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Communication Dans Un Congrès Année : 2012

A robust evidential fisher discriminant for multi-temporal images classification

Résumé

This paper develops a noise-robust processing method which can be used to enhance the classification of remotely sensed hyperspectral images. The method first illustrates the benefit of boosting the classical classifiers by exploiting the capability of belief functions. The evidential approach is adopted to produce a map which is approximately insensitive to the noise accompanying the original hyperspectral data-set. Then, a new Evidential Kernel Fisher Discriminant is proposed by using a modified version of the Expectation-Maximization (EM) algorithm. An experimental comparison of the proposed approach with other classical methods is conducted using both synthetic and real hyperspectral data collected by the HYPERION sensor. Our experiments reveal that both classification and unmixing process can benefit from the proposed aggregated approach, remarkably, when the noise level present in the original hyperspectral series is propositionally high.
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Dates et versions

hal-00945421 , version 1 (12-02-2014)

Identifiants

Citer

Salim Hemissi, Imed Riadh Farah, Karim Saheb Ettabaa, Basel Solaiman. A robust evidential fisher discriminant for multi-temporal images classification. IGARSS 2012: IEEE International Geoscience and Remote Sensing Symposium, Jul 2012, Munich, Germany. pp.4275-4278, ⟨10.1109/IGARSS.201206351723⟩. ⟨hal-00945421⟩
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