Multivariate association of compositional data matrices with applications in comparing hyperspectral images
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/366623
Tipus de documentText en actes de congrés
Data publicació2011
EditorCIMNE
Condicions d'accésAccés obert
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Abstract
It is well-known in image processing that, by varying the wavelength, any material reflects and absorbs
in a different way the solar radiation. This is registered by hyperspectral sensors, which collect
multivariate discrete images in a series of contiguous wavelength bands, providing the spectral curves,
which can distinguish between materials.
In order to partition a multivariate image in regions belonging to different materials, we need to
compare these regions which are previously modelled by using compositional data matrices, where the
entries in each row is a statistical discrete distribution of the radiance values (columns). These rows
correspond to distinct but contiguos wavelengths. Thus the distribution in a row is very similar to the
distribution in close rows. To measure this proximity, we use Hellinger distance between rows, which
provides a distance matrix.
Given two hyperspectral regions of an image providing two compositional data matrices, we ob-
tain the corresponding distance matrices and, by using metric multidimensional scaling, we compute
two sets of principal coordinates, which are related by a multivariate association measure based on
canonical correlations.
We ilustrate this approach comparing some multivariate regions of images captured by hyperspec-
tral remote sensors.
CitacióCuadras, C.M.; Valero, S. Multivariate association of compositional data matrices with applications in comparing hyperspectral images. A: CODAWORK 2011. "Proceedings of CoDaWork'11: 4th international workshop on Compositional Data Analysis, Egozcue, J.J., Tolosana-Delgado, R. and Ortego, M.I. (eds.) 2011". Barcelona: CIMNE, 2011, ISBN 978-84-87867-76-7.
ISBN978-84-87867-76-7
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p10-CoDaWork2011.pdf | 149,2Kb | Visualitza/Obre |