Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier

This is an author's accepted manuscript of an article published in "International Journal of Remote Sensing" ; Volume 35, Issue 19, 2014; copyright Taylor & Francis; available online at: http://www.tandfonline.com/doi/abs/10.1080/01431161.2014.960619In recent years, light detection and ranging (lidar) systems have been intensively used in different urban applications such as map updating, communication analysis, virtual city modelling, risk assessment, and monitoring. A prerequisite to enhance lidar data content is to differentiate ground (bare earth) points that yield digital terrain models and off-terrain points in order to classify urban objects and vegetation. The increasing demand for a fast and efficient algorithm to extract three-dimensional urban features was the motive behind this work. A new combined approach to extract bare-earth points is proposed, and a novel methodology to automatically classify airborne laser data into different objects in an urban area is presented. In addition, a new concept of angular classification is introduced to differentiate buildings from vegetation and other small objects. The new angular classifier analyses the distribution of bare-earth points around unclassified point clusters to determine whether a cluster can be classified either as building or as vegetation. The experimental results confirm the high accuracy achieved to automatically classify urban objects in flat complex areas.Sánchez Lopera, J.; Lerma García, JL. (2014). Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier. International Journal of Remote Sensing. 35(19):6955-6972. doi:10.1080/01431161.2014.960619695569723519Biosca, J. M., & Lerma, J. L. (2008). Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods. ISPRS Journal of Photogrammetry and Remote Sensing, 63(1), 84-98. doi:10.1016/j.isprsjprs.2007.07.010Brovelli, M. A., Cannata, M., & Longoni, U. M. (2004). LIDAR Data Filtering and DTM Interpolation Within GRASS. Transactions in GIS, 8(2), 155-174. doi:10.1111/j.1467-9671.2004.00173.xChen, H., Cheng, M., Li, J., & Liu, Y. (2012). AN ITERATIVE TERRAIN RECOVERY APPROACH TO AUTOMATED DTM GENERATION FROM AIRBORNE LIDAR POINT CLOUDS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B4, 363-368. doi:10.5194/isprsarchives-xxxix-b4-363-2012Chen, Q., Gong, P., Baldocchi, D., & Xie, G. (2007). Filtering Airborne Laser Scanning Data with Morphological Methods. Photogrammetric Engineering & Remote Sensing, 73(2), 175-185. doi:10.14358/pers.73.2.175Evans, J. S., & Hudak, A. T. (2007). A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments. IEEE Transactions on Geoscience and Remote Sensing, 45(4), 1029-1038. doi:10.1109/tgrs.2006.890412Filin, S., & Pfeifer, N. (2006). Segmentation of airborne laser scanning data using a slope adaptive neighborhood. ISPRS Journal of Photogrammetry and Remote Sensing, 60(2), 71-80. doi:10.1016/j.isprsjprs.2005.10.005Haala, N., & Brenner, C. (1999). Extraction of buildings and trees in urban environments. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3), 130-137. doi:10.1016/s0924-2716(99)00010-6Hartfield, K. A., Landau, K. I., & Leeuwen, W. J. D. van. (2011). Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat. Remote Sensing, 3(11), 2364-2383. doi:10.3390/rs3112364Kobler, A., Pfeifer, N., Ogrinc, P., Todorovski, L., Oštir, K., & Džeroski, S. (2007). Repetitive interpolation: A robust algorithm for DTM generation from Aerial Laser Scanner Data in forested terrain. Remote Sensing of Environment, 108(1), 9-23. doi:10.1016/j.rse.2006.10.013Kraus, K., & Pfeifer, N. (1998). Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 53(4), 193-203. doi:10.1016/s0924-2716(98)00009-4Li, Y. (2013). FILTERING AIRBORNE LIDAR DATA BY AN IMPROVED MORPHOLOGICAL METHOD BASED ON MULTI-GRADIENT ANALYSIS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W1, 191-194. doi:10.5194/isprsarchives-xl-1-w1-191-2013Lindenberger, J. 1993. “Laser-Profilmessungen zur topographischen Geländeaufnahme.” PhD diss., University Stuttgart, Institute of Photogrametry, Munich.Matikainen, L., Hyyppä, J., Ahokas, E., Markelin, L., & Kaartinen, H. (2010). Automatic Detection of Buildings and Changes in Buildings for Updating of Maps. Remote Sensing, 2(5), 1217-1248. doi:10.3390/rs2051217Meng, X., Wang, L., & Currit, N. (2009). Morphology-based Building Detection from Airborne Lidar Data. Photogrammetric Engineering & Remote Sensing, 75(4), 437-442. doi:10.14358/pers.75.4.437Mongus, D., & Žalik, B. (2012). Parameter-free ground filtering of LiDAR data for automatic DTM generation. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 1-12. doi:10.1016/j.isprsjprs.2011.10.002Moussa, A., & El-Sheimy, N. (2012). A NEW OBJECT BASED METHOD FOR AUTOMATED EXTRACTION OF URBAN OBJECTS FROM AIRBORNE SENSORS DATA. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B3, 309-314. doi:10.5194/isprsarchives-xxxix-b3-309-2012Pérez-García, J. L., Delgado, J., Cardenal, J., Colomo, C., & Ureña, M. A. (2012). PROGRESSIVE DENSIFICATION AND REGION GROWING METHODS FOR LIDAR DATA CLASSIFICATION. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B3, 155-160. doi:10.5194/isprsarchives-xxxix-b3-155-2012Shaker, A., & El-Ashmawy, N. (2012). LAND COVER INFORMATION EXTRACTION USING LIDAR DATA. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B7, 167-172. doi:10.5194/isprsarchives-xxxix-b7-167-2012Sithole, G., & Vosselman, G. (2004). Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 59(1-2), 85-101. doi:10.1016/j.isprsjprs.2004.05.004Susaki, J. (2012). Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation. Remote Sensing, 4(6), 1804-1819. doi:10.3390/rs4061804Trinder, J. C., & Salah, M. (2012). AERIAL IMAGES AND LIDAR DATA FUSION FOR DISASTER CHANGE DETECTION. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, I-4, 227-232. doi:10.5194/isprsannals-i-4-227-2012Wang, X., & Li, P. (2013). URBAN BUILDING COLLAPSE DETECTION USING VERY HIGH RESOLUTION IMAGERY AND AIRBORNE LIDAR DATA. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W1, 127-132. doi:10.5194/isprsarchives-xl-7-w1-127-2013Keqi Zhang, Shu-Ching Chen, Whitman, D., Mei-Ling Shyu, Jianhua Yan, & Chengcui Zhang. (2003). A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(4), 872-882. doi:10.1109/tgrs.2003.810682Zhang, K., & Whitman, D. (2005). Comparison of Three Algorithms for Filtering Airborne Lidar Data. Photogrammetric Engineering & Remote Sensing, 71(3), 313-324. doi:10.14358/pers.71.3.31