Using object-based analysis to derive surface complexity information for improved filtering of airborne laser scanning data

Abstract

Airborne laser scanning (ALS) is a technique used to obtain Digital Surface Models (DSM) and Digital Terrain Models (DTM) efficiently, and filtering is the key procedure used to derive DTM from point clouds. Generating seed points is an initial step for most filtering algorithms, whereas existing algorithms usually define a regular window size to generate seed points. This may lead to an inadequate density of seed points, and further introduce error type I, especially in steep terrain and forested areas. In this study, we propose the use of object-based analysis to derive surface complexity information from ALS datasets, which can then be used to improve seed point generation. We assume that an area is complex if it is composed of many small objects, with no buildings within the area. Using these assumptions, we propose and implement a new segmentation algorithm based on a grid index, which we call the Edge and Slope Restricted Region Growing (ESRGG) algorithm. Surface complexity information is obtained by statistical analysis of the number of objects derived by segmentation in each area. Then, for complex areas, a smaller window size is defined to generate seed points. Experimental results show that the proposed algorithm could greatly improve the filtering results in complex areas, especially in steep terrain and forested areas.

Keywords

airborne laser scanning / object-based analysis / surface complexity information / filtering algorithm

References

[1] Ackermann F (1999). Airborne laser scanning-present status and future expectation. ISPRS J Photogramm Remote Sens, 54(2–3): 64–67

[2] Axelsson P (2000). DEM generation from laser scanner data using adaptive TIN models. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIII(Part B4/1): 110–117

[3] Blaschke T (2010). Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens, 65(1): 2–16

[4] Ke Y, Quackenbush L J, Im J (2010). Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification. Remote Sens Environ, 114(6): 1141–1154

[5] Liu H X, Wang L, Sherman D, Gao Y G, Wu Q S (2010). An object-based conceptual framework and computational method for representing and analyzing coastal morphological changes. Int J Geogr Inf Sci, 24(7): 1015–1041

[6] Liu X (2008). Airborne lidar for dem generation: some critical issues. Prog Phys Geogr, 32(1): 31–49

[7] Liu Y, Guo Q H, Kelly M (2008). A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis. ISPRS J Photogramm Remote Sens, 63(4): 461–475

[8] Meng X L, Wang L, Silván-Cárdenas J L, Currit N (2009). A multidirectional ground filtering algorithm for airborne LIDAR. ISPRS J Photogramm Remote Sens, 64(1): 117–124

[9] Mongus D, Žalik B (2012). Parameter-free ground filtering of LiDAR data for automatic DTM generation. ISPRS J Photogramm Remote Sens, 67(1): 1–12

[10] Nyström M, Holmgren J, Fransson J E S, Olsson H (2014). Detection of windthrown trees using airborne laser scanning. Int J Appl Earth Obs Geoinf, 30(8): 21–29

[11] Shih F Y (2010). Image Processing and Pattern Recognition: Fundamentals and Techniques. New Jersey: Wiley-IEEE Press, 157–158

[12] Sithole G, Vosselman G (2004). Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS J Photogramm Remote Sens, 59(1–2): 85–101

[13] Sohn G, Dowman I J (2007). Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction. ISPRS J Photogramm Remote Sens, 62(1): 43–63

[14] Vu T T, Yamazaki F, Matsuoka M (2009). Multi-scale solution for building extraction from LiDAR and image data. Int J Appl Earth Obs Geoinf, 11(4): 281–289

[15] Wagner W, Hollaus M, Briese C, Ducic V (2008). 3D vegetation mapping using small-footprint full-waveform airborne laser scanners. Int J Remote Sens, 29(5): 1433–1452

[16] Wang C, Glenn N F (2009). Integrating LiDAR intensity and elevation data for terrain characterization in a forested area. IEEE Geosci Remote Sens Lett, 6(3): 463–466 doi:10.1109/LGRS.2009.2016986

[17] Yan M L, Blaschke T, Liu Y, Wu L (2012). An object-based analysis filtering algorithm for airborne laser scanning. Int J Remote Sens, 33(22): 7099–7116

[18] Zhang K, Whitman D (2005). Comparison of three algorithms for filtering airborne lidar data. Photogramm Eng Remote Sensing, 71(3): 313–324

Acknowledgments

The authors would like to thank the anonymous reviewers for providing comments to improve the quality of this paper, and iSPACE of Research Studios Austria FG (RSA) (http://ispace.researchstudio.at/) for providing the ALS datasets. The study described in this paper is funded by the National Natural Science Foundation of China (Grant No. 41301493), the High Resolution Earth Observation Science Foundation of China (GFZX04060103-5-17), and Special Fund for Surveying and Mapping Scientific Research in the Public Interest (201412007).

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2016 Higher Education Press and Springer-Verlag Berlin Heidelberg