Exploring synergetic effects of dimensionality reduction and resampling tools on hyperspectral imagery data classification
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Otros documentos de la autoría: Sánchez Garreta, Josep Salvador; García, Vicente; Mollineda, Ramón A.
Metadatos
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
Exploring synergetic effects of dimensionality reduction and resampling tools on hyperspectral imagery data classificationFecha de publicación
2011Editor
SpringerISSN
0302-9743; 1611-3349Cita bibliográfica
Lecture notes in computer science (2011), vol. 6871, 511-523Tipo 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 and a supervised filter are applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of combining several techniques to tackle the imbalance and the high dimensionality problems, and also to evaluate the order of application that leads to the best classification performance. Experimental results demonstrate the significance of using together these two preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order corresponds to first a resampling strategy and then a feature (or extraction) selection algorithm, this is a question that still needs a much more thorough investigation in the future [-]
Derechos de acceso
© 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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