Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

ISPRS Journal of Photogrammetry and Remote Sensing - Tập 105 - Trang 286-304 - 2015
Martin Weinmann1, Boris Jutzi1, Stefan Hinz1, Clément Mallet2
1Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstraße 7, 76131 Karlsruhe, Germany
2Université Paris-Est, IGN, SRIG, MATIS, 73 avenue de Paris, 94160 Saint-Mandé, France

Tài liệu tham khảo

Arya, 1998, An optimal algorithm for approximate nearest neighbor searching in fixed dimensions, J. ACM, 45, 891, 10.1145/293347.293348 Belton, D., Lichti, D.D., 2006. Classification and segmentation of terrestrial laser scanner point clouds using local variance information. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVI-5, pp. 44–49. Blomley, R., Weinmann, M., Leitloff, J., Jutzi, B., 2014. Shape distribution features for point cloud analysis – a geometric histogram approach on multiple scales. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3, pp. 9–16. Boyko, 2011, Extracting roads from dense point clouds in large scale urban environment, ISPRS J. Photogr. Remote Sens., 66, S02, 10.1016/j.isprsjprs.2011.09.009 Breiman, 1996, Bagging predictors, Machine Learn., 24, 123, 10.1007/BF00058655 Breiman, 2001, Random forests, Machine Learn., 45, 5, 10.1023/A:1010933404324 Bremer, M., Wichmann, V., Rutzinger, M., 2013. Eigenvalue and graph-based object extraction from mobile laser scanning point clouds. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-5/W2, pp. 55–60. Brodu, 2012, 3d terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology, ISPRS J. Photogr. Remote Sens., 68, 121, 10.1016/j.isprsjprs.2012.01.006 Carlberg, M., Gao, P., Chen, G., Zakhor, A., 2009. Classifying urban landscape in aerial lidar using 3d shape analysis. In: Proceedings of the IEEE International Conference on Image Processing, IEEE, Cairo, Egypt, 7–10 November, pp. 1701–1704. Chang, 2011, LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol., 2, 27:1, 10.1145/1961189.1961199 Chehata, N., Guo, L., Mallet, C., 2009. Airborne lidar feature selection for urban classification using random forests. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-3/W8, pp. 207–212. Cortes, 1995, Support-vector networks, Machine Learn., 20, 273, 10.1007/BF00994018 Cover, 1967, Nearest neighbor pattern classification, IEEE Trans. Inform. Theory, 13, 21, 10.1109/TIT.1967.1053964 Criminisi, 2013, Decision forests for computer vision and medical image analysis, 10.1007/978-1-4471-4929-3 Demantké, J., Mallet, C., David, N., Vallet, B., 2011. Dimensionality based scale selection in 3d lidar point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-5/W12, pp. 97–102. Demantké, J., Vallet, B., Paparoditis, N., 2012. Streamed vertical rectangle detection in terrestrial laser scans for facade database production. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-3, pp. 99–104. Efron, 1979, Bootstrap methods: another look at the jackknife, Ann. Stat., 7, 1, 10.1214/aos/1176344552 Fayyad, 1993, Multi-interval discretization of continuous-valued attributes for classification learning, 1022 Filin, 2005, Neighborhood systems for airborne laser data, Photogr. Eng. Remote Sens., 71, 743, 10.14358/PERS.71.6.743 Fisher, 1936, The use of multiple measurements in taxonomic problems, Ann. Eugen., 7, 179, 10.1111/j.1469-1809.1936.tb02137.x Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci., 55, 119, 10.1006/jcss.1997.1504 Friedman, 1977, An algorithm for finding best matches in logarithmic expected time, ACM Trans. Math. Softw., 3, 209, 10.1145/355744.355745 Gerke, M., Xiao, J., 2013. Supervised and unsupervised MRF based 3d scene classification in multiple view airborne oblique images. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W3, pp. 25–30. Gini, C., 1912. Variabilite e mutabilita. Memorie di metodologia statistica. Goulette F., Nashashibi, F., Abuhadrous, I., Ammoun, S., Laurgeau, C., 2006. An integrated on-board laser range sensing system for on-the-way city and road modelling. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVI-1. Guan, 2014, Using mobile laser scanning data for automated extraction of road markings, ISPRS J. Photogr. Remote Sens., 87, 93, 10.1016/j.isprsjprs.2013.11.005 Guo, 2014, Classification of airborne laser scanning data using JointBoost, ISPRS J. Photogr. Remote Sens., 92, 124 Guyon, 2003, An introduction to variable and feature selection, J. Machine Learn. Res., 3, 1157 Hall, M.A., 1999. Correlation-based feature subset selection for machine learning. Ph.D. thesis, Department of Computer Science, University of Waikato, New Zealand. Hebert, M., Bagnell, J.A., Bajracharya, M., Daniilidis, K., Matthies, L.H., Mianzo, L., Navarro-Serment, L., Shi, J., Wellfare, M., 2012. Semantic perception for ground robotics. In: Proceedings of SPIE 8387, Unmanned Systems Technology XIV, SPIE, Baltimore, USA, 23 April, pp. 83870Y:1–12. Hu, 2013, Efficient 3-d scene analysis from streaming data, 2297 John, 1995, Estimating continuous distributions in Bayesian classifiers, 338 Johnson, 1999, Using spin images for efficient object recognition in cluttered 3d scenes, IEEE Trans. Pattern Anal. Machine Intell., 21, 433, 10.1109/34.765655 Jutzi, B., Gross, H., 2009. Nearest neighbour classification on laser point clouds to gain object structures from buildings. