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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

SVM Classification of Urban High-Resolution Imagery Using Composite Kernels and Contour Information

Author 1: Aissam Bekkari
Author 2: Mostafa El yassa
Author 3: Soufiane Idbraim
Author 4: Driss Mammass
Author 5: Azeddine Elhassouny
Author 6: Danielle Ducrot

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2013.040718

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 7, 2013.

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Abstract: The classification of remote sensing images has done great forward taking into account the image’s availability with different resolutions, as well as an abundance of very efficient classification algorithms. A number of works have shown promising results by the fusion of spatial and spectral information using Support Vector Machines (SVM) which are a group of supervised classification algorithms that have been recently used in the remote sensing field, however the addition of contour information to both spectral and spatial information still less explored. For this purpose we propose a methodology exploiting the properties of Mercer’s kernels to construct a family of composite kernels that easily combine multi-spectral features and Haralick texture features as data source. The composite kernel that gives the best results will be used to introduce contour information in the classification process. The proposed approach was tested on common scenes of urban imagery. The three different kernels tested allow a significant improvement of the classification performances and a flexibility to balance between the spatial and spectral information in the classifier. The experimental results indicate a global accuracy value of 93.52%, the addition of contour information, described by the Fourier descriptors, Hough transform and Zernike moments, allows increasing the obtained global accuracy by 1.61% which is very promising.

Keywords: SVM; Contour information; Composite Kernels; Haralick features; Satellite image; Spectral and spatial information; GLCM; Fourier descriptors; Hough transform; Zernike moments.

Aissam Bekkari, Mostafa El yassa, Soufiane Idbraim, Driss Mammass, Azeddine Elhassouny and Danielle Ducrot, “SVM Classification of Urban High-Resolution Imagery Using Composite Kernels and Contour Information” International Journal of Advanced Computer Science and Applications(IJACSA), 4(7), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040718

@article{Bekkari2013,
title = {SVM Classification of Urban High-Resolution Imagery Using Composite Kernels and Contour Information},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040718},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040718},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {7},
author = {Aissam Bekkari and Mostafa El yassa and Soufiane Idbraim and Driss Mammass and Azeddine Elhassouny and Danielle Ducrot}
}


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