Satellite Image Classification using Ant Colony Optimization and Neural Network

Authors(1) :-Asha Rathee

From the last three decades, remote sensing has come up with great applications in the field of science and technology. The concept of remote sensing is the observation of earth using data acquired instruments satellites and aircrafts from the outer space. Remote sensing helps to monitor the environment states, natural resources availability and terrain features. The unique capabilities of remote sensing concept regarding earth observations are that it helps to monitor, forecast, understand and manage the available resources of earth. Here, remote sensing data used for the observations of land cover terrain features with the help of image classification process. It helps to obtain the geo spatial from satellite data that can be used in several applications of computing, research, space intelligence, defense etc. In this research work, we are using this image classification for the identification of land cover terrain features from the satellite data of Alwar region, India. Concept of ant colony optimization and neural network has been used for the classification. Ant colony is swarm intelligence based global optimization concept. The output from the ACO is used for the further optimization with neural network approach. Results are evaluated in terms of Overall accuracy and kappa coefficient. Results obtained using proposed integrated approach are efficient to declare the validate classification of image.

Authors and Affiliations

Asha Rathee
Department of Computer Science & Application, Maharshi Dayanand University, Rohtak, India

Image Classification, Satellite Image, Neural Network, Swarm Intelligence, Ant Colony Optimization

  1. Lu, Dengsheng, and Qihao Weng. "A survey of image classification methods and techniques for improving classification performance." International journal of Remote sensing 28, no. 5 (2007): 823-870.
  2. Haralick, Robert M., and Karthikeyan Shanmugam. "Textural features for image classification." IEEE Transactions on systems, man, and cybernetics 6 (1973): 610-621.
  3. Gorte, Ben. "Supervised image classification." In Spatial statistics for remote sensing, pp. 153-163. Springer, Dordrecht, 1999.
  4. Omran, Mahamed GH, Andries Petrus Engelbrecht, and Ayed Salman. "Differential evolution methods for unsupervised image classification." In Evolutionary Computation, 2005. The 2005 IEEE Congress on, vol. 2, pp. 966-973. IEEE, 2005.
  5. Lillesand, Thomas, Ralph W. Kiefer, and Jonathan Chipman. Remote sensing and image interpretation. John Wiley & Sons, 2014.
  6. Jensen, John R., and Kalmesh Lulla. "Introductory digital image processing: a remote sensing perspective." (1987): 65-65.
  7. Cilimkovic, Mirza. "Neural networks and back propagation algorithm." Institute of Technology Blanchardstown, Blanchardstown Road North Dublin 15 (2015).
  8. Riedmiller, Martin, and Heinrich Braun. "A direct adaptive method for faster backpropagation learning: The RPROP algorithm." In Neural Networks, 1993., IEEE International Conference on, pp. 586-591. IEEE, 1993.
  9. Dorigo, Marco, Vittorio Maniezzo, and Alberto Colorni. "Ant system: optimization by a colony of cooperating agents." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 26, no. 1 (1996): 29-41.
  10. Dorigo, Marco, and Mauro Birattari. "Ant colony optimization." In Encyclopedia of machine learning, pp. 36-39. Springer, Boston, MA, 2011.
  11. Birattari, Marco Dorigo Mauro, Christian Blum Luca M. Gambardella, and Francesco Mondada Thomas Stützle. "Ant Colony Optimization and Swarm Intelligence." Lecture Notes in Computer ScienceSpringer, Berlin, pp 3748Maniezzo V, Sttzle T, Vo S (eds) Matheuristicshybridizing metaheuristics and mathematical programming, Annals of information systems 10.
  12. Dorigo, Marco, and Christian Blum. "Ant colony optimization theory: A survey." Theoretical computer science 344, no. 2-3 (2005): 243-278.
  13. Mahanti, P. K., and Soumya Banerjee. "Automated testing in software engineering: using ant colony and self-regulated swarms." In Proceedings of the 17th IASTED international conference on Modelling and simulation (MS’06), pp. 443-448. 2006.
  14. Banerjee, Srideepa, Akanksha Bharadwaj, Daya Gupta, and V. K. Panchal. "Remote sensing image classification using Artificial Bee Colony algorithm." International Journal of Computer Science and Informatics 2, no. 3 (2012): 67-72.

Publication Details

Published in : Volume 1 | Issue 3 | November-December 2016
Date of Publication : 2016-12-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 76-81
Manuscript Number : CSEIT1833752
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Asha Rathee, "Satellite Image Classification using Ant Colony Optimization and Neural Network", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1, Issue 3, pp.76-81, November-December.2016

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