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Coastline Detection from SAR Images by Level Set Model

  • Maria Mercede Cerimele
  • Luigi Cinque
  • Rossella Cossu
  • Roberta Galiffa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

In this paper we present an innovative and automatic procedure which is used to extract the coastline from SAR (Synthetic Aperture Radar) images by the level set model. This model consists in a PDE (Partial Differential Equation) equation governing the evolution of a curve corresponding to the zero level of a 3D function, called level set function, until the curve reaches the edge of the region to be segmented. A coastline is the boundary between land and sea masses. Detecting the coastline is of fundamental importance when monitoring various natural phenomena such as tides, coastal erosion and the dynamics of glaciers. In this case SAR images show problems which arise from the presence of the speckle noise and of the strong signal deriving from the rough or slight sea. In fact in the case of heavy sea the signal determines an intensity similar to the one of land, making it difficult to distinguish the coastline.

Keywords

Active Contour Synthetic Aperture Radar Initial Curve Speckle Noise Speed Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  2. 2.
    Sethian, J.A.: Evolution, Implementation and Application of Level Set and Fast Marching Methods for Advancing Fronts. Journal of Computional Physics 169, 503–555 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. Springer, New York (2002)zbMATHGoogle Scholar
  4. 4.
    Lee, J.-S., Igor, J.: Coastline Detection and Tracing in SAR. Images IEEE Transaction on Geoscence and Remote Sensing 28, 662–668 (1999)Google Scholar
  5. 5.
    Dellepiane, S., De Laurentiis, R., Giordano, F.: Coastline Extraction from Sar Images and a Method for the Evaluation of the Coastline Precision. Pattern Recognition Letters 25, 146–147 (2004)CrossRefGoogle Scholar
  6. 6.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. International Journal Vision 1, 321–333 (1988)CrossRefzbMATHGoogle Scholar
  7. 7.
    Germain, O., Refregier, P.: Edge Location in SAR Images: Performance of the Likehood Ratio Filter and Accuracy Improvement with an Active Contour Approach. IEEE Trans. Image Processing 10, 72–77 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Chesnaud, C., Refregier, P., Boulet, V.: Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models. IEEE Trans. Pattern Analysis and Machine Intelligence 21, 1145–1157 (1999)CrossRefGoogle Scholar
  9. 9.
    Ben Ayed, I., Mitiche, A., Belhadj, Z.: Multiregion Level-Set Partitioning of Synthetic Aperture Radar Images. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 793–800 (2005)CrossRefGoogle Scholar
  10. 10.
    Yu, Y., Acton, S.T.: Speckle Reducing Anisotropic Diffusion. IEEE Trans. on Image Processing 11, 1260–1270 (2002)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Perona, P., Malik, J.: Scale Space and Edge Detection using Anisotropic Diffusion. IEEE Trans. Pattern Analysis and Machine Intelligence 12, 629–639 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Maria Mercede Cerimele
    • 1
  • Luigi Cinque
    • 2
  • Rossella Cossu
    • 1
  • Roberta Galiffa
    • 1
  1. 1.Istituto per le Applicazioni del Calcolo “M. Picone” CNRRomaItaly
  2. 2.Universitá degli Studi “Sapienza”RomaItaly

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