Movatterモバイル変換

[0]ホーム
URL:

Skip to main content
Springer Nature Link
Log in

A Mathematical Morphology Based Scale Space Method for the Mining of Linear Features in Geographic Data

  • Original Paper
  • Published:
Data Mining and Knowledge Discovery Aims and scope Submit manuscript

Abstract

This paper presents a spatial data mining method MCAMMO and its extension L_MCAMMO designed for discovering linear and near linear features in spatial databases. L_MCAMMO can be divided into two basic steps: first, the most suitable re-segmenting scale is found by MCAMMO, which is a scale space method with mathematical morphology operators; second, the segmented result at this scale is re-segmented to obtain the final linear belts. These steps are essentially a multi-scale binary image segmentation process, and can also be treated as hierarchical clustering if we view the points under each connected component as one cluster. The final number of clusters is the one which survives (relatively, not absolutely) the longest scale range, and the clustering which first realizes this number of clusters is the most suitable segmentation. The advantages of MCAMMO in general and L_MCAMMO in particular, are: no need to pre-specify the number of clusters, a small number of simple inputs, capable of extracting clusters with arbitrary shapes, and robust to noise. The effectiveness of the proposed method is substantiated by the real-life experiments in the mining of seismic belts in China.

This is a preview of subscription content,log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Figure 1
Figure 2.
Figure 3.
Figure 4.
Figure 5.
Figure 6.
Figure 7.
Figure 8.
Figure 9.
Figure 10.
Figure 11.
Figure 12.
Figure 13.
Figure 14.
Figure 15.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Acton S.T., and Mukherjee, D.P. 2000. Scale space classification using area morphology. IEEE Transactions on Image Processing, 9(4):623–635.

    Article  Google Scholar 

  • Amorese, D. Lagarde, J.L., and Laville, E. 1999. A point pattern analysis of the distribution of earthquakes in Normandy (France). Bulletin of the Seismological Society of America, 89(3):742–749.

    Google Scholar 

  • Asano T., and Katoh, N. 1996. Variants for the Hough transform for line detection. Computational Geometry, 6:231–252.

    Article MATH MathSciNet  Google Scholar 

  • Ball G. and Hall, D. 1965. ISODATA, a novel method of data analysis and classification. Research Report AD-699616, Stanford Research Institute, Stanford, CA.

  • Bezdek, J.C. Coray, C. Gunderson, R., and Watson, J. 1981. Detection and characterization of cluster substructure. I. Linear structure: Fuzzy C-Lines. SIAM J. Appl. Math., 40(2):339–357.

    Article MATH MathSciNet  Google Scholar 

  • Cui, Y. 2000. Image Processing and Analysis: Mathematical Morphology and Its Applications vol. 38. Beijing, China: Science Press, pp. 67–76.

    Google Scholar 

  • Di, K. Li, D.L., and Li, D.Y. 1998. A mathematical morphology based algorithm for discovering Clusters in spatial databases. Journal of Image and Graphics, 3(3):173–178.

    MathSciNet  Google Scholar 

  • Ester, M. Kriegel, H.P. Sander, J., and Xu, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, 324–331.

  • Ester, M. Kriegel, H.P. Sander, J., and Xu, X. 1998. Clustering for mining in large spatial databases. Special Issue on Data Mining. Knstliche Intelligenz, 12(1):18–24.

    Google Scholar 

  • Fasulo, D. 1999. An analysis of recent work on clustering algorithms. Technical Report 01-03-02, Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, http://citeseer.nj.nec.com/fasulo99analysi.html

  • Fu, Z. 1997. Research on the earthquake activity mechanics in China's mainland. Beijing, China: Earthquake Press, 124–128.

    Google Scholar 

  • GISdefelopment.net 2005, http://www.gisdevelopment.net/glossary/l.htm

  • He, B. Ma, T. Wang, Y., and Zhu, H. 2001. Digital image processing with visual C++. Beijing, China: People's Posts and Telecommunications Press, 335371.

    Google Scholar 

  • Honda, K. Togo, N. Fujii, T., and Ichihashi, H. 2002. Linear fuzzy clustering based on least absolute deviations. Proc. of 2002 IEEE International Conference of Fuzzy Systems, 1444–1449.

  • Jones, R.H., and Stewart, R.C. 1997. A method for determining significant structures in a cloud of earthquake. Journal of Geophysics Research, 102:8245–8254.

    Article  Google Scholar 

  • Kolatch, E. 2002. Clustering algorithms for spatial databases: a survey. http://citeseer.nj.nec.com/436843.html

  • Koperski, K. Adhikary, J., and Han, J. 1996. Spatial data mining: progress and challenges survey paper. Proc. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, Canada.

