Abstract
Digital terrain models (DTM) are basic products required for a number of applications and decision making processes. Nowadays, high spatial-resolution DTMs are primarily produced through airborne laser scanners (ALS). However, the ALS does not directly deliver DTMs but a dense cloud of 3-d points that embeds both terrain elevation and height of natural and human-made features. Such a point cloud is generally rasterized and referred to as the digital surface model (DSM). The discrimination of aboveground objects from terrain, also termed ground filtering, is a basic processing step that has proved especially difficult for large areas of complex terrain characteristics. This paper presents the development of a multiscale erosion operator for removing aboveground features in the DSM, thus producing a surface that is close to the DTM. Such an approximation was used to separate ground from non-ground points in the original point-cloud and the discrimination accuracy was assessed using publicly available data. Results indicated an improvement over a previously published method.
Chapter PDF
Similar content being viewed by others
References
Pfeifer, N., Mandlburger, G.: 11. In: LiDAR Data Filtering and DTM Generation, pp. 307–333. CRC Press, Boca Raton (2009)
Axelson, P.: Processing of laser scanner data - algorithms and applications. ISPRS Journal of Photogrametry and Remote Sensing 54(2-3), 138–147 (1999)
Meng, X., Currit, N., Zhao, K.: Ground filtering algorithms for airborne lidar data: A review of critical issues. Remote Sensing 2(3), 833–860 (2010)
Thuy, T., Tokunaga, M.: Filtering airborne laser scanner data: A wavelet-based clustering method. Photogrammetric Engineering and Remote Sensing 70(11), 1267–1274 (2004)
Chen, Q., Gong, P., Baldocchi, D., Xie, G.: Filtering airborne laser scanning data with morphological methods. Photogrammetric Engineering & Remote Sensing 73(2), 175–185 (2007)
Evans, J.S., Hudak, A.T.: A multiscale curvature algorithm for classifying discrete return lidar in forested environments. IEEE Transactions on Geoscience and Remote Sensing 45(4), 1029–1038 (2007)
Silván-Cárdenas, J., Wang, L.: A multi-resolution approach for filtering LiDAR altimetry data. ISPRS Journal of Photogrammetry and Remote Sensing 61(1), 11–22 (2006)
Silván-Cárdenas, J., Escalante-Ramírez, B.: The multiscale Hermite transform for local orientation analysis. IEEE Transactions on Image Processing 15(5), 1236–1253 (2006)
Silván-Cárdenas, J., Wang, C.W.L., Rogerson, P., Feng, T., Kamphaus, B.: Assessing fine-spatial-resolution remote sensing for small-area population estimation. International Journal of Remote Sensing 31(21), 5605–5634 (2010)
Silván-Cárdenas, J.L., Wang, L.: Extraction of buildings footprint from lidar altimetry data with the hermite transform. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds.) MCPR 2011. LNCS, vol. 6718, pp. 314–321. Springer, Heidelberg (2011)
Silván-Cárdenas, J.L.: A segmentation method for tree crown detection and modelling from lidar measurements. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera López, J.A., Boyer, K.L. (eds.) MCPR 2012. LNCS, vol. 7329, pp. 65–74. Springer, Heidelberg (2012)
Silván-Cárdenas, J., Wang, L.: A multiscale approach for ground filtering from LiDAR altimetry measurements. In: Scale Issues in Remote Sensing. John Wiley and Sons (to appear, 2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Silván-Cárdenas, J.L. (2013). A Multiscale Erosion Operator for Discriminating Ground Points in LiDAR Point Clouds. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Rodríguez, J.S., di Baja, G.S. (eds) Pattern Recognition. MCPR 2013. Lecture Notes in Computer Science, vol 7914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38989-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-38989-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38988-7
Online ISBN: 978-3-642-38989-4
eBook Packages: Computer ScienceComputer Science (R0)