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A Dual-Level Hybrid Approach for Classification of Satellite Images

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The traditional methods for extracting information from satellite images are generally based on the spectral response of the sensors. These approaches are in some cases insufficient in particular in case of high-resolution images. In fact these images have a spectral content increasingly heterogeneous. It is, therefore, necessary to use more efficient analysis methods. The multi-source classification is a robust analytical tool, and it is one of the most used approaches for the extraction of telemetric information. This paper is focused on the problem of the classification of satellite images by the hybridization of several methods: multi-layer perceptron, hidden Markov models and genetic algorithms. The results prove the efficiency of the proposed final approach, with a classification rate of 98.79%, significantly higher respect to the results obtained by the MLP method, and by other approaches.
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Neural Network; Markov Models; Genetic Algorithms; Supervised Classification; Hybrid System; Satellite Images; Remote Sensing

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H. Bischof, W. Schneider, and A. J. Pinz, Multispectral Classification of Landsat-Images Using Neural Networks, IEEE Transactions on Geoscience and Remote Sensing, (1992) vol. 30, Issue 3, pp. 482 – 490.

B. L. Markham and D. L. Helder, Forty-year calibrated record of earthreflected radiance from Landsat: A review, Remote Sens. Environ, (2012) vol. 122, pp. 30–40.

F. Melgani and L. Bruzzone, Classification of Hyperspectral Remote Sensing Images With Support Vector Machines, IEEE Transactions on Geoscience and Remote Sensing, (2004) vol. 42, Issue 8, pp. 1778-1790.

A. N. S. Njikam and H. Zhao, A novel activation function for multilayer feed forward neural networks, Springer Applied Intelligence, (2016) vol. 45, Issue 1, pp 75-82.

P. D. Heermann and N. Khazenie,Classification of Multispectral Remote Sensing Data Using a Back-Propagation Neural Network, IEEE Transactions on Geoscience and Remote Sensing, (1992) vol. 30, Issue 1, pp. 81-88.

Attaf, Y., Adane, A., Lahdir, M., Boudraa, A., Laghrouche, M., Ameur, Z., An AM-FM Based Image Segmentation: Detection of Clouds in MSG Images of Algeria, (2015) International Review on Computers and Software (IRECOS), 10 (7), pp. 789-797.

Kassem, A., El-Bayoumi, G., Habib, T., Kamalaldin, K., Improving Satellite Orbit Estimation Using Commercial Cameras, (2015) International Review of Aerospace Engineering (IREASE), 8 (5), pp. 174-178.

Shouman, M., El Bayoumi, G., Adaptive Robust Control of Satellite Attitude System, (2015) International Review of Aerospace Engineering (IREASE), 8 (1), pp. 35-42.

Lakshmi, M., Prasad, S., Rahman, M., Efficient Speckle Noise Reduction Techniques for Synthetic Aperture Radars in Remote Sensing Applications, (2016) International Review of Aerospace Engineering (IREASE), 9 (4), pp. 114-122.

Bavirisetti, D., Dhuli, R., Multi Sensor Image Fusion Using Saliency Map Detection, (2015) International Review on Computers and Software (IRECOS), 10 (7), pp. 757-763.ù

A. Gasmi, H. Zouari, A. Masse, and D. Ducrot, Potential of the Support Vector Machine (SVMs) for clay and calcium carbonate content classification from hyperspectral remote sensing, International Journal of Innovation and Applied Studies, (2015) vol. 13, pp. 497-506.

Mohia, Y., Ameur, S., Lazri, M., Brucker, J., Rainfall Intensity Classification Method Based on Textural and Spectral Parameters from MSG-SEVIRI, (2014) International Review on Computers and Software (IRECOS), 9 (7), pp. 1302-1313.

S. F. Abdullah, A. F. N. A. Rahman, Z. A. Abas and W. H. M. Saad, Multilayer Perceptron Neural Network in Classifying Gender using Fingerprint Global Level Features, Indian Journal of Science and Technology, (2016) vol. 9, Issue 9.

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

M. Govindarajan and RM. Chandrasekaran, Intrusion detection using neural based hybrid classification methods, Computer Networks: The International Journal of Computer and Telecommunications Networking, (2011) vol. 55, Issue 8, pp. 1662-1671.

A. Fadzil, M. N. Ashidi, H. Zakaria and O. M. Khusairi, Intelligent Medical Disease Diagnosis Using Improved Hybrid Genetic Algorithm - Multilayer Perceptron Network, Journal of Medical Systems, (2013) vol. 37, N. 8.

N. NEGGAZ and A. BENYETTOU, Hybrid models based on biological approaches for speech recognition, Artificial Intelligence Review, (2009) vol. 32, pp. 45-57.

A. E. Hassanien, H. M. Moftah, A. T. Azar and M. Shoman, MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier, Applied Soft Computing, (2014) vol.14, pp.62-71.

C. Persello, A. Boularias, M. Dalponte, T. Gobakken, E. Næsset, and B. Schölkopf, Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification, IEEE Transactions on Geoscience and Remote Sensing, (2014) vol. 52, pp. 6652 – 6664.

Z. Mitraka, F. D. Frate and F. Carbone, Nonlinear Spectral Unmixing of Landsat Imagery for Urban Surface Cover Mapping, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, (2016) vol. 9, Issue 7, pp. 3340 - 3350.

L. Feng and W. Hong, Classification error of multilayer perceptron neural networks, Neural Computing and Applications, (2009) vol. 18, pp. 377–380.

Darwin, Charles, Effects of the increased Use and Disuse of Parts, as controlled by Natural Selection, (John Murray,‎ 1872,p. 108).

Eiben AE, Smith JE, Introduction to evolutionary computing. Springer, (Berlin, 2003).

Goldberg D, Genetic algorithms in search, optimization and machine learning, (Addison Wesley, Massachusetts,1989).

J.P.Cocquerez et S.Philipp, Image analysis: filtering and segmentation, (Elsevier- MASSON, PARIS,1995).

M.Debyeche, et J.P.Haton, Automatic Speech Recognition by Hidden Markov Model with Distributed Neural Vector Quantification, Traitement et Analyse de l'Information: Méthodes et Applications (TAIMA), Tunisia, pp. 273-278, 2005.

B.Benmiloud et W.Pieczynski, Estimation of parameters in hidden Markov chains and image segmentation, (GRETSI, Saint Martin d'Hères, France, 1995, vol. 12).

J.Lacroix, Probability in depth, Course for Master of Mathematics, University of Pierre and Marie Curie. (2005/2006).

J.L.Lions, Small Encyclopedy OF Mathematics. Edition Paris, 1980.

L.Cammoun, S.M’hiri, et F.Ghorbel, Optimization of the EM_HMM algorithm by Bootstrap sampling in the context of unsupervised Bayesian classification, Traitement et Analyse de l'Information: Méthodes et Applications (TAIMA), Tunisia, p. 199-204, 2005.

Y.Zhang, The mean field theory in EM procedures for Markov Random Fields, IEEE Transaction of signal processing, (1992) vol. 40, Issue 10, pp. 2570-2583.

W. Skarbek, Generalized Hilbert scan in image printing, chapitre Theoretical Foundations of Computer Vision, R. Klette and W. G. Kropetsh Akademik Verlag Berlin, 1992.

W.Pieczynski, Markov models in image processing. Signal processing, pp. 255-278, 2003.


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