Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation

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 vectorial 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 image space and in factor image space, which gives the best segmentation. Results are presented on PLEIADES satellite simulated images.

• Type: Conference
• Year: 2007
• Where: KES
• Authors: Guillaume Noyel, Jesús Angulo, Dominique Jeulin
• Keywords: KES 2007, Multispectral Image, Unsupervised Segmentation, Weighted Marginal Pdf