Skip to Main content Skip to Navigation
Theses

Robust classifcation methods on the space of covariance matrices. : application to texture and polarimetric synthetic aperture radar image classification

Abstract : In the recent years, covariance matrices have demonstrated their interestin a wide variety of applications in signal and image processing. The workpresented in this thesis focuses on the use of covariance matrices as signatures forrobust classification. In this context, a robust classification workflow is proposed,resulting in the following contributions.First, robust covariance matrix estimators are used to reduce the impact of outlierobservations, during the estimation process. Second, the Riemannian Gaussianand Laplace distributions as well as their mixture model are considered to representthe observed covariance matrices. The k-means and expectation maximization algorithmsare then extended to the Riemannian case to estimate their parameters, thatare the mixture's weight, the central covariance matrix and the dispersion. Next,a new centroid estimator, called the Huber's centroid, is introduced based on thetheory of M-estimators. Further on, a new local descriptor named the RiemannianFisher vector is introduced to model non-stationary images. Moreover, a statisticalhypothesis test is introduced based on the geodesic distance to regulate the classification false alarm rate. In the end, the proposed methods are evaluated in thecontext of texture image classification, brain decoding, simulated and real PolSARimage classification.
Document type :
Theses
Complete list of metadatas

https://tel.archives-ouvertes.fr/tel-01511645
Contributor : Abes Star :  Contact
Submitted on : Friday, April 21, 2017 - 2:05:09 PM
Last modification on : Wednesday, January 31, 2018 - 4:57:09 AM
Long-term archiving on: : Saturday, July 22, 2017 - 1:00:43 PM

File

ILEA_IOANA_2017_CORR.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-01511645, version 1

Citation

Ioana Ilea. Robust classifcation methods on the space of covariance matrices. : application to texture and polarimetric synthetic aperture radar image classification. Other. Université de Bordeaux, 2017. English. ⟨NNT : 2017BORD0006⟩. ⟨tel-01511645⟩

Share

Metrics

Record views

469

Files downloads

377