E. Chuvieco, Wildland Fire Danger Estimation and Mapping: The role of Remote Sensing data, Remote Sensing. World Scientific, vol.4, 2003.

P. Barbosa, A. J. San-miguel, B. Martinez, and G. Schmuck, Burnt area mapping in Southern Europe using IRS-WiFS, 2002.

O. Zammit, X. Descombes, and J. Zerubia, Support vector machines for burnt area discrimination, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00185101

V. Vapnik, Statistical Learning Theory. JohnWiley and sons, inc, 1998.

F. Melgani and L. Bruzzone, Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.8, pp.1778-1790, 2004.
DOI : 10.1109/TGRS.2004.831865

B. Schölkopf, J. C. Platt, J. C. Shawe-taylor, A. J. Smola, and R. C. Williamson, Estimating the Support of a High-Dimensional Distribution, Neural Computation, vol.6, issue.1, pp.1443-1471, 2001.
DOI : 10.1214/aos/1069362732

L. Vincent and P. Soille, Watersheds in digital spaces: an efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.6, pp.583-598, 1991.
DOI : 10.1109/34.87344

J. Canny, A computational approach to edge detection. Pattern Analysis and Machine Intelligence, pp.679-698, 1986.

Y. Fang, C. Pan, L. Liu, and L. Fang, Fast Training of SVM via Morphological Clustering for Color Image Segmentation, International Conference on Intelligent Computing, pp.263-271, 2005.
DOI : 10.1007/11538059_28

B. Schölkopf, K. Tsuda, and J. Vert, Kernel Methods in computational biology, 2004.

P. Soille, Morphological Image Analysis: Principles and Applications, 1999.

G. Rätsch, B. Schölkopf, S. Mika, and K. Müller, SVM and boosting: One class, 2000.