Support vector reduction in svm algorithm for abrupt change detection in remote sensing

IEEE Geoscience and Remote Sensing Letters

Volumn 6, Issue 3, 2009, Pages 606-610

  Habib, Tarek ^a,b   Inglada, Jordi ^a   Mercier, Grégoire ^c   Chanussot, Jocelyn ^b  

^a Centre National d'Tudes Spatiales   (France)

^b GIPSA LAB   (France)

^c ECOLE DES MINES DE NANTES   (France)

Author keywords

Image classification; Image matching; Image processing; Remote sensing

Indexed keywords

ABRUPT CHANGE DETECTION; CHANGE DETECTION; CLASSIFICATION TIME; DECISION FUNCTIONS; REMOTE SENSING IMAGERY; RISK MANAGEMENT APPLICATION; SUPPORT VECTOR; SUPPORT VECTOR MACHINE ALGORITHM; SVM ALGORITHM; SVM CLASSIFICATION; TIME-CONSUMING TASKS;

ALGORITHMS; IMAGE MATCHING; IMAGING SYSTEMS; REMOTE SENSING; RISK ANALYSIS; RISK MANAGEMENT; SATELLITE IMAGERY; SIGNAL DETECTION; SUPPORT VECTOR MACHINES;

IMAGE CLASSIFICATION;

EID: 67651172772     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2009.2020306     Document Type: Article

References (9)

1. T. Habib, J. Chanussot, J. Inglada, and G. Mercier, "Abrupt change detection on multitemporal remote sensing images: A statistical overview of methodologies applied on real cases," in Proc. IEEE IGARSS, Barcelona, Spain, Jul. 2007, pp. 2593-2596.
2. N. Ghoggali and F. Melgani, "Genetic SVM approach to semisupervised multitemporal classification," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 2, pp. 212-216, Apr. 2008.
3. C. J. C. Burges, "Simplified support vector decision rules," in Proc. Int. Conf. Mach. Learn., 1996, pp. 71-77.
4. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
5. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
6. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, U.K.: Cambridge Univ. Press, 2000.
7. B. Schlkopf and A. J. Smola, Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning). Cambridge, MA: MIT Press, 2001.
8. D. H. Johnson and S. Sinanovic, "Symmetrizing the Kullback-Leibler distance," Rice Univ., Center for Multimedia Communication, Houston, TX, 2001. Tech. Rep.
9. S. Kullback and R. A. Leibler, "On information and sufficiency," Ann. Math. Statist., vol. 22, no. 1, pp. 79-86, 1951.