Despeckling of SAR Image Based on Fuzzy Inference System

  • Debashree Bhattacharjee
  • Khwairakpam Amitab
  • Debdatta Kandar
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)

Abstract

Synthetic Aperture Radar (SAR) is a type of imaging radar system that is widely used for remote sensing of Earth. It is observed that the images obtained from the SAR systems are often corrupted with speckle noise which reduces the visibility of the image. Preprocessing such images is often essential to enhance the clarity of the image for acquiring the required information present in it. A nonlinear filtering technique is proposed in this work, based on fuzzy inference rule-based systems, which uses fuzzy sets and fuzzy rules that operates on the luminance difference between the central pixel and its neighbors in a 3 × 3 window to reduce the presence of speckle noise in the SAR images. A comparative evaluation is performed on the proposed filter with three other existing filtering methods namely Mean, Median and FIRE filter for mixed noise to evaluate its performance.

Keywords

Synthetic Aperture Radar (SAR) Speckle noise Fuzzy inference system Image processing Image filtering 

References

  1. 1.
    Skolnik, M. I.: Introduction to Radar Systems. McGraw-Hill, New York (1962)Google Scholar
  2. 2.
    Skolnik, M. I.: Radar Handbook. McGraw-Hill, New York (2008)Google Scholar
  3. 3.
  4. 4.
    Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., Papathanassiou, K.P.: A Tutorial on Synthetic Aperture Radar. In: IEEE Geoscience and Remote Sensing Magazine, vol. 1, pp. 6–43. IEEE (2013)Google Scholar
  5. 5.
    Chan, Y.K., Koo, V.C.: An Introduction to Synthetic Aperture Radar (SAR). In: Progress in Electromagnetics Research B, vol. 2, pp. 27–60 (2008)Google Scholar
  6. 6.
    Jain, A.N.: Fundamentals of Digital Image Processing. Prentice Hall, NJ, USA (1989)Google Scholar
  7. 7.
    Lee, J-S.: Speckle Suppression and Analysis for Synthetic Aperture Radar. In: Optical Engineering vol. 25(5) (1986)Google Scholar
  8. 8.
    Frost, V.S., Stiles, J.A., Shanmugan, K.S., Holtzman, J.C.: A model for radar images and its application to adaptive digital filtering of multiplicative noise. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-4(2), pp. 157–166. IEEE (1982)Google Scholar
  9. 9.
    Kuan, D.T., Sawchuk, A.A., Strand, T.C., Chavel, P.: Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-7(2), pp. 165–177. IEEE (1985)Google Scholar
  10. 10.
    Chen, Y., Huang, F., Yang, J.: A Fuzzy Filter for SAR Image De-noising, 8th International Conference on Signal Processing, vol. 2. IEEE (2006)Google Scholar
  11. 11.
    Cheng, H., Tian, J.: Speckle Reduction of Synthetic Aperture Radar Images Based on Fuzzy Logic. In: First International Workshop on Education Technology and Computer Science, vol. 1, pp. 933–937. IEEE (2009)Google Scholar
  12. 12.
    Russo, F.: A user-friendly research tool for image processing with fuzzy rules. In: International Conference on Fuzzy System, pp. 561–568. IEEE (1992)Google Scholar
  13. 13.
    Nachtegael, M., Schulte, S., Van der Weken, D., De Witte, V., Kerre, E.E.: Fuzzy Filters for Noise Reduction: the Case of Gaussian Noise. In: The 14th IEEE International Conference on Fuzzy Systems, pp 201–206. IEEE (2005)Google Scholar
  14. 14.
    Mitra, S.K., Sicuranza, G.L.: Non Linear Image Processing, Elsevier, pp: 355– 374, (2001)Google Scholar
  15. 15.
    Russo, F.: Nonlinear Fuzzy Filters: An Overview. In: European Signal Processing Conference, EUSIPCO 1996, pp. 1–4. IEEE, Trieste, Italy (1996)Google Scholar
  16. 16.
    Kosko, B.: Neural Networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hal, Inc. NJ, USA (1992)Google Scholar
  17. 17.
    Russo, F.: Fuzzy Systems in Instrumentation: Fuzzy Signal Processing. In: IEEE Transactions on Instrumentation and Measurement, vol. 4, pp. 735–740. IEEE, Waltham, MA, USA (1995)Google Scholar
  18. 18.
    National Remote Sensing Centre Indian Space Research Organisation, http://nrsc.gov.in/RISAT-1_Sample_Images?q=nellore

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Debashree Bhattacharjee
    • 1
  • Khwairakpam Amitab
    • 1
  • Debdatta Kandar
    • 1
  1. 1.Department of Information Technology, School of TechnologyNorth Eastern Hill UniversityShillongIndia

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