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Statistical hypothesis test for robust classification on the space of covariance matrices

Abstract : This paper introduces a new statistical hypothesis test for robust image classification. First, we introduce the proposed statistical hypothesis test based on the geodesic distance and on the fixed point estimation algorithm. Next, we analyze its properties in the case of the zero-mean multivariate Gaus-sian distribution by studying its asymptotic distribution under the null hypothesis H0. Then, the performance of the proposed classifier is addressed by analyzing its noise robust-ness. Finally, the robust classification method is employed for the classification of simulated Polarimetric Synthetic Aperture Radar images of maritime pine forests.
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https://hal.archives-ouvertes.fr/hal-01228770
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Submitted on : Friday, November 13, 2015 - 5:13:55 PM
Last modification on : Monday, November 26, 2018 - 1:30:03 PM
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  • HAL Id : hal-01228770, version 1

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Ioana Ilea, Lionel Bombrun, Christian Germain, Romulus Terebes, Monica Borda. Statistical hypothesis test for robust classification on the space of covariance matrices. IEEE International Conference on Image Processing (ICIP), Sep 2015, Québec, Canada. ⟨hal-01228770⟩

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