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-1-4-7/W5. Khoshelham, K., Oude Elberink, S.J., 2012. Role of dimensionality reduction in segment-based classification of damaged building roofs in airborne laser scanning data. In: Proceedings of the International Conference on Geographic Object Based Image Analysis, Rio de Janeiro, Brazil, 7–9 May, pp. 372–377. Kim, 2011, Urban scene understanding from aerial and ground lidar data, Machine Vis. Appl., 22, 691, 10.1007/s00138-010-0279-7 Kim, H.B., Sohn, G., 2011. Random forests based multiple classifier system for power-line scene classification. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-5/W12, pp. 253–258. Kolmogorov, 1933 Kononenko, 1994, Estimating attributes: analysis and extensions of RELIEF, 171 Lafarge, 2012, Creating large-scale city models from 3d-point clouds: a robust approach with hybrid representation, Int. J. Comput. Vis., 99, 69, 10.1007/s11263-012-0517-8 Lalonde, 2005, Scale selection for classification of point-sampled 3d surfaces, 285 Lari, Z., Habib, A., 2012. Alternative methodologies for estimation of local point density index: moving towards adaptive lidar data processing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B3, pp. 127–132. Lee, I., Schenk, T., 2002. Perceptual organization of 3d surface points. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIV-3A, pp. 193–198. Lim, 2009, 3d terrestrial lidar classifications with super-voxels and multi-scale conditional random fields, Comput.-Aid. Des., 41, 701, 10.1016/j.cad.2009.02.010 Linsen, L., Prautzsch, H., 2001. Local versus global triangulations. In: Proceedings of Eurographics, Manchester, UK, 5–7 September, pp. 257–263. Liu, H., Motoda, H., Setiono, R., Zhao, Z., 2010. Feature selection: an ever evolving frontier in data mining. In: Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, Hyderabad, India, 21 June, pp. 4–13. Lodha, 2007, Aerial lidar data classification using AdaBoost, 435 Mallet, 2011, Relevance assessment of full-waveform lidar data for urban area classification, ISPRS J. Photogr. Remote Sens., 66, S71, 10.1016/j.isprsjprs.2011.09.008 Mitra, 2003, Estimating surface normals in noisy point cloud data, 322 Monnier, F., Vallet, B., Soheilian, B., 2012. Trees detection from laser point clouds acquired in dense urban areas by a mobile mapping system. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-3, pp. 245–250. Muja, M., Lowe, D.G., 2009. Fast approximate nearest neighbors with automatic algorithm configuration. In: Proceedings of the International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, 5–8 February, pp. 331–340. Munoz, 2009, Contextual classification with functional max-margin Markov networks, 975 Najafi, 2014, Non-associative higher-order Markov networks for point cloud classification, 500 Niemeyer, J., Rottensteiner, F., Soergel, U., 2012. Conditional random fields for lidar point cloud classification in complex urban areas. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-3, pp. 263–268. Niemeyer, 2014, Contextual classification of lidar data and building object detection in urban areas, ISPRS J. Photogr. Remote Sens., 87, 152, 10.1016/j.isprsjprs.2013.11.001 Osada, 2002, Shape distributions, ACM Trans. Graph., 21, 807, 10.1145/571647.571648 Oude Elberink, S., Kemboi, B., 2014. User-assisted object detection by segment based similarity measures in mobile laser scanner data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3, pp. 239–246. Özuysal, 2007, Fast keypoint recognition in ten lines of code, 1 Pauly, 2003, Multi-scale feature extraction on point-sampled surfaces, Comput. Graph. Forum, 22, 281, 10.1111/1467-8659.00675 Pearson, 1896, Mathematical contributions to the theory of evolution. III. Regression, heredity and panmixia, Philos. Trans. Roy. Soc. Lond. A, 187, 253, 10.1098/rsta.1896.0007 Peng, 2005, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Trans. Pattern Anal. Machine Intell., 27, 1226, 10.1109/TPAMI.2005.159 Poullis, 2009, Automatic reconstruction of cities from remote sensor data, 2775 Press, 1988 Pu, 2011, Recognizing basic structures from mobile laser scanning data for road inventory studies, ISPRS J. Photogr. Remote Sens., 66, S28, 10.1016/j.isprsjprs.2011.08.006 Quinlan, 1986, Induction of decision trees, Machine Learn., 1, 81, 10.1007/BF00116251 Riedmiller, M., Braun, H., 1993. