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A Dual-Level Hybrid Approach for Classification of Satellite Images

Mustapha Si Tayeb(1*), Hadria Fizazi(2)

(1) Department of Computer Science, Faculty of Mathematics and Computer Science, Université des Sciences et de la Technologie d’Oran - Mohamed boudiaf (USTO-MB), Algeria
(2) Department of Computer Science, Faculty of Mathematics and Computer Science, Université des Sciences et de la Technologie d’Oran - Mohamed boudiaf (USTO-MB), Algeria
(*) Corresponding author


DOI: https://doi.org/10.15866/irease.v10i1.11191

Abstract


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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Keywords


Neural Network; Markov Models; Genetic Algorithms; Supervised Classification; Hybrid System; Satellite Images; Remote Sensing

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References


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