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Article:A hybrid FPAB/BBO Algorithm for Satellite Image Classification

by Navdeep Kaur Johal, Samandeep Singh, Harish Kundra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 5
Year of Publication: 2010
Authors: Navdeep Kaur Johal, Samandeep Singh, Harish Kundra
10.5120/1074-1403

Navdeep Kaur Johal, Samandeep Singh, Harish Kundra . Article:A hybrid FPAB/BBO Algorithm for Satellite Image Classification. International Journal of Computer Applications. 6, 5 ( September 2010), 31-36. DOI=10.5120/1074-1403

@article{ 10.5120/1074-1403,
author = { Navdeep Kaur Johal, Samandeep Singh, Harish Kundra },
title = { Article:A hybrid FPAB/BBO Algorithm for Satellite Image Classification },
journal = { International Journal of Computer Applications },
issue_date = { September 2010 },
volume = { 6 },
number = { 5 },
month = { September },
year = { 2010 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume6/number5/1074-1403/ },
doi = { 10.5120/1074-1403 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:55:15.262643+05:30
%A Navdeep Kaur Johal
%A Samandeep Singh
%A Harish Kundra
%T Article:A hybrid FPAB/BBO Algorithm for Satellite Image Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 6
%N 5
%P 31-36
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the past years, remote sensing has been used for the classification of satellite image on a very large scale. This paper deals with image classification by using swarm computing technique. In this work, we use a new swarm data clustering method based upon flower pollination by artificial bees to cluster the satellite image pixels. The aim of clustering is to separate a set of data points into self-similar groups. Those clusters will be further classified using Biogeography Based Optimization. The results indicate that highly accurate classification of the satellite image is obtained by using the proposed algorithm.

References
  1. Bonabeau E., Dorigo M., and Theraulaz G. (`999), “Swarm Intelligence: From natural to artificial systems”, Oxford University press, NY, pp.1-25.
  2. Lillesand, T.M., Kiefer, R.W. and Chipman, J.W. (2003), “Remote Sensing and Image Interpretation”, Fifth Edition, Wiley & Sons Ltd., England, pp.586-592.
  3. Ma, H. (2009), “An analysis of the behavior of migration models for Biogeography-based Optimization”, Submitted for publication.
  4. Kazemian, M., Ramezani, Y., Lucas, C., Moshiri, B. (2006), “Swarm Clustering Based on Flowers Pollination by Artificial Bees”, Studies in Computational Intelligence (SCI), vol. 34, Springer Berlin Publishers, New York, pp. 191 – 202.
  5. Panchal, V.K., Singh, P., Kaur, N. and Kundra, H.(2009), “Biogeography based Satellite Image Classification”, International Journal of Computer Science and information Security, vol. 6, no.2, pp.269-274.
  6. Simon, D. (2008), “Biogeography-based Optimization”, IEEE Transactions on Evolutionary Computation, vol. 12, No.6, IEEE Computer Society Press. pp. 702-713.
  7. Simon, D., Ergezer, M. and Du, D.(2009), “Population Distributions in Biogeography-Based Optimization Algorithms with Elitism”, IEEE Conference on Systems, Man, and Cybernetics, San Antonio, TX, pp. 1017-1022.
  8. The MATLAB ver 7, The MathWorks, Inc.
Index Terms

Computer Science
Information Sciences

Keywords

FPAB Biogeography Based Optimization Satellite Image Classification

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