Random germs and stochastic watershed for unsupervised multispectral image segmentation

Abstract. This paper extends the use of stochastic watershed, recently introduced by Angulo and Jeulin [1], to unsupervised segmentation of multispectral images. Several probability density functions (pdf), derived from Monte Carlo simulations (M realizations of N random markers), are used as a gradient for segmentation: a weighted marginal pdf a vec-torial pdf and a probabilistic gradient. These gradient-like functions are then segmented by a volume-based watershed algorithm to define the R largest regions. The various gradients are computed in multispectral im-age space and in factor image space, which gives the best segmentation. Results are presented on PLEIADES satellite simulated images