Parameters Selection Of Morphological Scale-Space Decomposition For Hyperspectral Images Using Tensor Modeling - Mines Paris Accéder directement au contenu
Communication Dans Un Congrès Année : 2010

Parameters Selection Of Morphological Scale-Space Decomposition For Hyperspectral Images Using Tensor Modeling

Jesus Angulo

Résumé

Dimensionality reduction (DR) using tensor structures in morphological scale-space decomposition (MSSD) for HSI has been investigated in order to incorporate spatial information in DR.We present results of a comprehensive investigation of two issues underlying DR in MSSD. Firstly, information contained in MSSD is reduced using HOSVD but its nonconvex formulation implicates that in some cases a large number of local solutions can be found. For all experiments, HOSVD always reach an unique global solution in the parameter region suitable to practical applications. Secondly, scale parameters in MSSD are presented in relation to connected components size and the influence of scale parameters in DR and subsequent classification is studied.
Fichier principal
Vignette du fichier
VelascoAngulo_SPIE10.pdf (805.32 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00834484 , version 1 (15-06-2013)

Identifiants

Citer

Santiago Velasco-Forero, Jesus Angulo. Parameters Selection Of Morphological Scale-Space Decomposition For Hyperspectral Images Using Tensor Modeling. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, May 2010, Orlando, United States. 12 p., ⟨10.1117/12.850171⟩. ⟨hal-00834484⟩
183 Consultations
165 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More