Abstract
In this paper, a land-cover extraction thematic mapping approach for urban areas from very high resolution aerial images is presented. Recent developments in the field of sensor technology have increased the challenges of interpreting images contents particularly in the case of complex scenes of dense urban areas. The major objective of this study is to improve the quality of land-cover classification. We investigated the use of multiple classifier systems (MCS) based on dynamic classifier selection. The selection scheme consists of an ensemble of weak classifiers, a trainable selector, and a combiner. We also investigated the effect of using Particle Swarm Optimization (PSO) based classifier as the base classifier in the ensemble module, for the classification of such complex problems. A PSO-based classifier discovers the classification rules by simulating the social behaviour of animals. We experimented with the parallel ensemble architecture wherein the feature space is divided randomly among the ensemble and the selector. We report the results of using separate/similar training sets for the ensemble and the selector, and how each case affects the global classification error. The results show that selection improves the combination performance compared to the combination of all classifiers with a higher improvement when using different training set scenarios and also shows the potential of the PSO-based approach for classifying such images.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28(5), 823–870 (2007)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, New York (2001)
Stathakis, D., Vasilakos, A.: Comparison of computational intelligence based classification techniques for remotely sensed optical image classification. IEEE T. Geosci. Remote Sens. 44(8), 2305–2318 (2006)
Omran, M., Engelbrecht, A.P., Salman, A.: Particle swarm optimization method for image clustering. International Journal of Pattern Recognition and Artificial Intelligence 19(3), 297–321 (2005)
XiaoPing, L., Xia, L., XiaoJuan, P., HaiBo, L., JinQiang, H.: Swarm intelligence for classification of remote sensing data. Science in China Series D: Earth Sciences 51(1), 79–87 (2008)
Benediktsson, J.A., Chanussot, J., Fauvel, M.: Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 501–512. Springer, Heidelberg (2007)
Yu-Chang, T., Kun-Shan, C.: An adaptive thresholding multiple classifiers system for remote sensing image classification. Photogrammetry Engineering and Remote Sensing 75(6), 679–687 (2009)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Chichester (2004)
Giacinto, G., Roli, F.: Dynamic classifier selection. In: Proceedings of the First International Workshop on Multiple Classifier Systems, pp. 177–189 (2000)
Wanas, N., Dara, R., Kamel, M.S.: Adaptive Fusion and Co-operative Training for Classifier Ensembles. Pattern Recognition 39(9), 1781–1794 (2006)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks (ICNN 1995), Australia, vol. 4, pp. 1942–1948. IEEE Service Center, Perth (1995)
Sousa, T., Neves, A., Silva, A.: A particle swarm data miner. In: 11th Portuguese Conference on Artificial Intelligence, Workshop on Artificial Life and Evolutionary Algorithms, pp. 43–53 (2003)
Tri-Cities and Surrounding Communities Orthomosaics 2006 [computer file]. Waterloo, Ontario: The Regional Municipality of Waterloo (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bedawi, S.M., Kamel, M.S. (2011). Multiple Classifier System for Urban Area’s Extraction from High Resolution Remote Sensing Imagery. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-21596-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21595-7
Online ISBN: 978-3-642-21596-4
eBook Packages: Computer ScienceComputer Science (R0)