Please wait a minute...

Frontiers of Earth Science

Front. Earth Sci.    2017, Vol. 11 Issue (1) : 11-19     DOI: 10.1007/s11707-016-0567-2
RESEARCH ARTICLE |
Using object-based analysis to derive surface complexity information for improved filtering of airborne laser scanning data
Menglong YAN1(),Thomas BLASCHKE2,Hongzhao TANG3,Chenchao XIAO4,Xian SUN1,Daobing ZHANG1,Kun FU1
1. Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2. Z_GIS – Centre for Geoinformatics and Department for Geography and Geology, University of Salzburg, Salzburg A-5020, Austria
3. Satellite Mapping Application Center, State Bureau of Surveying and Mapping, Beijing 100048, China
4. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China
Download: PDF(3598 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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     
Corresponding Authors: Menglong YAN   
Online First Date: 24 March 2016    Issue Date: 23 January 2017
 Cite this article:   
Menglong YAN,Thomas BLASCHKE,Hongzhao TANG, et al. Using object-based analysis to derive surface complexity information for improved filtering of airborne laser scanning data[J]. Front. Earth Sci., 2017, 11(1): 11-19.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-016-0567-2
http://journal.hep.com.cn/fesci/EN/Y2017/V11/I1/11
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Menglong YAN
Thomas BLASCHKE
Hongzhao TANG
Chenchao XIAO
Xian SUN
Daobing ZHANG
Kun FU
Fig.1  Illustration of surface complexity.
Fig.2  The segmentation procedure pseudocode.
Fig.3  (a) Complex surface areas (identified by red boxes); (b) Smaller window size for seed point generation (identified by red boxes).
Fig.4  Comparison of filtering result (the complex areas are identified by blue boxes). (a) Filtering result obtained by original filtering algorithm of Axelsson (2000); (b) Filtering result improved by our new seed point generation method.
Fig.5  Profiles of complex areas; a1 to e1 and a2 to e2 are the filtering results of the corresponding areas in Fig. 4(a) and Fig. 4(b) respectively.
Data set Method Ground points eI points eII points Non-ground points eI/% eII/% eT/%
Site1 Axelsson (2000) 1,471,335 184,556 10,745 158,047 11.145 6.366 10.703
Yan et al. (2012) 1,436,541 206,591 11,954 169,597 12.573 6.584 11.977
Our method 1,537,173 118,718 10,967 157,825 7.169 6.497 7.107
Site2 Axelsson (2000) 901,671 129,808 10,783 66,768 12.585 13.904 12.677
Yan et al. (2012) 864,315 160,327 13,145 71,243 15.647 15.577 15.642
Our method 955,813 75,666 11,444 66,107 7.336 14.757 7.855
Tab.1  Filtering results of site1 and site2
Fig.6  Classification result of non-ground points using surface complexity information (green color represents vegetation and red color represents buildings).
1 Ackermann F (1999). Airborne laser scanning-present status and future expectation. ISPRS J Photogramm Remote Sens, 54(2–3): 64–67
doi: 10.1016/S0924-2716(99)00009-X
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
doi: 10.1016/j.isprsjprs.2009.06.004
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
doi: 10.1016/j.rse.2010.01.002
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
doi: 10.1080/13658810903270569
6 Liu X (2008). Airborne lidar for dem generation: some critical issues. Prog Phys Geogr, 32(1): 31–49
doi: 10.1177/0309133308089496
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
doi: 10.1016/j.isprsjprs.2008.01.007
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
doi: 10.1016/j.isprsjprs.2008.09.001
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
doi: 10.1016/j.isprsjprs.2011.10.002
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
doi: 10.1016/j.jag.2014.01.012
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
doi: 10.1016/j.isprsjprs.2004.05.004
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
doi: 10.1016/j.isprsjprs.2007.01.001
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
doi: 10.1016/j.jag.2009.03.005
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
doi: 10.1080/01431160701736398
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
doi: 10.1080/01431161.2012.699694
18 Zhang K, Whitman D (2005). Comparison of three algorithms for filtering airborne lidar data. Photogramm Eng Remote Sensing, 71(3): 313–324
doi: 10.14358/PERS.71.3.313
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed