Neural Computing and Applications

, Volume 31, Supplement 2, pp 1253–1261 | Cite as

Segmentation of weather radar image based on hazard severity using RDE: reconstructed mutation strategy for differential evolution algorithm

  • Meera RamadasEmail author
  • Millie Pant
  • Ajith Abraham
  • Sushil Kumar
Original Article


Weather describes the condition of our atmosphere during a specific period of time, and climate represents a composite of day to day weather over longer period of time. Climatology attempts to analyze and explain the impact of climate so that the society can plan accordingly. Climatology analysis is often done on radar images representing various climatic conditions. These images contain varying scale of severity for any specific climatic parameter of study. The climatologists often find it convenient to analyze climatic conditions if tools are available to segment the weather images based on the severity scale which is represented by different colors. Segmentation of the weather radar image is also used for automated analysis of weather conditions. Differential evolution (DE) approach instead is used for fast selection of optimal threshold. In present paper, we have applied DE with multilevel thresholding for weather image segmentation which results in minimum computational time and excellent image quality. A new mutation strategy for DE named reconstructed differential evolution (RDE) strategy is suggested for better performance over image segmentation. Using fuzzy entropy and RDE for multilevel thresholding provides better results in comparison with last suggested methods.


Radar Satellite images Multilevel thresholding Fuzzy Mutation Optimization Severity 


Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.


  1. 1.
    Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359Google Scholar
  2. 2.
    Roula MA, Bouridane A, Kurugollu F (2004) An evolutionary snake algorithm for the segmentation of nuclei in histopathological images. In: 2004 International Conference on Image Processing, 2004. ICIP’04. vol. 1, IEEE, pp. 127–130Google Scholar
  3. 3.
    Omran MGH, Engelbrecht AP, Salman A (2005) Differential evolution methods for unsupervised image classification. In: 2005 IEEE Congress on Evolutionary Computation, vol. 2, IEEE, pp. 966–973Google Scholar
  4. 4.
    Rahnamayan S, Tizhoosh HR, Salama MMA (2006) Image thresholding using differential evolution. In: International conference of image processing, computer vision and pattern recognition, pp. 244–249Google Scholar
  5. 5.
    Aslantas V, Tunckanat M (2007) Differential evolution algorithm for segmentation of wound images. In: IEEE International Symposium on Intelligent Signal Processing, 2007. WISP 2007, IEEE, pp. 1–5Google Scholar
  6. 6.
    Rahnamayan S, Tizhoosh HR (2008) Image thresholding using micro opposition-based differential evolution (micro-ODE). In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1409–1416Google Scholar
  7. 7.
    Hasan H, Haron H, Hashim SZ (2009) Freeman chain code extraction using differential evolution (DE) and particle swarm optimization (PSO). In: 2009. SOCPAR’09. International Conference of Soft Computing and Pattern Recognition, IEEE, pp. 77–81Google Scholar
  8. 8.
    Azarbad M, Ebrahimzadeh A, Babajani-Feremi A (2010) Brain tissue segmentation using an unsupervised clustering technique based on PSO algorithm. In: Biomedical Engineering (ICBME), 2010 17th Iranian Conference of, IEEE, pp. 1–6Google Scholar
  9. 9.
    Kumar S, Pant M, Ray AK (2011) Differential evolution embedded Otsu’s method for optimized image thresholding. In: 2011 World Congress on Information and Communication Technologies (WICT), IEEE, pp. 325–329Google Scholar
  10. 10.
    Li Z, Chen X, Luo P, Tian Y (2012) Water area segmentation of the Yangcheng Lake with SAR data based on improved 2D maximum entropy and genetic algorithm. In: 2012 Second International Workshop on Earth Observation and Remote Sensing Applications (EORSA), IEEE, pp. 263–267Google Scholar
  11. 11.
    Sarkar S, Das S (2013) Multilevel image thresholding based on 2D histogram and maximum Tsallis entropy—a differential evolution approach. IEEE Trans Image Process 22(12):4788–4797MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Paul S, Bandyopadhyay B (2014) A novel approach for image compression based on multi-level image thresholding using Shannon entropy and differential evolution. In: Students’ Technology Symposium (TechSym), 2014 IEEE, pp. 56–61Google Scholar
  13. 13.
    Ochoa-Montiel R (2015) Thresholding of biological images by using evolutionary algorithms. In: 2015 Latin America Congress on Computational Intelligence (LA-CCI), IEEE, pp. 1–6Google Scholar
  14. 14.
    El Allaouil A, Nasri M, Merzougui M, Mirhisse J (2016) Evolutionary Algorithm for Segmentation of Medical Images by Region Rrowing. In: 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV), IEEE, pp. 119–124Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Amity University Uttar PradeshNoidaIndia
  2. 2.Department of Applied Science and EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia
  3. 3.MIR LabsAuburnUSA

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