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MORPHOLOGICAL IMAGE DISTANCES FOR HYPERSPECTRAL DIMENSIONALITY EXPLORATION USING KERNEL-PCA AND ISOMAP
 

Summary: MORPHOLOGICAL IMAGE DISTANCES FOR HYPERSPECTRAL DIMENSIONALITY
EXPLORATION USING KERNEL-PCA AND ISOMAP
S. Velasco-Forero, J. Angulo
CMM-Centre de Morphologie Math´ematique
MINES Paristech, FRANCE
{santiago.velasco;jesus.angulo}@ensmp.fr
J. Chanussot
GIPSA-Lab
Grenoble Institute of Technology, FRANCE
jocelyn.chanussot@gipsa-lab.inpg.fr
1. INTRODUCTION: MOTIVATION AND AIM
The application of nonlinear manifold learning for hyperspec-
tral image analysis has been widely studied in last years [1,
4]. One of the main ingredients of these data reduction tech-
niques is the distance used to compare the spectral band im-
ages. By means of this distance the pairwise similarity matrix
is built and then, the matrix is used to explore the intrinsic
dimensionality of the hyperspectral image.
There are two main families of image distances which
have been considered in previous works: i) the distance be-

  

Source: Angulo,Jesús - Centre de Morphologie Mathématique, Ecole des Mines de Paris

 

Collections: Computer Technologies and Information Sciences