Classification of high resolution urban satellite images combining SVM and graph cuts

2010 5th International Symposium on I/V Communications and Mobile Networks, ISIVC 2010

Volumn , Issue , 2010, Pages

  Bekkari, A ^a   Idbraim, S ^a   Housni, K ^a   Mammass, D ^a   Chahir, Y ^b  

^a FACULTÉ DES SCIENCES   (Morocco)

^b UNIVERSITÉ DE CAEN   (France)

Author keywords

Graph cuts; Satellite image; Space and spectral information; SVM

Indexed keywords

AS GRAPH; GRAPH CUT; HIGH RESOLUTION; REMOTELY SENSED IMAGES; SATELLITE IMAGE; SATELLITE IMAGES; SPATIAL DOMAINS; SPECTRAL INFORMATION; SVM; SVM ALGORITHM; SVM CLASSIFICATION;

REMOTE SENSING; SATELLITES; WIRELESS NETWORKS;

GRAPHIC METHODS;

EID: 78651486901     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISVC.2010.5656433     Document Type: Conference Paper

References (9)

1. Christophe SAMSON "Contribution à la classification des images satellitaires par approche variationnelle et équations aux dérivées partielles": Thesis of doctorate, university of Nice-Sophia Antipolis.
2. M. Fauvel, J. A. Benediktsson, J. Chanussot and J. R. Sveinsson "Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles" IEEE International Geoscience and Remote Sensing Symposium, IGARSS 07, July 2007, Barcelona Spain.
3. Laetitia Chapel "Maintenir la viabilité ou la résilience d'un système: les machines à vecteurs de support pour rompre la malédiction de la dimensionnalité?": Thesis of doctorate, university of Blaise Pascal -Clermont II.
4. Sujeevan ASEERVATHAM "Apprentissage à base de Noyaux Sémantiques pour le Traitement de Données Textuelles": Thesis of doctorate, university of Paris 13 -Galilée Institut Laboratory of Data processing of Paris Nord.
5. Olivier Bousquet, "Introduction au Support Vector Machines (SVM)", Center mathematics applied, polytechnique school of Palaiseau, 2001. http://www.math.u-psud.fr/~blanchard/gtsvm/index.html.
6. Anton Schwaighofer "Support Vector Machine toolbox Version 2.51 (January 2002) Institute for Theoretical Computer Science of Graz in Austria. http://www.princeton.edu/~kung/ele571/571-MatLab/571svm/
7. SVMlight Version: 6.02 Developed at University of Dortmund, Informatik, AI-Unit Collaborative Research Center on 'Complexity Reduction in Multivariate Data' (SFB475). http://svmlight.joachims.org/
8. Boykov, Y., Funka-Lea, G.: Graph Cuts and Efficient N-D Image Segmentation. International Journal of Computer Vision 70(2), 109-131 2006.
9. Y. Boykov, O. Veksler et R. Zabih, Fast approximate energy minimization via graph cuts. IEEE International Conference on Computer Vision, volume 1, pages 377-384, septembre 1999.