PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A new method of line feature generalization based on shape characteristic analysis

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a piecewise line generalization algorithm (PG) based on shape characteristic analysis. An adaptive threshold algorithm is used to detect all corners, from which key points are selected. The line is divided into some segments by the key points and generalized piecewise with the Li-Openshaw algorithm. To analyze the performance, line features with different complexity are used. The experimental results compared with the DP algorithm and the Li-Openshaw algorithm show that the PG has better performance in keeping the shape characteristic with higher position accuracy.
Rocznik
Strony
597--605
Opis fizyczny
Bibliogr. 20 poz., rys., wykr., wzory
Twórcy
autor
autor
  • ESSS Center. School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China, nhs@nudt.edu.cn
Bibliografia
  • [1] Li, Z. (2007). Digital Map Generalization at the Age of Enlightenment: A Review of the First Forty Years. The Cartographic Journal, 44(1), 80-93.
  • [2] Li, Z., Openshaw, S. (1992). Algorithms for Automated Line Generalization Based on a Natural Principle of Objective Generalization. International Journal of Geographical Information Systems, 6(5), 373-389.
  • [3] Ariza, L. F. J., Garcia, B. J. L. (2008). Generalization-Oriented Road Line Segmentation by Means of an Artificial Neural Network Applied over a Moving Window. Pattern Recognition, 41, 1610-1626.
  • [4] Lei, W., Liu, D., Tong, X. (2005). Discussion About Uncertainty of Spatial Line Feature Generalization Algorithms. Engineering of Surveying and Mapping, 30(6), 20-22.
  • [5] Shahriari N., Tao V. (2002). Minimissing Positional Errors in Line Simplification Using Adaptive Tolerance. Symposium on Geospatial Theory, Procesing and Application, 4(3), 213-223.
  • [6] Douglas, D., Peucker, T. (1973). Algorithm for the Reduction of the Numbers of Points Required to Represent a Digitized Line or Its Caricature. The Canadian Cartographer, 10(2), 112-122.
  • [7] Zhang, Q., Liao, K. (2001). Line Ganeralization Based on Structure Analysis. Acta Scientiarum Naturalium Universitatis Sunyatseni, 40(5), 118-121.
  • [8] Muller, J. C. (1990). The Removal of Spatial Conflicts in the Line Generalization. Cartography and Geographic Information Systems, 17(2), 141-149.
  • [9] Visvalingham, M., Whyatt, J. (1993). Line Generalization by Repeated Elimination of Points. The Cartographic Journal, 30(1), 46-51.
  • [10] Saalfeld, A. (1999). Topologically Consistent Line Simplification with the Douglas-Peucker Algorithm. Cartography and Geographic Information Science, 26(1), 7-18.
  • [11] Gold, C., Thibault, D. (2002). Map Generalization by Skeleton Retraction. ICA Workshop on Map Generalization, Ottawa, 78-85.
  • [12] Tong, X.-H., Xu, G.-S. (2004). A New Least Squares Method Based Line Generalization in Gis. IEEE International Geoscience and Remote Sensing Symposium Proceedings, 5, 2912-2915.
  • [13] Li, Z., Openshaw, S. (1993). A Natural Principle for Objective Generalisation of Digital Map Data. Cartography and Geographic Information Systems, 20(1), 19-29.
  • [14] Li, Z., Openshaw, S. (1994). Linear Feature’s Self-Adapted Generalization Algorithm Based on Impersonality Generalized Natural Law. Translation of Wuhan Technical University of Surveying and Mapping, 1, 49-58.
  • [15] Ai, T. (2007). The Drainage Network Extraction from Contour Lines for Contour Line Generalization. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 93-103.
  • [16] Chen, B., Zhu, K., Xue, B. (2007). Quality Assessment of Linear Features Simplification Algorithms. Journal of Zhengzhou Institute of Surveying and Mapping, 2(24), 121-124.
  • [17] Zhu, K.-P., Wu, F., Wang, H.-L., et al. (2007). Improvement and Assessment of Li-Openshaw Algorithm. Cehui Xuebao/Acta Geodaetica et Cartographica Sinica, 36, 450-456.
  • [18] He, X. C., Yung, N. H. C. (2004). Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support. Proceedings of the 17th International Conference on Pattern Recognition, 2, 791-794.
  • [19] He, X. C., Yung, N. H. C. (2008). Corner Detector Based on Global and Local Curvature Properties. Optical Engineering, 47(5).
  • [20] Mokhtarian, A. K., Mackworth, A. K. (1992). A Theory of Multi-Scale Curvature-Based Shape Representation for Planar Curves. IEEE Trans. Pattern Anal. Mach. Intell., 14(8), 789-805.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0087-0007
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.