Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
권호 표지
DOI QR코드

DOI QR Code

Comprehensive Comparisons among LIDAR Fitering Algorithms for the Classification of Ground and Non-ground Points

지면.비지면점 분류를 위한 라이다 필터링 알고리즘의 종합적인 비교

  • Received : 2011.12.14
  • Accepted : 2012.02.06
  • Published : 2012.02.29
Download PDF
⟨ Previous Next ⟩

Abstract

Filtering process that separates ground and non-ground points from LIDAR data is important in order to create the digital elevation model (DEM) or extract objects on the ground. The purpose of this research is to select the most effective filtering algorithm through qualitative and quantitative analysis for the existing filtering method used to extract ground points from LIDAR data. For this, four filtering methods including Adaptive TIN(ATIN), Perspective Center-based filtering method(PC), Elevation Threshold with Expand Window(ETEW) and Progressive Morphology(PM) were applied to mountain area, urban area and the area where building and mountains exist together. Then the characteristics for each method were analyzed. For the qualitative comparison of four filtering methods used for the research, visual method was applied after creating shaded relief image. For the quantitative comparison, an absolute comparison was conducted by using control points observed by GPS and a relative comparison was conducted by the digital elevation model of the National Geographic Information Institute. Through the filtering experiment of the LIDAR data, the Adaptive TIN algorithm extracted the ground points in mountain area and urban area most effectively. In the area where buildings and mountains coexist, progressive morphology algorithm generated the best result. In addition, as a result of qualitative and quantitative comparisons, the applicable filtering algorithm regardless of topographic characteristics appeared to be ATIN algorithm.

수치표고모델(DEM : Digital Elevation Model)을 생성하거나 지상의 객체를 추출하기 위해서 라이다 자료에서 지면점과 비지면점을 분리하는 필터링(filtering) 과정은 중요하다. 본 연구에서는 라이다 자료에서 지면점을 추출하는 데 사용되는 기존의 필터링 방법을 대상으로 정성적 분석과 정량적 분석을 통해 가장 효과적인 필터링 알고리즘을 선정하는 것을 목적으로 하였다. 이를 위해 Adaptive TIN, Perspective Center Based Filtering Algorithm, Elevation Threshold with Expand Window, Progressive Morphology의 4가지 필터링 방법을 산악지역, 도시지역, 건물과 산이 공존하는 3가지 지역에 적용하여 각각의 방법에 대한 특징을 분석하였다. 실험에 사용된 4가지 필터링 방법의 정성적인 비교는 음영기복도를 생성한 후 시각적인 방법을 적용하였고 정량적인 비교는 GPS로 관측한 검사점을 이용한 절대적인 비교와 국토지리정보원의 수치표고모델을 이용하여 상대적인 비교를 수행하였다. 라이다 필터링 실험을 통하여 Adaptive TIN 알고리즘은 산악지역과 도시지역에서 지면점을 가장 효율적으로 추출하였고 건물과 산이 공존하는 지역에서는 Progressive Morphology 알고리즘이 가장 양호한 결과를 나타내었다. 또한 정성적, 정량적 비교 결과 전반적으로 지역적 특성에 관계없이 적용가능한 필터링 알고리즘은 ATIN 알고리즘으로 나타났다.

Keywords

References

  1. 유환희, 김의명, 정동기 (2005), LIDAR자료의 지면과 비지면요소의 분류 정확도 평가, 대한토목학회논문집, 대한토목학회, 제25권 제6D호, pp. 929-935.
  2. 황세란, 이임평 (2011), 산림지형 모델링을 위한 항공 라이다 데이터의 지면점 필터링 비교분석과 정확도 개선, 한국측량학회지, 한국측량학회, 제29권 제6호 pp. 641-650.
  3. Abdullah, A. F., Rahman, A. A., Vojinovic, Z. (2009), Lidar Filtering Algorithms for Urban Flood Application: Review on Current Algorithms and Filters Test, Laserscanning09, Volume XXXVIII, pp. 30-36.
  4. Anna M. Y. Jarvis (2008), Intgration of Phottogrammetric and LiDAR Data for Accurate Reconstruction and Visualization of Urban Environments, Department of Geomatics Engineering, University of Calgary, UCGE Reports Number 20282.
  5. Axelsson, P. (2000), DEM Generation From Laser Scanner Data Using Adaptive TIN Model, International Archives of Photogrammetry and Remote Sensing, Vol. 33, pp. 110-117.
  6. Chang, Y., Habib, A., Lee, D., Yom, J. (2008), Automatic Classification of Lidar Data into Ground and Nonground Points, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII, Part B4, pp. 457-462.
  7. Kim, C. (2008), Object-based Integration of Photogrammetric and LiDAR for Accurate Reconstruction and Visualization of Building Models, Department of Geomatics Engineering, University of Calgary, UCGE Reports Number 20281.
  8. Sulaiman, N. S., Majid, Z., Setan, H. (2010), DTM Generation from LiDAR Data by Using Different Filters in Open-Source Software, Geoinformation Science Journal, Vol. 10, No. 2, pp. 89-109.
  9. Whitman, D., Zhang, K., Leatherman, S. P., and Robertson, W. (2003), Airborne Laser Topographic Mapping : Application To Hurricane Storm Surge Hazards, Earth Sciences in the Cities, American Geophysical Union, pp. 363-376.
  10. Zhang, K., Chen, S., Whitman, D., Shyu, M., Yan, J. and Zhang, C. (2003), A progressive morphological filter for removing non-ground measurements from airborne LIDAR data, IEEE Transactions on Geoscience and Remote Sensing, VOL. 41, NO. 4, pp. 872-882. https://doi.org/10.1109/TGRS.2003.810682
  11. Zhang, K., Cui, Z. (2007), Airborne LiDAR Data Processing and Analysis Tools ALDPAT 1.0, Software Manual, National Center for Airborne Laser Mapping, Florida International University, pp. 75-76.
  12. Zhang, K., Whitman, D. (2005), Comparison of Three Algorithms for Filtering Airborne Lidar Data, Photogrammetric Engineering and Remote Sensing, Vol. 71, No. 3, pp. 313-324. https://doi.org/10.14358/PERS.71.3.313

Cited by

  1. Automatic Tree Extraction Using LIDAR Data vol.21, pp.1, 2013, https://doi.org/10.7319/kogsis.2013.21.1.039
  2. 모의 항공 라이다 자료를 이용한 개별 가로수의 추정 vol.30, pp.3, 2012, https://doi.org/10.7848/ksgpc.2012.30.3.269
  3. 항공 라이다 자료를 이용한 수목추출의 자동화 모델 개발 vol.15, pp.5, 2014, https://doi.org/10.5762/kais.2014.15.5.3213
  4. Development of Classification Technique of Point Cloud Data Using Color Information of UAV Image vol.35, pp.4, 2012, https://doi.org/10.7848/ksgpc.2017.35.4.303
  5. Investigation of Topographic Characteristics of Parcels Using UAV and Machine Learning vol.35, pp.5, 2012, https://doi.org/10.7848/ksgpc.2017.35.5.349
  6. 머신러닝을 이용한 3차원 도로객체의 분류 vol.36, pp.6, 2012, https://doi.org/10.7848/ksgpc.2018.36.6.535