• DocumentCode
    3307657
  • Title

    A Comparison of Ridge Detection Methods for DEM Data

  • Author

    Koka, Shinji ; Anada, Koichi ; Nakayama, Yasunori ; Sugita, Kimio ; Yaku, Takeo ; Yokoyama, Ryusuke

  • Author_Institution
    Dept. Comput. Sci. & Syst. Anal., Nihon Univ., Narashino, Japan
  • fYear
    2012
  • fDate
    8-10 Aug. 2012
  • Firstpage
    513
  • Lastpage
    517
  • Abstract
    We deal with ridge detection methods from digital elevation map (DEM) data. As ridge detection methods, the O (N2) -time steepest ascent method and the O (N) -time discrete Lap lace transform (D.L.T.) method are known, where N is the number of cells. However, the D.L.T. method is too blurry to form ridge lines. In this paper, we introduce a 12 neighbor D.L.T. method which is a modification of the 4 neighbor D.L.T. method. And we also introduce another ridge detection method by the classification of local shapes around each cell. We can consider 32 patterns for ridges or valleys. Furthermore, we compare and evaluate their ridge detection methods in a certain area. We note that our two methods provide blurry terrain maps, but it require only O (N) -time for N cells, in comparison with the steepest ascent method.
  • Keywords
    Laplace transforms; digital elevation models; discrete transforms; feature extraction; image classification; object detection; terrain mapping; DEM data; DLT method; digital elevation map data; discrete Laplace transform; feature extraction; local shape classification; ridge detection methods; terrain maps; time steepest ascent method; Classification algorithms; Computer science; Educational institutions; Feature extraction; Laplace equations; NASA; Shape; digital elevation map (DEM) data; ridge detection; the steepest ascent method;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD), 2012 13th ACIS International Conference on
  • Conference_Location
    Kyoto
  • Print_ISBN
    978-1-4673-2120-4
  • Type

    conf

  • DOI
    10.1109/SNPD.2012.46
  • Filename
    6299330