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http://dx.doi.org/10.7848/ksgpc.2020.38.1.1

Segmentation of Seabed Points from Airborne Bathymetric LiDAR Point Clouds Using Cloth Simulation Filtering Algorithm  

Lee, Jae Bin (Dept. of Civil Engineering, Mokpo National University, Next Generation Drone Commercialization Research Lab.)
Jung, Jae Hoon (Civil and Construction Engineering, Oregon State University)
Kim, Hye Jin (Dept. of Civil and Environmental Engineering, Seoul National University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.38, no.1, 2020 , pp. 1-9 More about this Journal
Abstract
ABL (Airborne Bathymetric LiDAR) is an advanced survey technology that uses green lasers to simultaneously measure the water depths and oceanic topography in coastal and river areas. Seabed point cloud extraction is an essential prerequisite to further utilizing the ABL data for various geographic data processing and applications. Conventional seabed detection approaches often use return waveforms. However, their limited accessibility often limits the broad use of the bathymetric LiDAR (Light Detection And Ranging) data. Further, it is often questioned if the waveform-based seabed extraction is reliable enough to extract seabed. Therefore, there is a high demand to extract seabed from the point cloud using other sources of information, such as geometric information. This study aimed to assess the feasibility of a ground filtering method to seabed extraction from geo-referenced point cloud data by using CSF (Cloth Simulation Filtering) method. We conducted a preliminary experiment with the RIGEL VQ 880 bathymetric data, and the results show that the CSF algorithm can be effectively applied to the seabed point segmentation.
Keywords
Airborne Bathymetric LiDAR; Seabed Points Segmentation; Ground Filtering; Cloth Simulation Filtering Algorithm;
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1 Andersen, M.S., Gergely, A., Al-Hamdani, Z., Steinbacher, F., Larsen, L.R., and Ernstsen, V.B. (2017), Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment, Hydrology and Earth System Science, Vol. 21, pp.43-63.   DOI
2 Chen, Z., Gao, B., and Devereux, B. (2017), State-of-the-art: DTM generation using airborne LiDAR data, Sensors, Vol. 17, No. 1, p. 150.   DOI
3 CloudCompare (2019a), CSF (plugin), CloudCompare, https://www.cloudcompare.org/doc/wiki/index.php?title=CSF_(plugin) (last date accessed: 9 January 2020).
4 CloudCompare (2019b), SOR filter, CloudCompare, https://www.cloudcompare.org/doc/wiki/index.php?title=SOR_filter (last date accessed: 9 January 2020).
5 Fawcett, T. (2006), An introduction to ROC analysis, Pattern Recognition Letters, Vol. 27, No. 8, pp. 861-874.   DOI
6 Guenther, G.C. (1985), Airborne Laser Hydrography: System Design and Performance Factors, NOAA Professional Paper Series No. 1, National Oceanographic and Atmospheric Administration, Rockville MD, pp. 203-242.
7 Guenther, G.C., Lillycrop, W.J., and Banic. J.R. (2002), Future advancements in airborne hydrography, International Hydrographic Review, Vol. 3, No. 2, pp. 67-90.
8 Huising, E.J. and Gomes Pereira, L.M. (1998), Errors and accuracy estimates of laser data acquired by various laser scanning systems for topographic applications, ISPRS Journal of Photogrammetry, Vol. 53, pp. 245-261.   DOI
9 Jeong, S.H. (2015), Accuracy Analysis of Seabed Terrain Modeling Technology, Master's thesis, University of Seoul, Seoul, Korea, 93p.
