Classification of high dimensional and imbalanced hyperspectral imagery data
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Otros documentos de la autoría: García, Vicente; Sánchez Garreta, Josep Salvador; Mollineda, Ramón A.
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Título
Classification of high dimensional and imbalanced hyperspectral imagery dataFecha de publicación
2011Editor
SpringerISSN
0302-9743; 1611-3349Cita bibliográfica
Lecture notes in computer science (2011), vol. 669, 644-651Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA ... [+]
The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA is applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of using together these two techniques, and also to evaluate the application order that leads to the best classification performance. Experimental results demonstrate the significance of combining these preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order of application corresponds to first a resampling algorithm and then PCA, this is a question that still needs a much more thorough investigation [-]
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© Springer-Verlag Berlin Heidelberg 2011
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info:eu-repo/semantics/openAccess
http://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
info:eu-repo/semantics/openAccess
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