  • Leung, Y. Zhang, J., and Xu, Z. 2000. Clustering by scale-space filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12):1396–1409.

    Article  Google Scholar 

  • Lindeberg, T. 1996. Scale-space: a framework for handling image structures at multiple scales. Proc. CERN school of computering, Egmond aan Zee, The Netherlands.

  • Maragos, P. 1989. Pattern spectrum and multiscale shape representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):701–716.

    Article MATH  Google Scholar 

  • Marceau, D.J. 1999. The scale issue in social and natural sciences. Canadian Journal of Remote Sensing, 25(4):347–356.

    MathSciNet  Google Scholar 

  • National Department of Earthquake 1996. Conspectus of the Layout Map on China's Earthquake Intensity (1990). Beijing, China: Earthquake Press, 64.

    Google Scholar 

  • Park, K. R., and Lee, C. 1996. Scale-space using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(11):1121–1126.

    Article MathSciNet  Google Scholar 

  • Peng, W. 1991. The Computer Processing of Remote Sensing Data and Geography Information System. Beijing, China: Beijing Normal School Publishing House, 128–132.

    Google Scholar 

  • Postaire, J.G. Zhang, R.D., and Botte, C.L. 1993. Cluster analysis by binary morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(2):170–180.

    Article  Google Scholar 

  • Sander, J. Ester, M. Kriegel, H., and Xu, X. 1998. Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 2:169–194.

    Article  Google Scholar 

  • Serra, J. 1982. Image analysis and mathematical morphology. New York: Academic Press

    MATH  Google Scholar 

  • Song, C.Q., and Zhang, Z.C. 1982. Basic Geology, Beijing, China: High Education Press

    Google Scholar 

  • Sun, J., and Yang, C. 1995. Computer Graphics. Beijing, China: Tsinghua University Press, 185–186.

    Google Scholar 

  • The seismic analysis and forecasting center 1980. China Seismological Bureau, The Seismic Catalog in East of China, Beijing: The Earthquake Publishing House.

    Google Scholar 

  • The seismic analysis and forecasting center 1989. China Seismological Bureau, The Seismic Catalog in West of China, Beijing: The Earthquake Publishing House.

    Google Scholar 

  • Witkin, A.P. 1983. Scale-space filtering. Proc. 8th Int. Joint Conf. Art. Intell. 1019–1022.

  • Wong, Y. 1993. Clustering data by melting. Neural Computation, 5:89–104.

    Article  Google Scholar 

  • Wong, Y., and Posner, E.C. 1993. A new clustering algorithm applicable to multispectral and polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing, 31(3):634–644.

    Article  Google Scholar 

  • Xu, X. 1999. A fast parallel clustering algorithm for large spatial databases, Data Mining and Knowledge Discovery, 3:263–290.

    Article  Google Scholar 

  • Zhang, D., and Lutz, T. 1989. Structural control of igneous complexes and kimberlites: a new statistical method. Tectonophysics, 159:137–148.

    Article  Google Scholar 

Download references

Acknowledgment

This work is supported by National Natural Science Foundation of China under grant No.40401039, and Chinese Postdoctoral Foundation. The authors would like to thank the referees for their valuable comments

Author information

Authors and Affiliations

  1. College of Geography Science, Nanjing Normal University, Nanjing, China

    Min Wang

  2. Department of Geography and Resource Management, Center for Environmental Policy and Resource Management, and Joint Laboratory for Geoinformation Science, The Chinese University of Hong Kong, Shatin, Hongkong, China

    Yee Leung

  3. State Key Laboratory of Resources and Environment Information System, Chinese Academy of Sciences, Beijing, China

    Chenhu Zhou, Tao Pei & Jiancheng Luo

Authors
  1. Min Wang

    You can also search for this author inPubMed Google Scholar

  2. Yee Leung

    You can also search for this author inPubMed Google Scholar

  3. Chenhu Zhou

    You can also search for this author inPubMed Google Scholar

  4. Tao Pei

    You can also search for this author inPubMed Google Scholar

  5. Jiancheng Luo

    You can also search for this author inPubMed Google Scholar

Rights and permissions

About this article

Cite this article

Wang, M., Leung, Y., Zhou, C.et al. A Mathematical Morphology Based Scale Space Method for the Mining of Linear Features in Geographic Data.Data Min Knowl Disc12, 97–118 (2006). https://doi.org/10.1007/s10618-005-0021-7

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp