Open Access Open Access  Restricted Access Subscription or Fee Access

Hybrid Approach for the Detection of Regions of a Satellite Image

Sarah Gherdaoui(1*), Hadria Fizazi(2)

(1) University of Science and Technology of Oran ‘Mohamed Boudiaf’, Computer Science Department, Faculty of Computer Science and Mathematics, Algeria
(2) University of Science and Technology of Oran ‘Mohamed Boudiaf’, Computer Science Department, Faculty of Computer Science and Mathematics, Algeria
(*) Corresponding author



The search and detection of the different regions constituting an image is a problem of great complexity and the use of the approximation algorithms is inevitable. For this purpose, several algorithms have been applied. Among the latter, we are interested in bio-inspiration by hybridizing two algorithms: Artificial Immunity Systems (AIS) with Evolutionary Algorithms (AE) in order to benefit from the good codification of the immune systems and the variation of the operators of the evolutionary algorithms, ensuring that the entire population is the solution. The main interests of this hybridization are to minimize the size of the representation of the individuals and to accelerate the convergence.
Copyright © 2017 Praise Worthy Prize - All rights reserved.


Image; Evolutionary Algorithm; Detection; Hybrid Approach; Remote Sensing

Full Text:



L. Wald, Methods of processing satellite imagery and their application to underwater work, ESA, SP 280, page.349-351, 1988.

Kavita, R. Saroha, R. Bala, S. Siwach, Review paper on overview of image processing and image segmentation, International Journal of Research in Computer Applications and Robotics, issn 2320-7345, volume 1, issue.7, page: 1-13 , october 2013.

W. Burger, M. J. Burge, Digital Image Processing An algorithmic introduction using Java, Springer-Verlag New York, ISBN 978-1-84628-379-6, 2008.

J. B. MacQueen, Some methods for classification analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Page 281-297, University of California Press, 1967.

J. R. Jensen, Introductory Digital Image Processing - A Remote Sensing Perspective, (Prentice Hall, Inc., New Jersey, pp. 197-256, 1996).

K. J. Cios, W. Pedryecz, R. W. Swinniarsky, L. A. Kurgan, Data Mining : A Knowledge Discovery Approach, Editions Springer Science, ISBN 978-0-387-36795-8, 2007.

I. Boussaïd, Improvement of metaheuristics for continuous optimization, Ph.D. dissertation, ED 532, University of Paris-East Créteil, June 2013.

S. Kirkpatrick, C. Gelatt, M. Vecchi, Optimization by Simulated Annealing, Science, volume 220, n° 4598, page 671-680. 1983.

S. Chu, J. F. Roddick, A clustering algorithm using the Tabu Search approach with Simulated Annealing, Data Mining II Proceedings of Second International Conference on Data Mining Methods and Databases, Adelaïde, Australie. 2000.

A. Babalola, R. Belkacemi, S. Zarrabian, R. Craven, Adaptive Immune System reinforcement Learning-Based algorithm for real-time Cascading Failures prevention, Engineering Applications of Artificial Intelligence, Elsevier, Volume 57, Page 118–133, January 2017.

J. Timmis, A. Hone , T. Stibor, E. Clark, Theoretical advances in artificial immune systems, Theoretical Computer Science, Elsevier Volume 403, Issue 1, Page 11-32, August 2008.

Y. Zhong, L. Zhang, An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery, IEEE Transactions On Geoscience And Remote Sensing, Volume 50, Issue: 3, March 2012 .

A. Benyamina, Application of ant colonization algorithms for optimization and classification of images, Ph.D. dissertation, departement Computer Science, University of Science and Technology of Oran ‘Mohamed Boudiaf, Algeria, april 2013.

Hannane, A., Fizazi, H., Metaheuristics and Neural Network for Satellite Images Classification, (2016) International Review of Aerospace Engineering (IREASE), 9 (4), pp. 107-113.

O. Timothy, Application of genetic algorithm to image segmentation: a review, International Journal of Information Research and Review, Volume. 03, Issue, 10, page 2908-2912, October, 2016.

C. Darwin, On the origin of species by means of natural selection or the preservation of favored races in the struggle for life, (Murray, Londre, 1859).

J. H. Holland, Adaptation in natural and artifical systems, Ann Arbor (University of Michigan Press, page 183, 1975).

F.Y. Yang, P. Lohmann, C. Heipke, Genetic Algorithms For Multi‐Spectral Image Classification, Institute of Photogrammetry and GeoInformation, Leibniz University of Hannover, 2008.

N. Benyahia, W. Rebhi, N.B. Bensaoud, H. Benghezela, Hybrid approach to recommending new collaborations, INFORSID, page 285-299, 2015

J. E. Gallardo, C. Cotta, A. J. Fernandez, On the hybridization of memetic algorithms with branch-and-bound techniques, IEEE Transactions on Systems, Man and Cybernetics – Part B, volume 37(1), page 77–83, 2007.

M. Sandeli, M. Batouche, , Multilevel thresholding for image segmentation based on parallel distributed optimization, 6th International Conference, Soft Computing and Pattern Recognition (SoCPaR), page 134-139, August 2014.

C. Vanaret, Hybridization of evolutionary algorithms and interval methods for the optimization of difficult problems, Ph.D. dissertation, Institut National Polytechnique de Toulouse , January 2015.

S. Bandyopadhyay, U. Maulik, Genetic clustering for automatic evolution of clusters and application to image classification, IEEE Pattern Recognition, volume 35, page 1197-1208, 2012.

J. Louchet, M. Guyon, M-J. Lesot, A.Boumaza, The algorithm of dynamic flies: guide a robot by artificial evolution in real time, Knowledge Extraction and Learning: Learning and Evolution, volume.1– N.3. Page 115-130, 2001.

J. Dréo, A. Petrowski, P. Siarry, E. Taillard, Metaheuristics for difficult optimization, (Eyrolles Edition, 2005).

D. Izzo, M. Rucinski And F. Biscani, The Generalized Island Model, Parallel Architectures & Bioinspired Algorithms, SCI 415, page. 151–169. Springer-Verlag Berlin Heidelberg 2012.

M. Ren, P. Liu, Z. Wang, J. Yi, A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters, Computational Intelligence and Neuroscienc, Volume 2016, Article ID 2647389, October 2016.

M.S. Yang, K.L. Wu, A new validity index for fuzzy clustering, IEEE International Fuzzy Systems Conference, page.89-92, 2001.

M. Yang, Y. Yang, T. Su, K. Huang, An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images, Scientific World Journal Volume 2014, Article ID 264512, February 2014.

C. E. Bichot, Elaboration of a new metaheuristic for the partitioning of graph: the fusion-fission method, Application to the division of the airspace, Ph.D. dissertation, National Institute of Technology, Toulouse, 2007.

M. Yaghini, N.Ghazanfari, Tabu-KM: A Hybrid Clustering Algorithm Based on Tabu Search Approach, International Journal of Industrial Engineering & Production Research Volume 21, Number 2, September 2010.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2021 Praise Worthy Prize