Artículo
Robust principal components for hyperspectral data analysis
Fecha de publicación:
07/2009
Editorial:
Springer
Revista:
Lecture Notes in Computer Science
ISSN:
0302-9743
ISBN:
978-3-642-02610-2
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Remote sensing data present peculiar features and characteristics that may make their statistical processing and analysis a difficult task. Among them, it can be mentioned the volume of data involved, the redundancy, the presence of unexpected values that arise mainly due to noisy pixels and background objects whose responses to the sensor are very different from those of their neighbours. Sometimes, the volume of data and number of variables involved is so large that any statistical analysis becomes unmanageable if data are not condensed in some way. A commonly used method to deal with this situation is Principal Component Analysis (PCA) based on classical statistics: sample mean and covariance matrices. The drawback in using sample covariance or correlation matrices as measures of variability is their high sensitivity to spurious values. In this work we analyse and evaluate the use of some Robust Principal Component techniques and make a comparison of Robust and Classical PCs performances when applied to satellite data provided by the hyperspectral sensor AVIRIS (Airborne Visible/Infrared Imaging Spectrometer). We conclude that some robust approaches are the most reliable and precise when applied as a data reduction technique before performing supervised image classification. © 2009 Springer Berlin Heidelberg.
Palabras clave:
IMAGE CLASSIFICATION
,
PRINCIPAL COMPONENT ANALYSIS
,
ROBUST INFERENCE
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Identificadores
Colecciones
Articulos(CCT - NORDESTE)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - NORDESTE
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - NORDESTE
Citación
Lucini, María Magdalena; Frery, Alejandro César; Robust principal components for hyperspectral data analysis; Springer; Lecture Notes in Computer Science; 5627 LNCS; 7-2009; 126-135
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