Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach

Résumé

Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly con-catenated into a long vector (also called stacked vector), on which is applied a conventional vector-based machine learning technique (e.g. SVM with Gaussian kernel). In this paper , we rather propose to use a sequence structured kernel: the spectrum kernel. We show that the conventional stacked vector-based kernel is actually a special case of this kernel. Experiments conducted on various publicly available hyper-spectral datasets illustrate the improvement of the proposed kernel w.r.t. conventional ones using the same hierarchical spatial features.
Fichier principal
Vignette du fichier
whispers2016.pdf (320.43 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01320012 , version 1 (13-11-2019)

Identifiants

  • HAL Id : hal-01320012 , version 1

Citer

Yanwei Cui, Laëtitia Chapel, Sébastien Lefèvre. Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach. Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016, Los Angeles, United States. ⟨hal-01320012⟩
154 Consultations
61 Téléchargements

Partager

Gmail Facebook X LinkedIn More