10 Kinzel, P.J., Legleiter, C.J., and Nelson. J.M. (2013), Mapping river bathymetry with a small footprint green Lidar: applications and challenges, Journal of the American Water Resources Association, Vol. 49, pp. 183-204.   DOI
11 Landis, J.R. and Koch, G.G. (1977), The measurement of observer agreement for categorical data, Biometrics, Vol. 33, No.1, pp. 159-174.   DOI
12 Lee, J., Kim, H., Hur, H., and Wie, K. (2019), Integration of airborne bathymetric LiDAR and multi-beam echo-sounder data for construction of high resolution terrain data in intertidal zone, Journal of Korean Society for Geospatial Information Science, Vol. 27, No. 2, pp. 23-30. (in Korean with English abstract)   DOI
13 Leica (2015), Leica LiDAR Survey Studio, Leica, http://leica-geosystems.com/products/airborne-systems/software/leica-lidar-survey-studio (last date accessed: 9 January 2020).
14 Mandlburger, G., Hauer, C., Wieser, M., and Pfeifer, N. (2015), Topobathymetric LiDAR for monitoring river morphodynamics and instream habitats-A case study at the Pielach River, Remote Sensing, Vol. 7, No. 5, pp. 6160-6195.   DOI
15 Nagle, D.B. and Wright, W.C. (2016), Algorithms Used in the Airborne Lidar Processing System (ALPS), Open-File Report 2016-1046, U.S. Geological Survey, Reston, Virginia, pp. 28-31.
16 NOAA (2018), NOAA data access viewer, NOAA, https://coast.noaa.gov/dataviewer/#/ (last date accessed: 9 January 2020).
17 Paine, J.G., Andrews, J.R., Saylam, K., and Tremblay, T.A. (2015), Airborne Lidar-based wetland and permafrostfeature mapping on an arctic coastal plain, north slope, Alaska, In: Remote Sensing of Wetlands, CRC Press, Boca Raton, F.L., pp. 413-434.
18 Polat, N. and Uysal, M. (2015), Investigating performance of airborne LiDAR data filtering algorithms for DTM generation, Measurement, Vol. 63, pp. 61-68.   DOI
19 RIEGL (2015), RiHYDRO data sheet, RIEGL, http://www.riegl.com/uploads/tx_pxpriegldownloads/DataSheet_RiHYDRO_2018-09-28_01.pdf (last date accessed: 9 January 2020).
20 Provot, X. (1995), Deformation constraints in a mass-spring model to describe rigid cloth behaviour, Graphics Interface 95, 17-19 May, Quebec, Canada, pp.147-154.
21 RIEGL (2018), VQ880G information sheet, RIEGL, http://www.riegl.com/uploads/tx_pxpriegldownloads/Infosheet_VQ-880-G_2016-05-23.pdf (last date accessed: 9 January 2020).
22 Saylam K., Hupp R.J., Averett R.A., Gutelius W.F., and Gelhar W.B. (2018), Airborne lidar bathymetry: assessing quality assurance and quality control methods with Leica Chiroptera examples, International Journal of Remote Sensing, Vol. 39, pp. 2518-2542.   DOI
23 Schwarz, R., Mandlburger, G., Pfennigbauer, M., and Pfeifer, N. (2019), Design and evaluation of a full-wave surface and bottom-detection algorithm for LiDAR bathymetry of very shallow waters, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 150, pp. 1-10.   DOI
24 Shin, M.S., Yang, I.T., and Lee, D.H. (2016), A study on airborne LiDAR calibration and operation techniques for bathymetric survey, Journal of the Korean Society for Geospatial Information Science, Vol. 24, No. 2, pp. 113-120.   DOI
25 Teledyne Optech (2013), Optech HydroFusion Information Sheet, Teledyne Optech, http://info.teledyneoptech.com/acton/attachment/19958/f-02e0/1/-/-/-/-/HydroFusion-Information-Sheet-160129-WEB.pdf (last date accessed: 6 January 2020).
26 Webster, T., McGuigan, K., Crowell, N., Collins, K., and MacDonald. C. (2014), Acquisition and Processing of Topobathymetric Lidar for Isle Madame in Support of the World Class Tanker Safety Initiative, Applied Geomatics Research Group. NSCC Middleton, NS, pp. 1-56.
27 Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan G. (2016), An easy-to-use airborne LiDAR data filtering method based on cloth simulation, Remote Sensing, Vol. 8, p. 501.   DOI