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MULTISCALE STOCHASTIC WATERSHED FOR UNSUPERVISED HYPERSPECTRAL IMAGE SEGMENTATION
 

Summary: MULTISCALE STOCHASTIC WATERSHED FOR UNSUPERVISED HYPERSPECTRAL
IMAGE SEGMENTATION
J. Angulo, S. Velasco-Forero
CMM-Centre de Morphologie Math´ematique
MINES Paristech, FRANCE
{jesus.angulo;santiago.velasco}@ensmp.fr
J. Chanussot
GIPSA-Lab
Grenoble Institute of Technology, FRANCE
jocelyn.chanussot@gipsa-lab.inpg.fr
ABSTRACT
This paper deals with unsupervised segmentation of hyper-
spectral images. It is based on the stochastic watershed, an
approach to estimate a probability density function (pdf) of
contours of an image using MonteCarlo simulations of water-
shed segmentations. In particular, it is introduced for the first
time a multiscale framework for the computation of the pdf of
contours using the stochastic watershed. Two multiscale ap-
proaches are considered: i) a linear scale-space using Gaus-
sian filters, ii) a nonlinear morphological scale-space pyramid

  

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

 

Collections: Computer Technologies and Information Sciences