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The Impact of Optimizing the Number of Points of ALS Data Set on the Accuracy of the Generated DTM

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Airborne laser scanning technology delivers the result of the survey in the form of a point cloud. In order to construct a digital terrain model, it is necessary to perform filtration, which consists in separating data reflecting the relief features from the data reflecting situational details. In view of the very large amount of data in the survey data set, as well as the time consumption and difficulty in automatic filtration of the point cloud, it is possible to apply an optimization algorithm reducing the size of the point cloud while deriving a digital terrain model. This study presents the stages of compiling an airborne laser scanning point cloud using filtration and optimization. The filtration was carried out using the adaptive TIN model and the method of robust moving surfaces, while optimization was carried out with the application of an already existing algorithm to reduce the size of the survey data set. The effect of reducing the size of the data set on the accuracy of the generated DTM was tested and empirical and numerical tests have been performed.
Słowa kluczowe
Rocznik
Tom
Strony
265--278
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
Bibliografia
  • AXELSSON P. 2000. DEM generation from laser scanner data using adaptive TIN models. International Archives of Photogrammetry and Remote Sensing, Vol. XXXIII/4B, Amsterdam.
  • BŁASZCZAK W., KAMIŃSKI W. 2007. Data number reduction in measurement results set using optimization algorithm. Proceedings of FIG Working Week, CD.
  • BŁASZCZAK-BĄK W., JANOWSKI A., KAMIŃSKI W., RAPIŃSKI J. 2010a. Proposition of modification of aerial laser survey point cloud processing methodology. Archives of Geomatics “New technology and instruments In survey”.
  • BŁASZCZAK-BĄK W., JANOWSKI A., KAMIŃSKI W., RAPIŃSKI J. 2010b. Modification of Lidar Point Cloud Processing Methodology. Sydney The XXX FIG General Assembly and Working Week, Sydney 13.05 - 17.05 2010. CD.
  • BŁASZCZAK-BĄK W., JANOWSKI A., KAMIŃSKI W., RAPIŃSKI J. 2011. ALS Data Filtration with Fuzzy Logic. Journal of the Indian Society of Remote Sensing, 39(4): pp 591-597.
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  • SCHUT G.H. 1976. Review of interpolation methods for digital terrain models. XII th Congress of the International Society for Photogrammetry. Helsinki.
  • SLUITER R. 2008. Interpolation methods for climate data: literature review. De Bilt, Royal Netherlands Meteorological Institute (KNMI).
  • VISVALINGAM M., WHYATT J.D. 1992. Line generalization by repeated elimination of point. Cartographic Information Systems Research Group, University of Hull.
  • VOSSELMAN G. 2001. Adjustment and filtering of raw laser altimetry data. OEEPE Workshop on Airborne Laserscanning and Interferometric SAR for Detailed Digital Elevation Models, Stockholm.
  • WACK R., WIMMER A. 2002. Digital terrain models from airborne laser scanner data - a grid based approach. International Archives of Photogrammetry and Remote Sensing, XXXIV/3B, Graz.
  • ZHANG K. 2003. A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(4): 872-882.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAR0-0070-0059
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