Segmentation d’images hyper-spectrales - Hyper-spectral images segmentation

Segmentation d’images hyper-spectrales

Hyper-spectral images segmentation

Robin Girard

Corresponding Author Email: 
robin.girard@imag.fr
Page: 
277-288
|
Received: 
19 December 2005
|
Accepted: 
N/A
|
Published: 
31 August 2006
| Citation

OPEN ACCESS

Abstract: 

We present a new image segmentation algorithm for hyper-spectral images that are supposed to be piecewise constant. The procedure is composed by three steps. The first step (denoising) is inspired by a nonparametric adaptive weights smoothing image restoration procedure based on a growing region type algorithm; the second step uses parameters estimated during the first phase to produce an estimation of the boundaries of the image segmentation. The last step of the algorithm groups the differents areas obtained during the second phase by minimising a penalized empirical squared loss criterion. The segmentation algorithm is then applied to simulated nuclear magnetic resonance data.

Résumé

Nous présentons un algorithme de segmentation d'images hyper-spectrales supposées constantes par régions. Cet algorithme est composé de trois phases. La première est une phase de débruitage inspirée d'une méthode adaptative de lissage pondéré fondée sur une segmentation par croissance de régions, la deuxième se sert des paramètres obtenus lors du débruitage de la première phase et a pour but de produire une estimation des contours des régions connexes issues du débruitage. La dernière étape de l'algorithme consiste à fusionner les régions issues de la deuxième phase en minimisant une version pénalisée de l'erreur quadratique de reconstruction. La méthodologie est illustrée sur des données simulées d'imagerie de résonance magnétique nucléaire.

1. Introduction
2. Généralités Et Notations
3. AWS : Algorithme De Débruitage Et D’estimation Des Poids
4. Segmentation Par Estimation Des Frontières
5. Regroupement Des Zones Par Minimisation De L’erreur Quadratique Empirique Pénalisée
6. Application
7. Conclusion Et Perspectives
A. Choix Des Paramètres De AWS
B. Calcul Et Estimation De E0[M(Γ)]
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