Mixed spectral-structural classification of very high resolution images with summation kernels
Détails
ID Serval
serval:BIB_EFDDDDA9D497
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Mixed spectral-structural classification of very high resolution images with summation kernels
Titre de la conférence
Proceedings of SPIE: Image and signal processing for remote sensing XIV, Dresden, Germany, 23 - 26 September
Statut éditorial
Publié
Date de publication
2008
Editeur⸱rice scientifique
Bruzzone L., Notarnicola C., Posa F.
Volume
7109
Pages
pp. 10
Langue
anglais
Notes
Tuia2008f
Résumé
In this paper, mixed spectral-structural kernel machines are proposed
for the classification of very-high resolution images. The simultaneous
use of multispectral and structural features (computed using morphological
filters) allows a significant increase in classification accuracy
of remote sensing images. Subsequently, weighted summation kernel
support vector machines are proposed and applied in order to take
into account the multiscale nature of the scene considered. Such
classifiers use the Mercer property of kernel matrices to compute
a new kernel matrix accounting simultaneously for two scale parameters.
Tests on a Zurich QuickBird image show the relevance of the proposed
method : using the mixed spectral-structural features, the classification
accuracy increases of about 5%, achieving a Kappa index of 0.97.
The multikernel approach proposed provide an overall accuracy of
98.90% with related Kappa index of 0.985.
for the classification of very-high resolution images. The simultaneous
use of multispectral and structural features (computed using morphological
filters) allows a significant increase in classification accuracy
of remote sensing images. Subsequently, weighted summation kernel
support vector machines are proposed and applied in order to take
into account the multiscale nature of the scene considered. Such
classifiers use the Mercer property of kernel matrices to compute
a new kernel matrix accounting simultaneously for two scale parameters.
Tests on a Zurich QuickBird image show the relevance of the proposed
method : using the mixed spectral-structural features, the classification
accuracy increases of about 5%, achieving a Kappa index of 0.97.
The multikernel approach proposed provide an overall accuracy of
98.90% with related Kappa index of 0.985.
Création de la notice
25/11/2013 18:18
Dernière modification de la notice
20/08/2019 17:17
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