Analytical Method of Multi-Objective Genetic Algorithm with Multi-Objective Messy Genetic Algorithm in Satellite Image Segmentation

Authors(2) :-K. Pavithra, P. Dhanushika

Image can be dividing into different Segmentation. In image processing , the important task is Segmentation process methods. This method involves such as K-means clustering, watershed segmentation, Fuzzy c-Means, Iterative Self Organizing Data. Clustering methods depends powerfully on the selection of the primary spectral signatures which represents initial cluster centers. Normally, this is either done physically or erratically based on statistical operations. In this case the outcome is random and sometime inaccurate. In base paper an unsupervised method based on Multi-Objective Genetic Algorithm (MO-GA) for the selection of spectral signature from satellite images is implemented. The goal is to make greatest cluster centers as an initial population for any segmentation technique. Experimental results are conducted using high-resolution SPOT V satellite image and the verification of the segmentation results is based on a very elevated resolution satellite image of kind Quickbird. The spectral signatures method to Fuzzy c-means and K-means by MO-GA method increased the speed of the clustering algorithm to approximately4 times the speed of the random based selection of signatures. In this paper unsupervised method is comparative with Multi-Objective Messy Genetic Algorithm(MOMGA) with existing MO-GA methods for the selection of spectral signature using satellite images.

Authors and Affiliations

K. Pavithra
Assistant Professor, KG College of Arts and Science, Coimbatore, Tamil Nadu, India
P. Dhanushika
IT Department, KG College of Arts and Science, Coimbatore, Tamil Nadu, India

Multi-Objective Genetic Algorithm, Multi-Objective Messy Genetic Algorithm Clustering, Image Segmentations, Satellite Images

  1. R. Demirci, Rule-based automatic segmentation of color images. International Journal of Electronics and Communication60,435.
  2. K. Deb, Multi-objective Optimization Using Evolutionary Algorithms. (John Wiley and Sons, England).
  3. https://www.google.co.in/search?q=spot+v+satellite+image&source=lnms&sa=X&ei=u7SeUpbWL83YkQXW4ICwAQ&ved=0CAYQ_AUoAA&biw=1366&bih=629&dpr=1#q=spectural+signature++in+image+processing+-+wiki.
  4. http://epbysique.ulb.ac.be/IMG/pdf/devooght_2011.pdf
  5. J.MacQueen, Some Methods for classification and Analysis of Multivariate Observations, In Abstracts of 5-th Berkeley Symposium on Mathematical Statistics and Probability 1,Berkeley,
  6. R. Duda, P. Hart, and D. Stork D, Pattern Classification, Second Edition. (JohnWiley& Sons, New Jersey, 2000)
  7. J.Tou and R.Gonzalez, Pattern RecognitionPrinciples.( Addison-Wesley,).
  8. J. Dunn, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics 3, pp..
  9. J. Bezdek, Pattern Recognition with Fuzzy Objective Function Algoritms, (Plenum Press, New York,.

Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 168-173
Manuscript Number : CSEIT1831138
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

K. Pavithra, P. Dhanushika, "Analytical Method of Multi-Objective Genetic Algorithm with Multi-Objective Messy Genetic Algorithm in Satellite Image Segmentation", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.168-173, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1831138

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