A Differential Evolutionary Multilevel Segmentation of Near Infra-Red Images Using Renyi’s Entropy

  • Soham Sarkar
  • Nayan Sen
  • Abhinava Kundu
  • Swagatam Das
  • Sheli Sinha Chaudhuri
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)


In recent years remote sensing image processing has got some intense attention of the researchers for its utility in land cover study, natural calamity detection, object tracking etc. In case of remote sensing image processing, the primal objective is to sub divide the image into more than one segment. In doing so, Multi-level thresholding based image segmentation techniques play an useful role in accomplishing this critical task. Endeavor of this paper is to focus on obtaining the optimal multiple threshold points from a LISS III Near Infra-Red (NIR) band by employing Renyi’s Entropy. Moreover, a state-of-art meta-heuristics like Differential Evolution (DE) is incorporated to acquire optimal threshold values in reduced computational time with precision.


Multilevel Image Segmentation Remote Sensing Images Renyi’s Entropy Differential Evolution LISS-III Near Infra-Red (NIR) 


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  1. 1.
    Sahoo, P.K., Wilkins, C., Yeager, J.: Threshold selection using Renyi’s entropy. Pattern Recognition 30, 71–84 (1997)CrossRefMATHGoogle Scholar
  2. 2.
    Sahoo, P.K., Arora, G.: A thresholding method based on 2-D Renyi’s entropy. Pattern Recognition, 1149–1161 (2004)Google Scholar
  3. 3.
    Hammouchea, K., Diaf, M., Siarry, P.: A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem. Engineering Applications of Artificial Intelligence 23(5), 676–688 (2010)CrossRefGoogle Scholar
  4. 4.
    Storn, R., Price, K.V.: Differential Evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, ICSI (1995),
  5. 5.
    Das, S., Suganthan, P.N.: Differential evolution – a survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15(1), 4–31 (2011)CrossRefGoogle Scholar
  6. 6.
    Sarkar, S., Patra, G.R., Das, S.: A Differential Evolution Based Approach for Multilevel Image Segmentation Using Minimum Cross Entropy Thresholding. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part I. LNCS, vol. 7076, pp. 51–58. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Rényi, A.: On measures of information and entropy. In: Proceedings of the 4th Berkeley Symposium on Mathematics, Statistics and Probability, pp. 547–561 (1960)Google Scholar
  8. 8.
    Zuiderveld, K.: Contrast Limited Adaptive Histograph Equalization. In: Graphic Gems IV, pp. 474–485. Academic Press Professional, San Diego (1994)CrossRefGoogle Scholar
  9. 9.
    Miyahara, M., Kotani, K., Algazi, V.R.: Objective picture quality scale (PQS) for image coding. IEEE Trans. on Communications 46(9), 1215–1226 (1998)CrossRefGoogle Scholar
  10. 10.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Soham Sarkar
    • 1
  • Nayan Sen
    • 1
  • Abhinava Kundu
    • 1
  • Swagatam Das
    • 2
  • Sheli Sinha Chaudhuri
    • 3
  1. 1.Electronics and Communication Engineering DepartmentRCC Institute of Information TechnologyKolkataIndia
  2. 2.Electronics and Communication Sciences UnitIndian Statistical InstituteKolkataIndia
  3. 3.Electronics and Telecommunication Engineering DepartmentJadavpur UniversityKolkataIndia

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