Mahalanobis kernel for the classification of hyperspectral images

Mathieu Fauvel 1 Alberto Villa 2, 3 Jocelyn Chanussot 4 Jon Atli Benediktsson 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
4 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
Abstract : The definition of the Mahalanobis kernel for the classification of hyperspectral remote sensing images is addressed. Class specific covariance matrices are regularized by a probabilistic model which is based on the data living in a subspace spanned by the p first principal components. The inverse of the covariance matrix is computed in a closed form and is used in the kernel to compute the distance between two spectra. Each principal direction is normalized by a hyperparameter tuned, according to an upper error bound, during the training of an SVM classifier. Results on real data sets empirically demonstrate that the proposed kernel leads to an increase of the classification accuracy by comparison to standard kernels.
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Communication dans un congrès
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010), Jul 2010, Honolulu, Hawaii, United States. IEEE, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010), pp.3724-3727, 2010, 〈10.1109/IGARSS.2010.5651956〉
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Mathieu Fauvel, Alberto Villa, Jocelyn Chanussot, Jon Atli Benediktsson. Mahalanobis kernel for the classification of hyperspectral images. IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010), Jul 2010, Honolulu, Hawaii, United States. IEEE, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010), pp.3724-3727, 2010, 〈10.1109/IGARSS.2010.5651956〉. 〈hal-00578952〉

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