Multiscale stochastic watershed for unsupervised hyperspectral image segmentation

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 Monte Carlo simulations of watershed 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 approaches are considered: i) a linear scale-space using Gaussian filters, ii) a nonlinear morphological scale-space pyramid using levelings. In addition, a multiscale pyramid obtained by modifying the size of the random markers is also studied. Then, it is shown how the pdf of contours can finally be segmented using the non-parametric waterfalls algorithm. The performances of the proposed methods are compared using two examples of standard remote sensing hyperspectral images.