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Auteur Erchan Aptoula |
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Vector attribute profiles for hyperspectral image classification / Erchan Aptoula in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
[article]
Titre : Vector attribute profiles for hyperspectral image classification Type de document : Article/Communication Auteurs : Erchan Aptoula, Auteur ; Mauro Dalla Mura, Auteur ; Sébastien Lefèvre, Auteur Année de publication : 2016 Article en page(s) : pp 3208 - 3220 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification automatique
[Termes IGN] image hyperspectrale
[Termes IGN] morphologie mathématique
[Termes IGN] niveau de gris (image)
[Termes IGN] vecteur propre
[Termes IGN] végétationRésumé : (Auteur) Morphological attribute profiles are among the most prominent spectral-spatial pixel description methods. They are efficient, effective, and highly customizable multiscale tools based on hierarchical representations of a scalar input image. Their application to multivariate images in general and hyperspectral images in particular has been so far conducted using the marginal strategy, i.e., by processing each image band (eventually obtained through a dimension reduction technique) independently. In this paper, we investigate the alternative vector strategy, which consists in processing the available image bands simultaneously. The vector strategy is based on a vector-ordering relation that leads to the computation of a single max and min tree per hyperspectral data set, from which attribute profiles can then be computed as usual. We explore known vector-ordering relations for constructing such max trees and, subsequently, vector attribute profiles and introduce a combination of marginal and vector strategies. We provide an experimental comparison of these approaches in the context of hyperspectral classification with common data sets, where the proposed approach outperforms the widely used marginal strategy. Numéro de notice : A2016-850 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2513424 En ligne : https://doi.org/10.1109/TGRS.2015.2513424 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82932
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3208 - 3220[article]