Polarimetric radar image classi cation using directional diffusion and descriptive statistics - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Rev. Roum. Sci. Techn. – Électrotechn. et Énerg Année : 2018

Polarimetric radar image classi cation using directional diffusion and descriptive statistics

Résumé

This paper proposes a novel denoising method for polarimetric synthetic aperture radar (PolSAR) image preprocessing tasks with integration and applications for texture-like land images classification tasks. The method is developed using the partial differential equations framework and employs a multi-polarimetric tensor to capture the geometry of fully polarimetric PolSAR data. The filtering intensity is modulated by the multiplicative gradient norm computed on the total scattered power. The method has good texture preservation properties and it is integrated on a PolSAR image classification chain providing good recognition accuracy rates. Visual results on real PolSAR data of maritime pine forests stands are also provided for showing its effectiveness.
Fichier non déposé

Dates et versions

hal-01930194 , version 1 (21-11-2018)

Identifiants

  • HAL Id : hal-01930194 , version 1

Citer

Romulus Terebes, Raul Malutan, Monica Borda, Christian Germain, Lionel Bombrun, et al.. Polarimetric radar image classi cation using directional diffusion and descriptive statistics. Rev. Roum. Sci. Techn. – Électrotechn. et Énerg, 2018, 63 (1), pp.83-88. ⟨hal-01930194⟩
42 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More