Kohonen-Based Credal Fusion of Optical and Radar Images for Land Cover Classification - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

Kohonen-Based Credal Fusion of Optical and Radar Images for Land Cover Classification

Imen Hammami
  • Fonction : Auteur
  • PersonId : 1036041
Grégoire Mercier
  • Fonction : Auteur
  • PersonId : 1032190

Résumé

This paper presents a Credal algorithm to perform land cover classification from a pair of optical and radar remote sensing images. SAR (Synthetic Aperture Radar) /optical multispectral information fusion is investigated in this study for making the joint classification. The approach consists of two main steps: 1) relevant features extraction applied to each sensor in order to model the sources of information and 2) a Kohonen map-based estimation of Basic Belief Assignments (BBA) dedicated to heterogeneous data. This framework deals with co-registered images and is able to handle complete optical data as well as optical data affected by missing value due to the presence of clouds and shadows during observation. A pair of SPOT-5 and RADARSAT-2 real images is used in the evaluation, and the proposed experiment in a farming area shows very promising results in terms of classification accuracy and missing optical data reconstruction when some data are hidden by clouds.
Fichier principal
Vignette du fichier
DTIS19145.1566560996_preprint.pdf (8.08 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02335697 , version 1 (28-10-2019)

Identifiants

Citer

Imen Hammami, Jean Dezert, Grégoire Mercier. Kohonen-Based Credal Fusion of Optical and Radar Images for Land Cover Classification. FUSION 2018, Jul 2018, CAMBRIDGE, United Kingdom. pp.1623-1630, ⟨10.23919/ICIF.2018.8455272⟩. ⟨hal-02335697⟩
24 Consultations
30 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More