. Clode, S. P„ Rottensteiner, F. and Kootsookos, P., 2005. Data acquisition for 3d city models from lidar extracting buildings and roads. Proceedings of CMRT05. Duda, R. O., Hart, P. E. and Stork, D. G., 2000. Pattern Classifi cation. Wiley-Interscience Publication. Ester, M., peter Kriegel, H., S, J. and Xu, X., 1996. A density- based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Knowledge Discovery and Data Mining Conference, AAAI Press, pp. 226-231. Frontoni, E„ Khoshelham, K., Nardinocchi, C., Nedkov, S. and Zingaretti, R, 2008. Comparative analysis of automatic ap proaches to building detection from multisource aerial data. Pro ceedings of GEOBIA. Guo, Y., Bai, Z., Li, Y. and Liu, Y., 2007. Genetic algorithm and region growing based road detection in sar images. Proceedings of Int. Conference on Natural Computation pp. 330-334. Hahn, K., Han, Y. and Hahn, H., 2007. Extraction of partially occluded elliptical objects by modified randomized hough trans form. In: Proceedings German conference on Advances in Arti ficial Intelligence, Springer-Verlag, pp. 323-336. Kass, M„ Witkin, A. and Terzopoulos, D., 1987. Snakes: Active contour models. Int. J. of Computer Vision 1 (4), pp. 321 -331. Liu, H. and Motoda, H., 2008. Computational Methods of Fea ture Selection. Chapman and Hall/CRC. Lu, Y., Trinder, J. and Kubik, K., 2006. Automatic building de tection using the dempster-shafer algorithm. Photogrammetric Engineering and Remote Sensing 72(4), pp. 395^403. Marikhu, R., Dailey, M. N., Makhanov, S. and Honda, K., 2006. A family of quadratic snakes for road extraction. Lecture Notes in Computer Science ACCV 2007 4843, pp. 85-94. Mena, J., 2003. State of the art on automatic road extraction for gis update: a novel classification. Pattern Recognition Letters 24, pp. 3037-3058. Schapire, R. and Singer, Y., 1999. Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3), pp. 297-336. Walter, V., 2004. Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry and Remote Sensing 58, pp. 225-238. Weidner, U. and Forstner, W., 1995. Towards automatic building extraction from high resolution digital elevation models. ISPRS J. of Photogrammetry and Remote Sensing 50(4), pp. 38^49.

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