• DocumentCode
    467666
  • Title

    Automatic Terrain Selection Based on Clustering and Genetic Algorithm

  • Author

    Zhang, Quan-Xin ; Zheng, Jian-Jun ; Ling, Hai-yun ; Fan, Xiu-Mei

  • Author_Institution
    Beijing Inst. of Technol., Beijing
  • Volume
    1
  • fYear
    2007
  • fDate
    19-22 Aug. 2007
  • Firstpage
    372
  • Lastpage
    376
  • Abstract
    Terrain selection can´t be realized automatically in C3I. For a certain GIS, an automatic terrain selection based on clustering (CTS) is proposed. It processes the data from the DEM layer according to constrained conditions, and constructs clusters by unconnected graph traversal in order to obtain the primary terrain. Another terrain selection based on genetic algorithm (GATS) is also proposed. It uses correlative selection operator in the family, adjusts dynamically crossover and mutation ratios, and designs fitness function by utilizing synthetically several species of constrained conditions and fuzzy membership degree so as to optimize terrain selection. Wherein, CTS doesn´t need to assign the numbers of clusters previously, and the selected terrain accord with primary requirement. GATS can provide prepared multi-schemes straightway. The experimental results show that the two methods are feasible and effective.
  • Keywords
    fuzzy set theory; genetic algorithms; geographic information systems; DEM layer; automatic terrain selection; correlative selection operator; dynamically crossover; fuzzy membership degree; genetic algorithm; geographical information system; mutation ratios; Clustering algorithms; Clustering methods; Computer science; Cybernetics; Data models; Genetic algorithms; Geographic Information Systems; Geography; Machine learning; Rivers; Clustering; GIS; Genetic algorithm; Terrain selection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370172
  • Filename
    4370172