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, San Francisco, USA, 28 March–1 April, pp. 586–591. Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0 Rusu, R.B., Marton, Z.C., Blodow, N., Beetz, M., 2008. Persistent point feature histograms for 3d point clouds. In: Proceedings of the International Conference on Intelligent Autonomous Systems, Baden-Baden, Germany, 23–25 July, pp. 119–128. Rusu, 2009, Fast point feature histograms (FPFH) for 3d registration, 3212 Saeys, 2007, A review of feature selection techniques in bioinformatics, Bioinformatics, 23, 2507, 10.1093/bioinformatics/btm344 Schapire, 1990, The strength of weak learnability, Machine Learn., 5, 197, 10.1007/BF00116037 Schindler, 2012, An overview and comparison of smooth labeling methods for land-cover classification, IEEE Trans. Geosci. Remote Sens., 50, 4534, 10.1109/TGRS.2012.2192741 Schmidt, 2014, Contextual classification of full waveform lidar data in the Wadden Sea, IEEE Geosci. Remote Sens. Lett., 11, 1614, 10.1109/LGRS.2014.2302317 Secord, 2007, Tree detection in urban regions using aerial lidar and image data, IEEE Geosci. Remote Sens. Lett., 4, 196, 10.1109/LGRS.2006.888107 Serna, 2013, Urban accessibility diagnosis from mobile laser scanning data, ISPRS J. Photogr. Remote Sens., 84, 23, 10.1016/j.isprsjprs.2013.07.001 Serna, 2014, Detection, segmentation and classification of 3d urban objects using mathematical morphology and supervised learning, ISPRS J. Photogr. Remote Sens., 93, 243, 10.1016/j.isprsjprs.2014.03.015 Serna, 2014, Paris-rue-Madame database: a 3d mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods, 819 Shannon, 1948, A mathematical theory of communication, Bell Syst. Tech. J., 27, 379, 10.1002/j.1538-7305.1948.tb01338.x Shapovalov, R., Velizhev, A., Barinova, O., 2010. Non-associative Markov networks for 3d point cloud classification. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-3A, pp. 103–108. Shapovalov, 2013, Spatial inference machines, 2985 Tokarczyk, P., Wegner, J.D., Walk, S., Schindler, K., 2013. Beyond hand-crafted features in remote sensing. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W1, pp. 35–40. Tombari, 2010, Unique signatures of histograms for local surface description, 356 Unnikrishnan, 2008, Multi-scale interest regions from unorganized point clouds, 1 Vanegas, 2012, Automatic extraction of manhattan-world building masses from 3d laser range scans, IEEE Trans. Visual. Comput. Graph., 18, 1627, 10.1109/TVCG.2012.30 Velizhev, A., Shapovalov, R., Schindler, K., 2012. Implicit shape models for object detection in 3d point clouds. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-3, pp. 179–184. Vosselman, G., 2013. Point cloud segmentation for urban scene classification. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W2, pp. 257–262. Waldhauser, 2014, Automated classification of airborne laser scanning point clouds, 269 Weinmann, M., Jutzi, B., Mallet, C., 2013. Feature relevance assessment for the semantic interpretation of 3d point cloud data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-5/W2, pp. 313–318. Weinmann, M., Jutzi, B., Mallet, C., 2014. Semantic 3d scene interpretation: a framework combining optimal neighborhood size selection with relevant features. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3, pp. 181–188. Weinmann, 2015, Distinctive 2d and 3d features for automated large-scale scene analysis in urban areas, Comput. Graph., XXX West, K.F., Webb, B.N., Lersch, J.R., Pothier, S., Triscari, J.M., Iverson, A.E., 2004. Context-driven automated target detection in 3-d data. In: Proceedings of SPIE 5426, Automatic Target Recognition XIV, SPIE, Orlando, USA, 12 April, pp. 133–143. Wurm, 2014, Identifying vegetation from laser data in structured outdoor environments, Robot. Autonom. Syst., 62, 675, 10.1016/j.robot.2012.10.003 Xiong, 2011, 3-d scene analysis via sequenced predictions over points and regions, 2609 Xu, 2014, Multiple-entity based classification of airborne laser scanning data in urban areas, ISPRS J. Photogr. Remote Sens., 88, 1, 10.1016/j.isprsjprs.2013.11.008 Yu, 2003, Feature selection for high-dimensional data: a fast correlation-based filter solution, 856 Yu, 2013, A performance evaluation of volumetric 3d interest point detectors, Int. J. Comput. Vis., 102, 180, 10.1007/s11263-012-0563-2 Zhao, Z., Morstatter, F., Sharma, S., Alelyani, S., Anand, A., Liu, H., 2010. Advancing feature selection research – ASU feature selection repository. Technical Report, School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, USA. Zhou, 2013, Complete residential urban area reconstruction from dense aerial lidar point clouds, Graph. Models, 75, 118, 10.1016/j.gmod.2012.09.001 Zhou, 2012, Mapping curbstones in airborne and mobile laser scanning data, Int. J. Appl. Earth Observ. Geoinform., 18, 293, 10.1016/j.jag.2012.01.024