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Universitat de Girona. Departament d鈥橧nform脿tica i Matem脿tica Aplicada  
Cuadras, C.M.
Valero, S. 

It is wellknown in image processing that, by varying the wavelength, any material reflects and absorbsin a different way the solar radiation. This is registered by hyperspectral sensors, which collectmultivariate 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 tocompare these regions which are previously modelled by using compositional data matrices, where theentries in each row is a statistical discrete distribution of the radiance values (columns). These rowscorrespond to distinct but contiguos wavelengths. Thus the distribution in a row is very similar to thedistribution in close rows. To measure this proximity, we use Hellinger distance between rows, whichprovides a distance matrix.Given two hyperspectral regions of an image providing two compositional data matrices, we obtainthe corresponding distance matrices and, by using metric multidimensional scaling, we computetwo sets of principal coordinates, which are related by a multivariate association measure based oncanonical correlations.We ilustrate this approach comparing some multivariate regions of images captured by hyperspectralremote sensors  
http://hdl.handle.net/2072/273626  
eng  
Universitat de Girona. Departament d鈥橧nform脿tica i Matem脿tica Aplicada  
Tots els drets reservats  
Estad铆stica matem脿tica  Congressos
Mathematical statistics  Congresses An脿lisi multivariable  Congressos Multivariate analysis  Congresses 

Multivariate Association of Compositional Data Matrices with Applications in Comparing Hyperspectral Images  
info:eurepo/semantics/conferenceObject  
Recercat 