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Semi-supervised hyperspectral image segmentation using regionalized stochastic watershed
 

Summary: Semi-supervised hyperspectral image segmentation using
regionalized stochastic watershed
Jes´us Anguloa and Santiago Velasco-Foreroa
aCMM-Centre de Morphologie Math´ematique, Math´ematiques et Syst`emes, MINES ParisTech;
35, rue Saint-Honor´e, 77305 Fontainebleau cedex - France
ABSTRACT
Stochastic watershed is a robust method to estimate the probability density function (pdf) of contours of a
multi-variate image using MonteCarlo simulations of watersheds from random markers. The aim of this paper is
to propose a stochastic watershed-based algorithm for segmenting hyperspectral images using a semi-supervised
approach. Starting from a training dataset consisting in a selection of representative pixel vectors of each spectral
class of the image, the algorithm calculate for each class a membership probability map (MPM). Then, the MPM
of class k is considered as a regionalized density function which is used to simulate the random markers for the
MonteCarlo estimation of the pdf of contours of the corresponding class k. This pdf favours the spatial regions
of the image spectrally close to the class k. After applying the same technique to each class, a series of pdf are
obtained for a single image. Finally, the pdf's can be segmented hierarchically either separately for each class or
after combination, as a single pdf function. In the results, besides the generic spatial-spectral segmentation of
hyperspectral images, the interest of the approach is also illustrated for target segmentation.
Keywords: hyperspectral images, semi-supervised segmentation, stochastic watershed, regionalized random
germs, probabilistic segmentation
1. INTRODUCTION

  

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

 

Collections: Computer Technologies and Information Sciences